on-line predictive condition monitoring system for coal pulverizers

On-Line Predictive Condition MonitoringSystem for Coal Pulverizers

Application of Wireless Technology

Technical Report

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WARNING:Please read the License Agreementon the back cover before removingthe Wrapping Material.

EPRI Project Manager R. Shankar

EPRI • 3412 Hillview Avenue, Palo Alto, California 94304 • PO Box 10412, Palo Alto, California 94303 • USA 800.313.3774 • 650.855.2121 • [email protected] • www.epri.com

On-Line Predictive Condition Monitoring System for Coal Pulverizers Application of Wireless Technology

1004902

Final Report, October 2003

Cosponsors SmartSignal Corp. 901 Warrenville Road Suite 300 Lisle, IL 60532 Principal Investigator A. Wolosewicz

Dynegy Midwest Generation 2828 N. Monroe St. Decatur, IL 62526 Principal Investigator T. Wheeler

DISCLAIMER OF WARRANTIES AND LIMITATION OF LIABILITIES

THIS DOCUMENT WAS PREPARED BY THE ORGANIZATION(S) NAMED BELOW AS AN ACCOUNT OF WORK SPONSORED OR COSPONSORED BY THE ELECTRIC POWER RESEARCH INSTITUTE, INC. (EPRI). NEITHER EPRI, ANY MEMBER OF EPRI, ANY COSPONSOR, THE ORGANIZATION(S) BELOW, NOR ANY PERSON ACTING ON BEHALF OF ANY OF THEM:

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ORGANIZATION(S) THAT PREPARED THIS DOCUMENT

SmartSignal Corp. Dynegy Midwest Generation

NEITHER EPRI, ANY MEMBER OF EPRI, NOR ANY PERSON OR ORGANIZATION ACTING ON BEHALF OF THEM:

1. MAKES ANY WARRANTY OR REPRESENTATION WHATSOEVER, EXPRESS OR IMPLIED, INCLUDING ANY WARRANTY OF MERCHANTABILITY OR FITNESS OF ANY PURPOSE WITH RESPECT TO THE VENDORS, TECHNOLOGIES OR PRODUCTS DISCLOSED IN THIS REPORT; OR

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THE PURPOSE OF THIS REPORT IS TO PROVIDE AN OVERVIEW OF RELEVANT TECHNOLOGIES THAT MAY SUPPORT PLANT OPERATIONS AND MAINTENANCE. THE USE OF VENDOR NAMES AND/OR PRODUCT NAMES OR ILLUSTRATIONS ARE FOR EXAMPLE ONLY AND ARE NOT RECOMMENDATIONS FOR, NOR ENDORSEMENTS OF, A PARTICULAR VENDOR, TECHNOLOGY OR PRODUCT.

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Copyright © 2003 Electric Power Research Institute, Inc. All rights reserved.

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CITATIONS

This report was prepared by

SmartSignal Corporation 901 Warrenville Road Lisle, IL 60532

Principal Investigator A. Wolosewicz

Dynegy Midwest Generation 2828 N. Monroe St. Decatur, IL 62526

Principal Investigator in charge of eCMTM installation T. Wheeler

Principal Investigator in charge of wireless sensor installation B. Baldwin

This report describes research sponsored by EPRI.

The report is a corporate document that should be cited in the literature in the following manner:

On-Line Predictive Condition Monitoring System for Coal Pulverizers: Application of Wireless Technology. EPRI, Palo Alto, CA; SmartSignal Corp., Lisle, IL; and Dynegy Midwest Generation, Decatur, IL: 2003. 1004902.

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PRODUCT DESCRIPTION

This report discusses how the combination of two new technologies (equipment predictive monitoring software and wireless technology vibration sensors) provides more information for early detection of coal pulverizer failures than conventional vibration analysis alone. Early warning before failure, from several hours to as much as several days, allows maintenance staff to plan proactive maintenance under the best possible scenario versus continually reacting to upset conditions and performing unscheduled maintenance under emergency conditions.

Results and Findings This is the first EPRI-sponsored (partial) demonstration of on-line monitoring (OLM) to assess equipment condition from signals obtained from plant components during operation. This requires the processing of several signals, some from the component under examination but most from surrounding equipment/systems that have a “high degree of correlation.” The project results have shown that vibration magnitude data can be modeled with correlated process variables to detect failures with early warning. Initially in this study, currently available process variable signals were used to model and monitor a coal pulverizer. The results identified a need to consider vibration signals in order to capture more failure modes. Vibration signals provided by a wireless network, in combination with the existing process signals, significantly increase the ability to provide valuable early warning. Results also indicate the ability to differentiate between sensor anomalies (that is, drift, sensor failure, and the like) and mechanical faults, such as those from the bearings on the pulverizer and motor.

The combination of vibration signals and process variables, when monitored by the predictive monitoring system, increases the number of detectable failure modes from 11% to 46%. The value proposition is that the technology was capable of achieving up to 12 hours early warning of impending failure.

Challenges and Objectives The ability to accurately predict equipment failure from on-line signals to plan maintenance and/or repair remains a challenging problem facing the power industry. Successful prediction can lead to increased efficiency, lower operation and maintenance (O&M) costs, and increased reliability. The objectives of this effort were to:

• Demonstrate the on-line ability to combine wireless vibration signals with other process and equipment signals to warn of anomalous equipment conditions

• Demonstrate the ability to operate under plant operating conditions

Applications, Values, and Use The system used in the demonstration is a commercially available, enterprise software solution that provides real-time asset health monitoring for large industrial assets such as combustion

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turbines, jet engines, diesel engines, pumps, electric motors, and pipeline flow meters. The product has been specifically designed to allow operators to monitor fleets of equipment in real time and easily scales to plant-wide or fleet-wide applications. This technology has been successfully demonstrated in the United States in commercial airline operations and maintenance by providing the earliest possible warning of deteriorating conditions in jet engines across various manufacturers.

EPRI Perspective A class of highly advanced, model-based equipment condition monitoring software systems have been developed and deployed in several industries for continuous health monitoring. These provide early detection of signal anomalies and enable timely disposition of asset condition without incurring costly corrective maintenance or failure. Coupled with advances in wireless technology that are enabling measurement of heretofore difficult-to-access signals, OLM capabilities offer the potential for more reliable detection.

EPRI has initiated projects in 2003 to leverage previous work done in implementation of these technologies for instrument calibration reduction. These advanced technologies utilize powerful pattern recognition methods that use either causal or model-based mathematical models to detect out-of-range conditions. Typically, they consider multiple process signals obtained from the plant historian. However, some consider other non-quantitative inputs, such as operator rounds information and nondestructive evaluation (NDE) data as well as other non-periodically obtained signal information (for example, vibration data). While not demonstrated yet, these technologies have the potential to significantly improve condition-based assessment and are likely to have a large impact on plant reliability and availability. The vision is to see equipment condition monitoring software applied on entire fleets so that there are measurable reductions in maintenance, improved reliability, and increased customer and investor confidence.

Approach The system determines if equipment operation is significantly different from normal operation and, if so, then guides the analyst to finding the problems. First, an empirical model is created from normal operational data on some equipment or system. During real-time operation, samples of data for the system are compared to the model estimates (of expected normal behavior) to determine whether the current state is normal. Finally, a suite of incident rules built to capture expected fault mode signatures is applied to guide the analyst to the likely fault modes and the amount of degradation. The underlying technology involves proprietary algorithms in a step-by-step mathematical approach. The methodology is an engineering-based method used to identify critical pieces of equipment where early warning of failure could prove financially beneficial by allowing operators to shift from unplanned maintenance to planned maintenance, by reducing maintenance duration, or by increasing the interval between maintenance activities.

Keywords On-line monitoring (OLM) Equipment condition monitoring Wireless network Early warning fault detection Vibration Failure analysis

EPRI Licensed Material

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ABSTRACT

This report discusses how the health monitoring of a coal pulverizer is significantly enhanced when vibration signals are added to a model of existing process variables. Two new technologies, SmartSignal eCMTM predictive monitoring software and wireless vibration sensors, are utilized to achieve the goal. Systems analysis indicates that the combination of process variables and vibration signals increases the number of coal pulverizer failure modes, detectable through early warning, by a factor of four to six (46%) versus modeling on process variables alone (11%) or vibration signals alone (7%). Providing early warning of failures can allow time for proactive or scheduled repairs, resulting in substantial financial benefit to maintenance and operations.


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CONTENTS

1 INTRODUCTION ................................................................................................................. 1-1

2 SMARTSIGNAL ECM SOFTWARE..................................................................................... 2-1

2.1 Overview................................................................................................................... 2-1

2.2 SmartSignal eCM Software Architecture ................................................................... 2-3

2.3 Underlying SmartSignal Technology ......................................................................... 2-5

2.4 SmartStart Methodology............................................................................................ 2-9

2.3.1 Value Analysis .................................................................................................2-11

2.3.2 System Analysis ..............................................................................................2-14

2.3.2.1 Process Decomposition.............................................................................2-15

2.3.2.2 Process Behavior Analysis ........................................................................2-16

2.3.2.3 Fingerprint Charts......................................................................................2-17

2.3.3 Data Analysis ..................................................................................................2-19

2.3.4 eCM WorkBench Application Development .....................................................2-19

2.3.4.1 eCM Model Testing ...................................................................................2-20

2.3.4.2 Rule Testing ..............................................................................................2-20

3 MERGING ECM WITH WIRELESS VIBRATION TECHNOLOGY ....................................... 3-1

3.1 Pulverizer Value Analysis.......................................................................................... 3-1

3.1.1 Description of Pulverizer Function .................................................................... 3-1

3.1.2 Benefits of Early Warning of Pulverizer Failure................................................. 3-3

3.2 Pulverizer System Analysis ....................................................................................... 3-4

3.2.1 Coal Pulverizer eCM Models with Original Process Variable Sensors .............. 3-5

3.2.2 Coal Pulverizer eCM Models with Added Vibration Sensors ............................. 3-9

3.3 The Wireless Vibration Network ...............................................................................3-11


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4 RESULTS ............................................................................................................................ 4-1

4.1 Case A Results: Models on Initial Process Variables ................................................ 4-1

4.2 Case B Results: Models with Vibration and Process Variables.................................. 4-5

5 CONCLUSIONS .................................................................................................................. 5-1

5.1 Technology Shows Promise for Coal Pulverizers ...................................................... 5-1

5.2 Wireless System Obstacles and Lessons Learned.................................................... 5-2

5.3 Technology Works - Reliability of Signal Is an Issue ................................................. 5-3


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LIST OF FIGURES

Figure 2-1 Overview of the SmartSignal eCM Process............................................................ 2-1 Figure 2-2 Architecture of the SmartSignal eCM Software ...................................................... 2-3

Figure 2-3 Estimate of a Dynamic Response Surface Based on Exemplars............................ 2-8 Figure 2-4 Auto-Associative Modeling of Stator-Winding Temperatures.................................. 2-9 Figure 2-5 The SmartStart Methodology Process...................................................................2-11

Figure 2-6 Early Warning Value Hierarchy .............................................................................2-12 Figure 2-7 Frequency Pareto Chart of Pulverizer Failure Modes............................................2-14 Figure 2-8 Process Decomposition ........................................................................................2-15 Figure 3-1 Schematic of Coal Pulverizer ................................................................................. 3-6

Figure 3-2 Frequency of Pulverizer Failure Modes: Breakdown by Available Signals.............3-10 Figure 3-3 Data Flow from Wireless Vibration Network ..........................................................3-11 Figure 3-4 Roller Bearing Vibration Sensor Installation ..........................................................3-12

Figure 3-5 NCAP Transmitter Installation ...............................................................................3-13 Figure 3-6 Antenna and Access Point Installation ................................................................3-133 Figure 4-1 Bowl Differential Pressure: 11/22/02 to 11/25/02.................................................... 4-2

Figure 4-2 Current: 11/22/02 to 11/25/02 ................................................................................ 4-3 Figure 4-3 Bowl dP (L) and Current (R): 12/6/02 to 12/10/02 .................................................. 4-4 Figure 4-4 Base Pressure (L) and Bowl Differential Pressure (R): 1/16/03 to 1/25/03 ............. 4-5 Figure 4-5 Base Pressure (L) and Bowl Differential Pressure (R): 1/16/03 to 1/25/03 ............. 4-7

Figure 4-6 WatchList Results: VIBPOINT 5-8, 05/01–7/1/03 ................................................... 4-8 Figure 4-7 WatchList Results: VIBPOINT 1-4, 6/12–8/27/03 ..................................................4-10 Figure 4-8 WatchList Results: VIBPOINT 5-8, 06/12–8/27/03 ................................................4-11

Figure 5-1 Cumulative Failure Mode Pareto Chart of Catchable Failures................................ 5-2


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LIST OF TABLES

Table 2-1 Fingerprint Chart for Baldwin Unit 3 Pulverizer 3A..................................................2-18 Table 3-1 Effect of 12 Hours of Early Warning of Pulverizer Failure ........................................ 3-4

Table 3-2 Signal List for Enhanced Model............................................................................... 3-8


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1 INTRODUCTION

This report discusses how the health monitoring of a coal pulverizer is significantly enhanced when vibration signals are added to a model of existing process variables. Two new technologies, SmartSignal’s eCMTM predictive maintenance software and wireless network with vibration sensors, are utilized to achieve the goal. Systems analysis indicates that the combination of process variables and vibrations signals increases the number of detectable coal pulverizer failure modes by a factor of four over modeling on process variables alone or vibration signals alone. Providing early warning on failures can allow time for proactive or scheduled repairs, resulting in substantial financial benefit to maintenance and operations.

The purpose of this report is to:

• Provide detailed technical information on how SmartSignal eCM predictive monitoring software detects the earliest sign of physical deterioration in a pulverizer

• Explain how continuous wireless vibration monitoring can improve pulverizer performance

The combination of wireless vibration technology and predictive monitoring software will substantially improve power plant performance. By early detection of pulverizer problems, plant operations, engineering, and maintenance staff can plan proactive maintenance under the best possible scenario versus continually reacting to upset conditions and performing unscheduled maintenance under emergency conditions.

The SmartStart methodology is used as a framework to explain how the combination of wireless vibration technology and SmartSignal eCM predictive monitoring software work together. SmartSignal eCM software has demonstrated to be a platform for continuous asset management and has proven to be successful monitoring coal-fired plants.

In any process or equipment, fundamental physical laws such as conservation of mass, energy, and momentum govern the system’s operation and tie together the complex set of relationships between sensor readings. As equipment degrades or failure begins, the system physically changes. For example, as bearings wear, excess friction increases bearing temperature and vibration and ultimately reduces overall system efficiency. When bearing temperature or vibration exceeds traditional monitoring thresholds, permanent damage could have already occurred. Early warning of failure provides maximum value when it detects the earliest signs of deterioration before permanent damage has occurred.

SmartSignal’s eCM technology analyzes historical data and constructs high fidelity, empirical models of normal, expected equipment and process operation. During real-time operation, eCM compares estimated sensor values to actual real-time data collected from the equipment. This comparison identifies developing failures much earlier then conventional technologies.

EPRI Licensed Material Introduction

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Many factors prevent conventional mathematical and statistical models from accurately revealing the complex relationships between sensors. First, equation-based mathematical modeling fails for all but the simplest system even under normal, steady-state operating conditions because the equations that describe the physical process quickly become too complex. Second, as the process moves from one state to another, transient behavior further complicates analytical techniques. Finally, random process variance and sensor measurement error obscure deterministic physical relationships. Computationally, first principle models simply become impractical for most real-world applications

While some catastrophic failures occur too quickly to avoid, most failures develop over time and disrupt the equipment’s normal sensor relationships. This impending failure changes how process variables move with relation to each other in very subtle ways. Frequently, the change progressively grows until either the equipment fails or a safety system detects a threshold violation and shuts down the unit.

SmartSignal eCM detects abnormal equipment behavior within the variability of normal operation and well within existing threshold alarm limits. The key is calculating residual values by subtracting the estimates generated by an eCM model from the actual real-time sensor values. Analysis of the residuals identifies very subtle process deviations well within the normal threshold alarm limits and detects the earliest possible signs of equipment malfunction. The alert feature of the system detects deviations from normal operation without reacting to the normal process or system variation. SmartSignal eCM diagnoses failure modes, based on a pattern of high or low residuals, which is unique to each failure mode. Therefore, we refer to this as a fingerprint or signature unique to the pattern of residuals.

This report explains the value proposition of these two technologies in the coal pulverizer application, discusses the workings of the system, and shows examples of the two technologies working together to provide actionable and valuable information.


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2 SMARTSIGNAL ECM SOFTWARE

2.1 Overview

SmartSignal’s eCM V. 2.5 is an enterprise software solution with a browser-based, graphical user interface (GUI) that provides real-time asset health monitoring for large industrial assets such as combustion turbines, jet engines, diesel engines, pumps, electric motors, meters, and, in fact, almost any instrumented mission-critical asset with installed sensors. Moreover, the product easily scales to plant-wide or fleet-wide applications.

As eCM receives raw data (known as actual signals) in real time from the data source, it generates expected values (known as estimate signals) using eCM models built from historical data of the equipment. eCM determines for each sensor whether the actual values deviate significantly from the estimates and, if so, produces an alert. Alerts are passed through user-configurable rules that determine whether to create an incident and automatically notify users that this asset must be watched. Rules can also be used to diagnosis the cause of the incident and classify it according to severity and confidence of diagnosis. This focuses the attention of engineers and analysts on only those components and systems with developing problems. A graphical representation of this process is shown in Figure 2-1.

Figure 2-1 Overview of the SmartSignal eCM Process

EPRI Licensed Material SmartSignal eCM Software

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To start, eCM collects a history of data samples for all of the sensors in an eCM model. Next, eCM automatically creates an empirical model of normal performance of the asset based on the normal historical data. Unlike conventional threshold monitoring techniques, the eCM empirical model generates an estimated value for each sensor that would be characteristic of normal operation. Each sensor estimate is based not only on that sensor’s history but also on how that sensor interacted with every other sensor value. The result is a data-driven “personality” profile of each asset. Then, in real time, the software effectively removes the effect of normal operation by subtracting the estimated values from the actual values to generate residual values. If the equipment is running normally, the resulting residuals should be small and evenly distributed around zero. Equipment faults show up as spikes or trends in the residuals.

eCM compares the residuals using a patented statistical technique. If significant deviations are found, the equipment is running abnormally, and the eCM issues an alert. These alerts are fed into the diagnostic rules engine that analyzes the pattern of alerts to see if this is a known pattern or if it meets the preestablished criteria for being promoted into an item on the “WatchList” and/or notifying an analyst. (These criteria are set up during the SmartStart methodology phase.) Information about the incident is fed back into the eCM database, and all the critical information can be sent back to the control system.


SmartSignal eCM Software

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2.2 SmartSignal eCM Software Architecture

Figure 2-2 Architecture of the SmartSignal eCM Software

The server computer receives data and performs state estimation and alerting. The results are viewable through web browsers connected via internet/intranet to the WatchList ManagerTM. The eCM WorkBenchTM tool is used for initial offline system setup.


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1. WatchList Manager

• eCM end user interface:

– Focuses attention on failing assets

– Provides quick access to critical diagnostic and sensor data

– Provides exception driven data

– Provides asset event reports

– Provides fleet summary reports

– Provides historical views

– Provides asset event logs

• Web based

• IBM MQ middleware

• Secure access

• User configuration and management

2. eCM Database

• Repository for real-time data and configuration information:

– Asset hierarchy

– Models

– Diagnostic rules

– Signal data

– Alerts and incidents

– User permissions and roles

3. eCM Run-Time Engine

• Key analytical component:

– Generates signal estimates and residuals

– Applies rules

– Triggers incidents

– Provides real-time operation, 24 hours a day, 7 days a week



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4. eCM Data Feeds

• Real-time data connectivity

• Bi-directional data transfer

• Easy integration of data and incidents to:

– Control systems

– Historians

– Enterprise-level business systems

5. eCM WorkBench

• Provides eCM engineer setup environment

• Allows the user to develop models and rules

• Provides data visualization and analysis

• Provides offline model testing

• Provides runtime configuration

This platform for asset management has led to solutions for detecting, alerting, and classifying failures for actionable early warning of failure in components such as boiler feed pumps and recirculation valves, steam turbines, condensers, boilers, and electrostatic precipitators.

The SmartStart methodology first uses a value analysis to identify critical pieces of equipment where early warning of failure could prove financially beneficial by shifting unplanned maintenance to planned maintenance, by reducing maintenance duration, or by increasing the interval between maintenance activities. Next, during a systems analysis, key failure modes are linked to available sensors to help the engineers design the SmartSignal eCM models. Finally, during a data analysis, SmartSignal models based on historical sensor readings are built and tested to determine their efficacy as a fault detection tool.

2.3 Underlying SmartSignal Technology

The core of SmartSignal’s commercial eCM software comprises a unique modeling technology called a similarity-based model (SBM). In contrast to first principle modeling techniques, it is entirely empirical, that is, data driven. The model is built from sets of exemplars or historic data observations that sufficiently characterize normal or desirable operation of the monitored equipment. In contrast to parametric modeling techniques (like polynomial regression, Kalman filters, or neural networks), no assumption about the analytical form of the solution is imposed. The model is capable of making reasonable, if not exacting, estimates of proper equipment behavior globally by means of a proprietary form of similarity-based regression.


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The two primary requirements of the technology are:

1. Good historic data truly representing the equipment free from fault

2. Sufficient historic data

Both of these requirements are hallmarks of all empirical techniques. However, SmartSignal has developed tools for data cleansing to address requirement 1 and adaptive bootstrapping techniques for implementations where sufficient data are not available at first to address requirement 2.

The SmartSignal modeling technology is based on using a similarity operation on two observation vectors. The result of the similarity operation is a similarity score (a scalar) for the comparison of the two observation vectors. The similarity operation is akin to the vector dot or inner product and can be extended to matrix operations where a scalar similarity score is rendered for each combination of a row from one matrix and column from a second matrix. Accordingly, an estimate vector of estimated sensor values yest is determined from:

wDyest ⋅= Eq. 2-1

where:

D is a state matrix of the aforementioned exemplars characterizing normal operational states

w is a weighting vector derived from:

=

∑=

N

j

jw

ww

1

)(ˆ

ˆ Eq. 2-2

( ) ( )inTT yDDDw ⊗⋅⊗= −1

ˆ Eq. 2-3

Here, yin is the current actual observation vector, comprising sensor data. The similarity operation is indicated by the symbol ⊗ (which is not meant here to designate the Kronecker Product as it is sometimes used in literature).

The similarity operator for use in the above equations meets the following criteria:

• Similarity is a scalar range, bounded at each end.

• The similarity of two identical inputs is the value of one of the bounded ends.

• The absolute value of the similarity increases as the two inputs approach being identical.

The similarity operation can be a vector operation or can be an element-to-element operation that is averaged across the vectors being compared. For example, one vector-to-vector similarity



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operation that can be used is based on the Euclidean distance in n-space (where n is the number of sensors in the observation):

1

1

2)(1),(

−

=

−+= ∑N

nnn yxyxS Eq. 2-4

An example of an element-to-element similarity operation that can be used is:

( )∑=

−

−

+=N

n n

nn

R

yx

NyxS

1

12

11

),( Eq. 2-5

The D matrix of exemplars is created from a large set of historic reference data covering the full dynamic range of the equipment. The selection of exemplars is extremely efficient, and SmartSignal has developed a number of proprietary techniques for this. All such selection steps are one-pass processes, in contrast to iterative training algorithms that characterize parametric approaches like neural networks. Consequently, they are both extremely fast as well as deterministic. The number of exemplars required is dependent on both the number of sensors in the model, as well as the dynamic variability of the equipment in normal operation. However, a general rule of thumb provides for the selection of a number of exemplars no less than two times the number of sensors. It is not uncommon to model a 50–60 sensor system with 200–300 exemplar observations.


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Figure 2-3 Estimate of a Dynamic Response Surface Based on Exemplars

As can be seen in Figure 2-3, an unknown dynamics surface is empirically mapped by exemplars (shown in blue), using the SmartSignal modeling technology. An estimate (shown in red) is generated as a weighted combination of the exemplars. The contributions (shown in green along each contributing exemplar) can be both positive and negative.

In the classic inferential mode common to most modeling approaches, where estimates are made for variables that are not in the input vector, the D matrix comprises two subsets: one of input exemplars and one of corresponding output exemplars. Equations 2-1 and 2-3 above become:

wDy outest ⋅= Eq. 2-6

( ) ( )inTinin

Tin yDDDw ⊗⋅⊗= −1

ˆ Eq. 2-7

Advantageously, the modeling technique is also capable of generating estimates in what we call the auto-associative mode, where all outputs are also inputs (Equations 2-1 and 2-3). This is a critical advantage (and somewhat unorthodox in the modeling world) for the common situation where the instrumented variables of the equipment represent a mix of the causes and effects of the physics that drive the equipment. It ameliorates the need to determine causes, effects, and feedback, and reduces the degree of ill-posed problem caused by many-to-one state issues. In addition, the auto-associative mode enables monitoring of variables that are comprised solely of responses (for example, stator winding temperatures or vibration signal spectral features).



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Figure 2-4 shows an example of an auto-associative model used to monitor 81 stator-winding slot temperatures of a generator. The first eight temperatures (in blue) are plotted along with the corresponding auto-associative estimates (in red). The temperature signal in the upper left plot is drifting by 5°F (2.8°C) over the length of the signal due to a simulated fault in the winding. The model estimate indicates what the temperature readings should be based on the auto-associative SBM. A clear deviation from the actual signal and the estimate is first evident when the drift has only progressed to about 1 to 1.5°F (0.6 to 0.8°C). The deviation becomes greater over time, indicating the presence of drifting temperature signal.

°F = (°C × 9/5) + 32

Figure 2-4 Auto-Associative Modeling of Stator-Winding Temperatures

Four parallel sensors. The X-axis represents samples (measured once per minute). The Y-axis represents temperature (in °F). The top left figure shows the signal deviating from the estimate. The three other signals have overlapping signal and estimate.

2.4 SmartStart Methodology

The SmartStart methodology assists the user to develop valuable, actionable, and accurate equipment condition monitoring. SmartSignal delivers focused, robust applications that span wide operating ranges and require minimal ongoing maintenance.


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The SmartStart methodology emphasizes repeatability and completeness. Repeatability minimizes rework as eCM spans different applications across a fleet of assets. The SmartStart methodology consists of four phases:

1. Value analysis - Asset owners need to mitigate their most costly failures and problems. Value analysis develops a list of valuable, actionable failures that, if detected early, will substantially reduce an owner’s expenses and increase revenue. This is done by creating a value pareto chart with failure modes prioritized by value, which takes into account severity and frequency and ensures that the project focus is on high value opportunities. Thus, the eCM solution will reduce expenses and increase revenue.

2. System analysis - The goal of system analysis is to break down complex equipment and processes into manageable subsystems that can be modeled to the accuracy needed to detect the failures identified by value analysis. To do this, engineers who implement eCM solutions must understand their equipment. Analyzing equipment according to engineering principles provides a base level of familiarity to build successful models. In addition, expert interviews provide the information necessary to identify failure fingerprints that will be useful in providing diagnostic advice to operators.

3. Data analysis - eCM models require historical data that explain normal equipment behavior. The data should represent the complete operating range to provide a highly efficient model. SmartSignal’s eCM WorkBench includes many tools to help visualize and extract normal operating data.

4. eCM WorkBench application development - The analysis stages provide the decision points necessary to build models and rules to detect early warning of failure. The eCM WorkBench provides an efficient model and rule-building tool that allows users to test the theories they developed while analyzing the system.

As illustrated in Figure 2-3, the results of each phase of the SmartSignal methodology help to focus and refine the effort in subsequent phases. For example, identifying valuable actionable failures in the value analysis helps to define which area of the plant needs to be analyzed. Only the data associated with equipment models of interest would be considered. Using the SmartStart process analysis methodology to reduce project scope through each stage, the application engineer can confidently implement an eCM predictive monitoring system to provide valuable, actionable advice for operators.



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Figure 2-5 The SmartStart Methodology Process

Information is gathered from operating, finance, maintenance, and engineering. A value analysis reveals the most valuable faults to catch. A system analysis identifies which signals and systems can be modified. The data analysis proves which faults can be seen with early warning and which financial savings can be achieved.

2.3.1 Value Analysis

A value analysis is the first step of the SmartStart methodology process. The end result of value analysis is the value pareto chart. The value pareto chart lists valuable, actionable fault modes that, if detected early with eCM technology, will quantifiably improve financial and operational results. Constructing the value pareto chart requires three steps.


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1. First, identify the type and frequency of failure modes for a given piece of equipment from historical reliability data.

2. Next, calculate the value of mitigating each failure mode, multiply by its frequency, and order the failure modes by value.

3. Finally, since the efficacy of eCM is limited by sensor availability, drop the failure modes that existing sensors cannot detect. It is also considered that some failure modes are unable to provide early warning before failure (such as lightning strike) and are disregarded.

Value calculations must include not only readily apparent charges but also hidden costs such as lost production. The early warning value hierarchy (Figure 2-6) provides a framework for calculating all the value associated with identifying an impending failure.

Figure 2-6 Early Warning Value Hierarchy

Early warning can be broken into catastrophic failure or minor failure and deterioration. These can be further broken into maintenance or operations effects and eventually to actual dollars.



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Effective eCM implementations deliver value when they detect equipment failure and emerging problems early enough for equipment operators to change their maintenance and operations practices. Altering business processes increases operational revenue and decreases maintenance expense. Early warning of failure and equipment condition monitoring achieves these improvements through the following mechanisms:

• Reduce the catastrophic failure rate - Recognizing imminent catastrophic failures in time can avoid costly repairs and reduce collateral damage to other parts of the machine or nearby equipment.

• Reduce the minor failure rate and gradual deterioration - Detecting minor failures and gradual loss of functionality can increase equipment efficiency and profitability.

• Shift unplanned downtime to planned downtime - Early detection of a fault frequently offers the choice to correct a problem during a regularly scheduled downtime or a more desirable time or place than an unplanned event.

• Extend the major overhaul interval - Interval extension is possible when operations has confidence that the system can catch catastrophic problems early enough and maintenance can become truly condition based with minimal risk.

• Reduce the maintenance duration - Early warning technology reduces the preparation, troubleshooting, and repair time required to complete maintenance activities on a piece of equipment by focusing attention on the failing elements.

• Achieve the operational excellence - In the best organizations, maintenance and operations departments work together to achieve the maximum return on equipment investment.

Once the value of each failure mode is calculated, the value pareto chart can easily be created. An example for a frequency pareto chart for a coal pulverizer is shown in Figure 2-7. This incorporates information about the failure modes and also the frequency of occurrence, completing Step 1 as described above. To determine the value pareto, multiply the frequency of failure by the number of failures by the cost per failure to achieve a dollar cost, and rearrange by priority (highest to lowest). Finally, those failure modes that could provide early warning through eCM are identified. The final value pareto chart gives the priority list of failure modes to identify, therefore also prioritizing equipment or processes that should be monitored to provide the greatest financial benefit.


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Figure 2-7 Frequency Pareto Chart of Pulverizer Failure Modes

Based on an EPRI report1, scaled on total failures sum to 100%. For example, shaft problems occur more frequently than mill fires.

The value pareto chart guides the implementation. It also serves as the basis for return on investment (ROI) projections during the project approval process. The value pareto chart drives creation of the models. It focuses the application engineer on building models to detect early warning of failures with the highest value.

2.3.2 System Analysis

The next step of the SmartSignal methodology is system analysis. In system analysis, the application engineer divides large systems and processes into smaller components that can be effectively modeled and determines how to use the output of these models to define and annunciate failure modes. Since eCM technology works most effectively when analyzing sensors that have strong deterministic relationships, this technique enables excellent modeling of extremely complex systems.

1 Derdiger, J. A., Bhatt, K. M., and Siegfriedt, W. E. Component Failure and Repair Data for Coal-Fired Power Units, Coal Mill Application, pp, 2–26. EPRI, Palo Alto, CA: Oct. 1981. AP-2071.



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System analysis provides three deliverables:

1. Process decomposition - Block diagrams that show how to divide equipment and processes into easily modeled components.

2. Process behavior analysis - Categorization of the operation into distinct modes and sensors into independent random variables, independent control variables, and dependent variables to guide modeling technology choices.

3. Fingerprint charts - Table that links failure modes with expected residual patterns. Since each failure mode has a unique, identifying combination of residuals, SmartSignal calls the table that relates failure modes to residuals the fingerprint chart.

Figure 2-8 Process Decomposition

The user defines the primary step(s) involved to achieve the primary equipment goal(s) and any auxiliary systems to support the primary steps.

2.3.2.1 Process Decomposition

Process decomposition defines the overall process goal and the inputs and outputs of energy, mass, and momentum. The process is broken down into primary and auxiliary stages. The raw inputs flow through the primary stages to create the final product, while auxiliary stages provide support for the primary stages. Around each stage, the engineer defines a control volume


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bounded by the laws of conservation of mass, momentum, and energy. Within each stage, the engineer identifies the principal inputs, products, mass, and energy flows, and the associated sensors.

Operating on the primary and auxiliary stages, the composition process continues recursively until each of the existing sensors are associated with each stage. In the process decomposition, gaps in sensor measurements necessary to effectively model a primary or auxiliary stage are identified. Sometimes, sensors are too expensive, or a sensor interferes with correct functioning of the process. When this occurs, proxy variables can account for key missing sensor measurements. Proxy variables substitute for the value of the missing sensors and correlate strongly with the key variable of interest.

2.3.2.2 Process Behavior Analysis

Process behavior analysis strives to understand process dynamics. By understanding such things as which sensors measure the primary inputs or independent variables, and which sensors measure the dependent variables, the application engineer can configure many of the eCM modeling features such as modes, adaptation, and model-style.

Commercial equipment rarely runs in one operational mode. Process plants commonly move through startup, part-load, and shutdown conditions as their normal cycle. Frequently, the available historical data cannot represent the full range of operating conditions for a given piece of equipment. For example, if only winter data were used when the model was created, the ambient temperature in the summer can represent a whole new operating state. In these cases, the eCM model must recognize when a new process state has been reached and adapt its model accordingly. The difficulty, of course, is distinguishing between a developing fault condition and a new process state. SmartSignal eCM incorporates many strategies to ensure that the system does not mistake a fault condition for a new operating condition. Correctly classifying the model sensors into the appropriate variable types is crucial to successful runtime adaptation.

Independent variables are external and represent input into the system. Sometimes, they can have no direct correlation to each other and can assume any value within their allowable range. Independent variables break down into control variables and random variables. A control variable typically responds to a set point on an automatic controller.

Dependent variables are system outputs. Note that in some cases variables can be dependent in one stage and independent in another. The physical laws governing the process and the state of the independent variables define the dependent variables.

Once the types of variables that a model contains are understood, developing adaptation strategies is much simpler. For example, imagine a process model that runs very accurately throughout winter and spring and begins to falter in the summer. By comparing the range of the ambient pressure and temperature used to train the model to the current ambient conditions, it should quickly become apparent that the equipment has entered a new operating regime and the models must be augmented. In addition to adaptation strategies, understanding variable types can help the user choose the type of eCM model best suited for the application.



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SmartSignal eCM employs two distinct model types: auto-associative and inferential. Auto-associative models generate an estimate for every independent and dependent input signal as a function of every sensor whether it is independent or dependent. While auto-associative models do not require a distinction between independent and dependent variables, variable groupings require strong sensor relationships. When key independent variables are unavailable from sensor measurements, auto-associative models provide the best results. Inferential models use measured independent sensors to generate the complex set of estimates for the independent and dependent variables. If the application engineer confidently accounts for all the independent sensors and uses high quality data, then inferential models can provide very accurate results.

2.3.2.3 Fingerprint Charts

SmartSignal models generate alerts when the residual value (the difference between the measured and estimated signal value) is statistically significant, that is, varies significantly from zero. Different failure modes will result in unique patterns of shifting residuals and alert events. If the actual value of a signal is greater than (less than) the estimate, then the residual value is positive (negative). Information captured in a fingerprint chart could help to troubleshoot problems based on residual patterns. For example, an increasing positive residual in a temperature sensor on a bearing could indicate overheating due to excessive rubbing. However, a high residual in a temperature sensor combined with a low residual in lube oil flow could indicate a problem with the lube oil pump.

Information that is captured in a fingerprint chart can be incorporated into the diagnostic portion of the eCM software to pass the information to the analyst. Expected residual patterns can be used to define eCM rules that generate an eCM incident. The SmartSignal rule engine interprets which residual shifts and alerts are significant enough to notify the operator of an incident and provide a preliminary diagnosis. Operators are notified of the incident through the eCM WatchList, which displays incidents that might require action to prevent a specific failure mode from occurring. The process in which alerts escalate to incidents virtually eliminates nuisance and false alarms from appearing on the WatchList.

Table 2-1 shows how a fingerprint chart relates failure modes to incidents in the pulverizer. Developing fingerprint charts requires expert interviews to complement analysis of the engineering data and drawings. In expert interviews, the people who have expert knowledge of, and experience with, the equipment are consulted for how the equipment operates and how it fails.


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Table 2-1 Fingerprint Chart for Baldwin Unit 3 Pulverizer 3A

OSI PITM Tag Name Description Units

Grinding Problems

Sensor Plugging

BAQ1331 Opacity PCT

BYC5109 Cold air damper demand PCT

BYC5115 Hot air damper demand PCT

BYF2085I Coal flow KLBH

BYF2145I Air flow KLBH

BYI1091 Amps AMPS +

BYP1072 Air differential pressure INWC

BYP1134 Bowl differential pressure INWC + / - +

BYP1147 Base pressure INWC -

BYS5097 Feeder speed demand PCT

BYT1145 Inlet air temperature DEGF

BYT1515 Fuel air temperature DEGF

BYZ1109 Cold air damper driver position PCT

BYZ1115 Hot air damper driver position PCT

FDRASPD Feeder A speed PCT

Notes: PCT = percent KLBH = kilopounds per hour (1 lb = 0.45 kg) AMPS = amperes INWC = inches of water column (1 in. = 2.54 cm) DEGF = °F = (°C × 9/5) + 32

Process decomposition and behavior analysis provide the application engineer with sufficient background to interview an expert technician, operator, or engineer to determine how the likely failure modes would manifest themselves in terms of the process variable shifts. During the expert interview, the application engineer seeks to translate the expert’s internalized troubleshooting process and operating rules of thumb into the fingerprint chart format so that the rules can be programmed after the models are built.



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2.3.3 Data Analysis

After system analysis has identified which models are best suited to detect crucial failure modes, data analysis identifies and prepares predefined normal data for building eCM models. To build models that provide early warning of failure with minimal false alerts, application engineers require reference data that represent normal or consistent states of the process or equipment. Raw data can consist of time stamped sensor readings over a period of days, weeks, or months, depending on the cycle time of the process.

SmartSignal eCM includes a number of tools to help identify and capture normal data. The use of the four features described below helps prepare reference data that will result in models that provide early warning of failure:

1. The application engineer can use filtering tools to eliminate abnormal data spikes and compressed data. For example, self-calibrating instruments can spike through the high and low ranges at regular intervals and interrupt otherwise normal data. The filter tools remove data that exceed a fixed number of standard deviations on either side of the average, that lie outside some threshold values, or that drop out into non-numeric values. Some data historians compress data into flat signals or piecewise linear blocks that reduce the data fidelity. The filter tool can automatically delete these sections if the application engineer believes they obscure the data.

2. The application engineer uses graphing tools to ensure that data represent normal operation. Time series graphs plot each sensor value against time. A selection tool provides a mechanism to remove blocks of data that are not normal based on visual inspection of the trends.

3. The WorkBench application or software also includes correlation tools to help ensure that the variable grouping produced during process decomposition did not miss any key relationships. The Pearson correlation tool calculates the Pearson correlation coefficient of every variable against every other variable over the entire time series.

4. Using a wizard, an application engineer can define rules that sort incoming data into one or more distinct operating modes. Data grouped by modes are often easier to model. For a power generation combustion turbine, the full load-operating operating mode could include data only when the power output exceeds 100 MW. In another example, full load power is defined as greater than 90% maximum load; part load power is 30 to 90% power; and power less than 30% is unit off.

2.3.4 eCM WorkBench Application Development

Value analysis, system analysis, and data analysis provide the sensor groupings, the raw data, and the background for building and testing models and rules. Using this information, the application engineer uses eCM WorkBench to build models based on normal data obtained during the data analysis step.


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The application engineer builds the initial models and rules in eCM WorkBench and tests the output to determine whether the variable groupings, the dependent versus independent, and inferential versus auto-associative model choices accurately represent the process.

2.3.4.1 eCM Model Testing

With the initial models in hand, the application engineer uses eCM Workbench model testing tools to determine whether the models will enable early warning of failure with minimal false alerts. The first test tool runs the model over a set of test data. WorkBench provides time series plots of the residuals for all the sensors and displays indications of alerts and incidents based on the rules. If the initial model does not achieve the desired accuracy, the application engineer should double check that the reference data represented normal operation. The application engineer can consider adding or deleting variables from the model or changing the model type.

The application engineer can use the simulated disturbance tool to determine whether the model responds to changes as expected. To aid in the model troubleshooting process, eCM WorkBench includes a tool to add a gradual drift, to add a step change, or to add a constant to one, some, or all of the sensors in the model. The simulated disturbance tool provides a good mechanism for determining abilities and limitations of a model to detect signal deviations. For example, the application engineer can test if a 5°F (2.8°C) temperature shift of an input variable produces a statistically significant residual. The engineer can test the amount of deviation that will be annunciated by eCM, which can be linked to how much early warning a model can provide.

Refining eCM models is typically an iterative process. An application engineer can tune the accuracy of a model by a number of different methods. Examples include increasing the amount of reference data, adding or deleting sensors, attempting different model types, employing adaptation strategies, changing run-time parameters, and so on.

Once the application engineer is satisfied with the new model’s fidelity, the next step is selecting the algorithms and sensitivities that will be used to analyze residual signals and, in turn, generate alerts. The key consideration is that the model is sufficiently accurate, and the alert logic sufficiently sensitive, to provide early warning of failures defined in the value pareto chart .

2.3.4.2 Rule Testing

Process behavior analysis and the fingerprint chart development lay the groundwork for incident rules. The application engineer uses the WorkBench rule editor to translate the fingerprint chart into rules that interpret the residual signals and alert events to generate incidents that will appear on the WatchList. During the model testing process, the application engineer also checks that the rules operate as expected. The simulated disturbance tool provides a good method to vary sensors in a controlled manner and investigate whether the rules operate according to the design.

SmartStart methodology was used to develop the pulverizer model philosophy. This philosophy was to detect grinding problems caused by roller journal bearings seizing and insufficient fineness of coal leaving the pulverizer. Also, this philosophy included the early detection of motor problems relating coal grind rate to current draw and motor vibration. In the value



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analysis, the value drivers were determined to be roller bearing outage interval extension, sensor plugging duration reduction, and motor problems shifted from unplanned to planned outages.

The system analysis broke the equipment and process into easily developed models. Fingerprint charts were developed to capture residual patterns, and so they can be incorporated into eCM incident rules, which will generate eCM incidents to the WatchList. In data analysis, the raw data from time stamped sensor readings are prepared for building eCM models. Tools with the eCM software are applied to filter the data to eliminate abnormal data spikes. Once the sensors were grouped, the initial models were built and tested. During the iterative process of refining and testing the eCM models that were built, it was realized that motor failure bearing and lubrication temperatures did not correlate with the other process variables to predict grinding problems. These sensors were removed from the model.

The eCM models ran with 15 sensors for about six months. In the six-month project review, the determination was made that the modeling was capable of predicting some failure modes and that significantly more failure modes could be detected if vibration sensor data were applied to the modeling. Secondary models were developed including the vibration sensor utilizing wireless technology to transmit the signal. The project is still in the iterative stage of model testing. The preliminary model rules utilizing the vibration sensor are being monitored for their capability to predict failure. As more experience is gained the eCM rules will be modified to enhance failure detection.


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3 MERGING ECM WITH WIRELESS VIBRATION TECHNOLOGY

The SmartSignal eCM technology was applied to process variables in a coal pulverizer, creating models for equipment condition monitoring. To provide more comprehensive predictive condition monitoring system for the pulverizers, eCM was then merged with wireless vibration technology

3.1 Pulverizer Value Analysis

The pulverizer is a critical asset that serves two main purposes:

• The pulverizer reduces large chunks of coal into particles suited for combustion.

• The pulverizers provide contact between the coal and hot air to dry out the coal.

Coal is delivered to the plant at a 1.5 to 2.0 in. (38.1 to 50.8 mm) size and is reduced to 0.75 in. (19.1 mm) top size in a coal conditioner. The 0.75 in. (19.1 mm) coal enters the pulverizer with hot air and is reduced to the consistency of face powder. The output of the pulverizer is a two-phase mixture of hot air transporting pulverized coal to the (boiler) burners.

3.1.1 Description of Pulverizer Function

In a pulverizer, raw coal is moved over a grinding ring by centrifugal force. Coal is passed under spring or hydraulically loaded rollers. It is pulverized by a combination of rolling, crushing, and attrition. Partially ground coal passes over the edge of a rotating bowl and is entrained in the rising hot air stream, flash dried, and carried upward toward a classifier. The larger coal particles are thrown out, or dropped out, of the stream to fall back into the bowl. The smaller particles and fine material enter the classifier tangentially through a number of circumferentially located openings. Externally adjusted vanes, located within the periphery of the classifier, impart a spinning action to the coal/air mixture. The more spin imparted, the finer the product leaving the pulverizer. Material that is oversized is rejected by the classifier and returned to the bowl for further grinding. The fine material is carried out of the pulverizer by the air stream and into the furnace for combustion. Approximately 75 to 80 % of the classifier input is returned to the pulverizer body where it mixes with the incoming raw coal. This circulation produces some pre-drying of the raw coal and reduces the average particle surface moisture in the flash drying zone. This enables the pulverizer to handle coal of varying moisture content without reduction of pulverizer capacity or classification efficiency up to the limitation of the hot air supply. Foreign materials in the coal fall through or spill over through an annulus around the rotating bowl and are rejected from the lower housing of the pulverizer.

EPRI Licensed Material Merging eCM with Wireless Vibration Technology

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Fine grinding of coal is necessary to ensure complete combustion of coal in the furnace for maximum boiler efficiency. However, coal can be ground too finely, resulting in the use of unnecessary amperage (or power). Also, the coal could not be ground finely enough, which could result in boiler plugging, coal pipe plugging, and/or carbon carryover or delayed ignition due to incomplete combustion. The coarser the coal is ground without plugging, the greater the efficiency of the pulverizer and the combustion process. Furthermore, the combustion process is more efficient if the fineness increases with increasing burner elevations. This is true because of the decreasing time for combustion as the elevations increase. Units where low nitrous oxide burner nozzles with over-fire air systems are utilized are more sensitive to coal sizing for proper combustion.

In many plants, environmental constraints and economics dictated the use of low sulfur Powder River Basin (PRB) sub-bituminous coal instead of the substantially higher heat content of the original bituminous coal required for plant design targets. The characteristics of PRB coal have several ramifications on plant operations. Its lower heat or Btu content and high moisture content requires that for the same amount of steam and power output, the plant’s boiler requires more coal than the original plant design. This requirement also affects the sizing of the air heater and other heat recovery sections of the furnace, which could result in not enough hot air being available.

The temperature of the coal and air mixture from the pulverizer to the burner is important. Generally, 155 to 160°F (68.3 to 71.1°C) is recommended as coal pipe temperature for high moisture coals. Some plants that switched to PRB sub-bituminous coal are unable to reach this temperature due to insufficient hot air being available from the air heater. As a consequence, this puts a greater load on the coal pulverizers to keep up with coal demand. The lower heat content coupled with excessive moisture could further limit pulverizer throughput exceeding the capacity of the hot air used for drying in the pulverizer. The higher moisture content in the coal results in additional load on pulverizer capacity to the extent that enough coal cannot be delivered to the furnace to maintain full load.

Low coal pipe temperatures are common, and when coal gets wet in handling and storage, the problems get worse. Low coal pipe temperature contributes to carbon carryover and upper furnace slagging. For example, plants that historically operated on three coal pulverizers and kept one pulverizer in reserve now require the fourth pulverizer to run in order to maintain power output. The original design or pulverizer selection basis was one full spare with remaining pulverizers operating at a 90% capacity level. This makes it more likely that any upset in a pulverizer will reduce coal production to the point that a unit is forced to reduce power output. The pulverizers have become a critical piece of plant equipment, and their operating availability a capacity limiting constraint for the plant.

In addition to the lower Btu content, PRB coal is much softer than the coal the plant was designed to burn. This has the positive effect of extending the useful life of the rollers in the pulverizers. Historically, the rollers have been the most frequent cause of pulverizer failures, and a plant could plan around certain number of operating hours before a roller would need to be replaced. While the rollers were being replaced, the plant could perform other needed maintenance during the down time.


Merging eCM with Wireless Vibration Technology

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Now because of the longer life of rollers, pulverizers are experiencing a greater frequency of unexpected shutdowns due to other causes. Plant personnel have not as yet developed the tools for early detection of these new failure modes. In fact, the relative scarcity of sensors in the pulverizers and extremely harsh environment inside the pulverizers make this job very difficult and one ideally suited for these new technologies.

3.1.2 Benefits of Early Warning of Pulverizer Failure

A SmartSignal eCM system could provide the key benefits of shifting unplanned repairs to planned and also reducing the duration of repairs. The plant can then experience both maintenance expense reduction and revenue improvement. The calculations in Table 3-1 show how 12 hours of early warning of pulverizer failure provides annual benefits of $100,000 at a 600 MW unit such as Unit 3 of the Baldwin Energy Complex. Consider a roller journal bearing failure that occurs on a Tuesday morning in July around 6:00 AM. The unit must be immediately derated. The maintenance team must scrounge for replacement parts and scramble to repair the problem with whatever maintenance crew is available. Consider the same failure with 12 hours of early warning, annunciated on Monday at 6:00 PM. The plant could requisition parts from the warehouse, schedule the proper craftsmen to do the repair on the night shift, and take the derating during nonpeak power periods.


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Table 3-1 Effect of 12 Hours of Early Warning of Pulverizer Failure

Expected Benefits Per Incident for a 600 MW Unit

Reduce Maintenance Expense

Reduce work-hours by optimizing logistics and scheduling

15 work-hours at $50/hour $ 750

Increase Revenue by Shifting Maintenance to Nonpeak Production

12 hours at 600 MW versus 12 hours at 510 MW $ 21,600

Total $ 22,350

Expected Annual Benefits for a 600 MW Unit

Mean Time Between Failure (Year) 0.5

Number of Mills per Units 6

Peak Fraction 37%

Annual Expected Value of 12 Hours Early Warning $ 97,938

One incident could save $22,000; yearly benefit for a plant is nearly $100,000.

3.2 Pulverizer System Analysis

The six Raymond Bowl pulverizers of Unit 3 at the Baldwin Energy Complex are CE-Raymond Model 923 RP pulverizers. They use a three-roller system to produce pulverized coal suitable for combustion. Each pulverizer is sized to grind 126,000 lb/hr (47,028.4 kg/hr) of Midwestern Sub-bituminous coal with a Hardgrove Grindability of 56 to fineness such that 70% of a sample will pass through a 200 mesh screen. The percentage on 50 mesh should not exceed 1.5% because oversized coal will contribute to poor combustion, affecting nitrous oxide output, slag, and high carbon carryover. As discussed earlier, the actual capacities are affected by the coal moisture and grindability.



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3.2.1 Coal Pulverizer eCM Models with Original Process Variable Sensors

During the SmartSignal eCM implementation kickoff meeting, Dynegy Midwest Generation (DMG) pulverizer specialists explained the key failure modes experienced at the Baldwin Energy Complex. Based on the sensors available, the joint SmartSignal-Dynegy team estimated that SmartSignal eCM could provide early warning of failure modes that represent about 11% of pulverizer breakdowns, primarily grinding element problems and motor problems. All of the grinding element failure modes, and half of the motor failure modes, can be detected early using the original process variable set.

The fingerprint chart in Table 2-1 lists the sensors used in each model and associates failure modes with expected residual deviation patterns. Each failure mode is illustrated as having a unique, identifying combination of residual patterns, which were the basis for the original pulverizer model. Grinding problems have characteristically high amperage residuals and can have high or low pulverizer bowl differential pressure residuals. Sensor plugging is characterized as a high residual on bowl differential pressure and a low residual on base pressure. Figure 3-1 shows a schematic diagram of the pulverizer and the roles of the process variables.


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Figure 3-1 Schematic of Coal Pulverizer

The goal of the designed model was to detect subtle shifts in current draw and/or bowl differential pressure that would signal roller bearing failure, loss of grinding efficiency, or other mechanical problems with the pulverizer that could impact the viability of that pulverizer and possibly cause a derate of the unit. Detection of these problems hours or days in advance would enable Baldwin personnel to perform any additional tests or maintenance during off-peak hours and minimize the risk of losing generation capacity.

From the beginning, it was understood and accepted that the diagnostic capability of such a model was limited by several factors. The quality of the pressure, flow, and temperature readings was suspect given the harsh sensor environment of the pulverizer. As time went on, it became clear that plugging of the pressure impulse lines resulted in a reading that was not reliable. The



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ability to have confidence in subtle shifts in bowl differential pressure diminished, and each incident had to be first reviewed as a possible line plug that needed purging. In fact, the majority of the incidents detected by the SmartSignal eCM monitoring platform were acting as sensor validation because they were, in fact, pressure tap line plugs that needed to be manually purged. There was one incident that preceded a roller seizing by several days, but there were two mechanical failures on the pulverizer that did not provide any warning.

The original sensors used in the model consisted of the first 15 signals shown in Table 3-2. Temperature sensor data were available from the motor inboard and outboard bearings as well as from the lubrication system. While bearing temperatures and lube system operation can provide information to prevent motor and worm gear failures, they were not part of the primary coal pulverizer system (see Figure 2-7 regarding primary versus auxiliary systems). These temperature signals did not directly correlate to the process variables used in the initial coal pulverizer model and do not contribute to predicting grinding problems or reduction of fineness problems. In absence of vibration sensor information, motor bearing temperatures and lubrication sensors operate independently of the system that predicts grinding efficiency or mechanical problems, which were the primary goal of the model.


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Table 3-2 Signal List for Enhanced Model

Tag OSI PI Tag Name Description Units

1 BAQ1331 Opacity PCT

2 BYC5109 Cold air damper demand PCT

3 BYC5115 Hot air damper demand PCT

4 BYF2085I Coal flow KLBH

5 BYF2145I Air flow KLBH

6 BYI1091 Amps AMPS

7 BYP1072 Air differential pressure INWC

8 BYP1134 Bowl differential pressure INWC

9 BYP1145 Base pressure INWC

10 BYS5097 Feeder speed demand PCT

11 BYT1145 Inlet air temperature DEGF

12 BYT1515 Fuel air temperature DEGF

13 BYZ1109 Cold air damper driver position PCT

14 BYZ1115 Hot air damper driver position PCT

15 FDRASPD Feeder A speed PCT

16 VIBPOINT-1 Motor outer bearing in./s

17 VIBPOINT-2 Motor inner bearing in./s

18 VIBPOINT-3 Worm drive inner bearing in./s

19 VIBPOINT-4 Worm drive outer bearing in./s

20 VIBPOINT-5 Vertical shaft lower bearing in./s

21 VIBPOINT-6 North roller vibration g

22 VIBPOINT-7 South roller vibration g

23 VIBPOINT-8 East roller vibration g

Notes: PCT = percent KLBH = kilopounds per hour (1 lb = 0.45 kg) AMPS = amperes INWC = inches of water column (1 in. = 2.54 cm) DEGF = °F = (°C × 9/5) + 32



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The key piece of information that was missing that could have helped detect these failures and differentiate between sensor issues and mechanical faults was vibration information from the bearings on the pulverizer and motor. The original model development for monitoring the pulverizers was done in coordination with DMG experts and engineers on-site at the Baldwin facility. With the scarcity in available sensors on the pulverizers, nearly all of the available sensors were used to build a model capable of monitoring each pulverizer and its driving motor.

The pulverizer value drivers are:

• Detecting grinding problems, such as roller journal bearing seizing and insufficient fineness, to gain interval extension between pulverizer overhauls

• Detecting motor problems relating coal grind rate to current and motor vibration to shift from unplanned to planned maintenance

• Detecting sensor plugging of the pressure differential signal to avoid operational impact and unnecessary derate

Early warning on motor vibration problems cannot be diagnosed based only on the process variables listed in Table 2-1.

3.2.2 Coal Pulverizer eCM Models with Added Vibration Sensors

DMG Engineering recognized the need to include mechanical vibration data along with the available process parameters for the coal pulverizer model after the failure to detect a cracked vertical shaft on one of the pulverizers. The original pulverizer design does not include vibration instrumentation to support continuous monitoring. Although vibration data were routinely collected and analyzed through the use of portable hand held instruments on a quarterly basis, this provides only a snapshot view of current conditions and does not provide real-time correlation with other process parameters or historical trends associated with dynamic operating conditions.

The need for continuous vibration data on the pulverizer gearbox and roll journals is essential to identifying key failure modes and degraded performance associated with items such as excessive roll clearances or unequal loading of the rolls. One coal pulverizer was targeted for a pilot study in a tailored collaboration with EPRI to evaluate the use of wireless vibration technology.

Figure 3-2 is a breakdown of the failure modes identified in Figure 2-7, demonstrating the proportion of each failure mode that can be detected with early warning for various model configurations. For example, for shaft problems (which make up 18% of all pulverizer failures), only a small amount of shaft problems can be detected by vibration signals alone, and none by the process variables alone, but nearly half of shaft problems can be detected with eCM models that incorporate both vibration and process variables. Therefore, eCM models that combine the two signal types are much more capable of providing early warning of failures than models made by either signal type. The sum total amount of failure modes detectable by vibration signals alone is 7%, the total by (original) process variables alone is 11%, and the total failure modes detectable combining vibration signals and process variables is 46%.


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Figure 3-2 Frequency of Pulverizer Failure Modes: Breakdown by Available Signals

A review of the project after six months concluded that while the current approach had promise, significantly more failure modes could be detected if additional sensor data were available and could be used in the model, particularly vibration sensors. At this point, it was decided to utilize a wireless vibration network provided by 3e Technologies, Inc. (3eTI) to continuously monitor vibration on key pulverizer components. Table 3-2 illustrates the vibration sensors (Tags #16-23) that were added to the pulverizer and drive motor. After the eight wireless vibration sensors were installed, SmartSignal developed a secondary pulverizer model that was the original model plus the eight additional sensors.

The monitoring project began on 5/01 and has been collecting data at five-minute intervals ever since. The analysis that follows is on data that were collected between 5/16 at 9:00 AM and 05/18 at 9:00 AM. The modeling used data collected between 05/01 and 05/13 as a reference during periods of indicated stable behavior. For the next month, the SmartSignal model monitored the performance of the pulverizers, but with the exception of a temporary increase in roller vibration and an increase in motor current in all the pulverizers, there were no mechanical incidents with the pulverizer.



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3.3 The Wireless Vibration Network

A cost-effective solution with a short implementation schedule was needed to fit the timeline of the SmartSignal pilot project. DMG Engineering evaluated the use of contemporary wireless technologies for transmitting vibration information to their data archive/historian software. Research of the various wireless technologies available included Bluetooth, IEEE Standard 802.11b, and 900 MHz systems. Following the evaluation of several commercial systems, DMG selected the IEEE 802.11b wireless standards that indicated better signal strength and range within a power plant environment, as well as providing nearly real-time data transfer capabilities.

Figure 3-3 Data Flow from Wireless Vibration Network

The application of wireless vibration network and sensors involved adding:

• Vibration sensors from Wilcoxon Research

• Data collecting sensors (Wireless I/O Node) from 3eTechnologies, Inc. (3eTI)

• Wireless technology (transmits data from a parabolic antenna) from 3eTI

• Wireless Access Point system receiver 3eTI

• Wireless Gateway (forwards data to plant OSI PI historian) from 3eTI

• SmartSignal eCM (retrieves data from plant historian and performs analysis)

DMG Engineering selected the 3eTI’s network capable application processor (NCAP) system that operates at 2.4 GHz with a 100 mW signal having a range of up to 300 ft (91.44 m). Wilcoxon 786A general purpose accelerometers were hard wired via armored cable to the eight-channel NCAP transmitter that provides both power and signal conditioning for the sensors.

Vibration accelerometers were installed on each of the three-roll journal trunion shafts, inboard and outboard worm bearings, lower vertical shaft bearing, and the motor inboard and outboard bearings utilizing an epoxy/pad mount (Figure 3-4). The Access Point wireless system receiver utilizes an ethernet connection with the plant’s local area network (LAN), where a dedicated


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desktop computer receives the data and provides an interface with the plant OSIsoft PI Data Archive server. DMG utilized installation services of the wireless system by 3eTi, including a signal strength survey to determine the optimum location for the Access Point and configuration of the NCAP transmitter.

Figure 3-4 Roller Bearing Vibration Sensor Installation

The NCAP transmitter was mounted on a structural I-beam (Figure 3-5) within a few feet of the pulverizer, and the Access Point was mounted approximately 270 ft (82.30 m) away in a maintenance shop with a nearby ethernet connection (Figure 3-6). The eight channels within the NCAP were individually configured to provide proper signal conditioning, data sampling rates, units of measure, and frequency range for the application. The NCAP was configured to provide an update of vibration magnitudes at 30-second intervals in support of the 1-minute scanning frequency of the data archive program. The eight inputs to the NCAP were installed with the capability to install a portable vibration analyzer/data collector to obtain additional diagnostic data if needed.



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Figure 3-5 NCAP Transmitter Installation

Figure 3-6 Antenna and Access Point Installation


4-1

4 RESULTS

This section is divided into presenting and discussing the results from the original model configuration (Case A) with process variables and from the enhanced configuration with process variables and eight vibration signals (Case B).

4.1 Case A Results: Models on Initial Process Variables

The initial eCM model was built on data available from the Baldwin plant data historian (OSI PI system). The data were extracted from 5/1/02 through 5/13/02. Test data for model testing were collected from 5/1/02 through 5/24/02. Incident rules were designed to trigger incidents for grinding problems and sensor plugging. These rules were based on patterns of deviations indicated in the fingerprint chart (Figure 3-2) where a grinding problem is identified by an increase in pulverizer amps and a significant increase or decrease in bowl differential pressure, and sensor line plugging is identified by an increase in bowl differential pressure while there is a change in base pressure.

Prior to the start of eCM monitoring, the rolls on Pulverizer 3A needed adjustment. Operators had observed deterioration in coal fineness and heard noise coming from the rolls contacting the bowl. The rolls were adjusted on 10/25/02.

Monitoring of Baldwin Unit 3 Pulverizer 3A began on 11/02. Data were received from a live feed from the plant historian at 5-minute intervals and processed by the eCM Run-Time Engine in real time. The modeling results are stored in the eCM database and are viewable through the eCM WatchList Manager. On 11/22/02, eCM began annunciating incidents on the WatchList. Significant residuals were registering on Pulverizer 3A motor current draw; at the same time there were high residuals on bowl differential pressure. These abnormal conditions persisted for 7.5 hr before readings returned to normal. Figure 4-1 shows the bowl differential pressure for the period the 11/22 to 11/25/02.

EPRI Licensed Material Results

4-2

Figure 4-1 Bowl Differential Pressure: 11/22/02 to 11/25/02

The top chart shows the actual values (dark) and estimates (light). The bottom chart shows the residuals. The Y-axis is pressure (actual/estimate range 0 to 10, residual range -6 to 3) in units of INWC. The X-axis is the sample number (range 0 to 4300). The “X” marks at the top of lower figure indicate alerts, and the black diamonds indicate incidents.


Results

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Figure 4-2 Current: 11/22/02 to 11/25/02

The top chart shows the actual values (dark) and estimates (light). The bottom chart shows the residuals. The Y-axis is current (actual/estimate range 0 to 110, residual range -80 to 20) in amps. The X-axis is the sample number (range 0 to 4300). The “X” marks at the top of lower figure indicate alerts, and the black diamonds indicate incidents.

Figures 4-1 and 4-2 are images from the eCM WatchList Manager sensor views for the bowl differential pressure and current, respectively. In the bottom chart of Figure 4-1, high residuals are visible on bowl differential pressure between samples 1000 and 1400. Alerts begin around sample 1000, and the persistence of alerts triggers incidents beginning around sample 1200. Similarly, the bottom chart of Figure 4-2 indicates a high residual on pulverizer amps with subsequent alerts and incidents. For the next few days following this abnormal period, the signals returned to normal with some occasional alerts on differential pressure (occurring on Saturday and Sunday). This is visible in Figure 4-1 around samples 2400, 3800, and 4000. No immediate action was taken based on the intermittent nature of the subsequent alerts.

In the investigation of the abnormal conditions on Pulverizer 3A, there were no significant deviations found in any of the other five pulverizers during the same time period. This indicated that a coal change or process change was not a likely cause of abnormal deviations. Potential roller bearing failure diagnosis was not verified because signals returned to normal.


4-4

On Monday, 11/24/02, during routine walk-around monitoring, the operator heard noise coming from Pulverizer 3A. The pulverizer was taken out of service, and an internal inspection was conducted. The in-place inspection of the east roller revealed that the roller was not turning and the dust seal was leaking oil. The roller was replaced with an overhauled roll, the spring preload was adjusted, and the mill was returned to service.

Figure 4-3 Bowl dP (L) and Current (R): 12/6/02 to 12/10/02

The “Y” range on bowl dP is 5 to 9 (actual) and 0 to 2 (residual) units of INWC. The “Y” range on current is 73 to 93 (actual) and -4 to 4 (residual) units of amps. The “X” range has 5700 samples.

On 12/6 and 12/8/02, slight positive residuals were seen in the Pulverizer 3A bowl differential pressure and an increase in residuals on pulverizer motor amps (see Figure 4-3). The operator investigating Pulverizer 3A provided a diagnosis that there was likely a motor rub, audible to the operator again during walk-around monitoring. An outside contractor, who was called in to investigate the incident, further identified the incident as an eccentricity problem on 12/10/02. The problem was determined to not be severe enough to require repair at that time.

On 12/16/02 the vertical shaft (from the gear drive to the bowl) broke. Determination was made that roll tension might not have been tight enough. The pulverizer was subsequently overhauled. The overhaul included replacement of the vertical shaft and the worm gear, and all three rolls were removed and replaced with overhauled units.

During the pulverizer system analysis (see Section 3.2), we discussed how accurately predicting grinding problems was hindered due to frequent sensor line plugging. An example of sensor line plugging was seen shortly after Pulverizer 3A was returned to service on 1/23/03 from the


Results

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overhaul. Figure 4-4 shows the base pressure and bowl differential pressure for 1/16/03 to 1/25/03. High residuals were seen on bowl differential pressure, and a change in base pressure was observed. The signal characteristics of bowl differential pressure and base pressure changed significantly over the course of 10 days from 1/16 through 1/25/03.

Figure 4-4 Base Pressure (L) and Bowl Differential Pressure (R): 1/16/03 to 1/25/03

The “Y” range on base press is 20 to 27 (actual) and -2 to 0.5 (residual) units of INWC. The “Y” range on bowl dP is 4 to 10 (actual) and -1.5 to 1.5 (residual) units of INWC. The “X” range has 11,000 samples.

Note that in Figure 4-3 the range of the noise (signal variation) on the bowl differential pressure increased from about 0.25 to nearly 2 INWC. The noise on the base pressure signal decreased from 5 to 0.5 INWC. The diagnosis in this incident was probable pressure sensing line plugging. The sensing lines were manually purged.

4.2 Case B Results: Models with Vibration and Process Variables

The wireless vibration sensors system was installed on Pulverizer 3A in the end of 4/03. A new model was developed for Pulverizer 3A, and live data began flowing to the eCM run-time engine with results viewable in the WatchList Manager on 5/1/03.

The model continued to run without reporting any system incidents. There were two issues with the data quality of the wireless sensor system that have hindered full utilization of the vibration sensors. The first was a vibration sensor problem with the east roller vibration reading inaccurately high, which was fixed in mid-July. The second problem was due to interference


4-6

between the wireless transmitter and receiver. The result was periods of flat readings from the sensors that are not useable as reference data and reduce the reliability of the diagnostic logic. Both of these issues are discussed below.

The wireless monitoring models were rebuilt in mid-August removing all data prior to the south roller vibration sensor being repaired. Additionally, the data were manually filtered to remove the periods of lost signals. This model is currently up and processing data. But, since then, there have not been any mechanical failures with the pulverizer.

Figures 4-5 and 4-6 illustrate vibration data received from 5/1/03 through 7/1/03. Most vibration levels are within about 0.2 in./s (5.08 mm/s) or 1 g. The period in the middle of the figures (samples 6500–10,500) represents a time when the unit was out of service. The east roller (VIBPOINT-8) saw an elevated vibration magnitude reading from 5/1 through 6/11 as a result of a sensor problem. The signal was inaccurately registering a value between 10 and 20 g. Note that even with one sensor failing by reporting (erroneous) readings too large by an order of magnitude, the model demonstrates robustness in maintaining accuracy in the remaining sensor estimates within a threshold of 0.1 in/s (2.54 mm/s) and 0.1 g.


Results

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Figure 4-5 Base Pressure (L) and Bowl Differential Pressure (R): 1/16/03 to 1/25/03

In the figure, the dark line (behind) is the actual value. The overlying light line (on top) is the model estimate. The red “X” indicates any samples with alerts. The “X” range is from 0 to almost 88,000. The “Y” range is approximately 0 to 0.4 in./s (0 to 10.16 mm/s).


4-8

Figure 4-6 WatchList Results: VIBPOINT 5-8, 05/01–7/1/03

The “X” range is from 0 to about 88,000. The “Y” range on VIBPOINT-5 is 0 to 0.6 in./s (0 to 15.24 mm/s), on VIBPOINT-6 is 0 to 1.8 g, on VIBPOINT-7 is 0 to nearly 2.8 g, and on VIBPOINT-8 is 0 to 35 g.


Results

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Figures 4-7 and 4-8 illustrate vibration data from 6/12/03 through 8/27/03 for the same vibration signals. Every sensor had a significant number of dropouts and lost signals and these are most clearly visible in the signals VIBPOINT-5 and VIBPOINT-7 of Figure 4-8. Signal loss hinders the ability to diagnose failures. For example, consider the south roller vibration sensor (VIBPOINT-7, third chart of Figure 4-8). Toward the end of this period, the analyst can see the beginning of an increase in vibration magnitude. However, the signal was lost for the remainder of the period shown, so clear diagnosis of a growing deviation is not possible.


4-10


The “X” range is from 0 to about 22,000. The “Y” range is approximately 0 to 0.4 in./s (0 to 10.16 mm/s).


Results

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The “X” range is from 0 to about 22,000. The “Y” range on VIBPOINT-5 is 0 to 0.6 in./s (0 to 15.24 mm/s). The “Y” range tics on others are 1 g apart.


4-12

Individually, process variables and vibration signals provide limited information about plant operation and enable engineers and operators to assess plant health and plan accordingly. However, there are a lot of failure modes that are not clearly, if at all, identifiable in either of these sensor classes. The strength of building one model that looks at the behavior of both sensor classes is that vibration anomalies can be referenced against process changes (and vice versa) to enable a much higher degree of confidence in making a critical decision.

These vibration patterns seen could be differences in roll clearance. As this is a new observation, there is a need to gain confidence before calling for the shutdown of this pulverizer for maintenance. This pulverizer is being monitored closely to determine if these vibration spikes will trigger an incident indicating a trend to failure.

Prior to this experiment, Pulverizer 3A returned to service from overhaul on 1/23/03; therefore, the tests were performed on a newly overhauled machine. Since that date, the unit has not experienced an extensive amount of run hours. Also, since the addition of vibration sensors to the system, there has not been a significant amount of reliable signal data on the system, due in part to frequent signal loss, weak signals, and sensor plugging. Moving forward, engineering interest lies in monitoring performance as run hours increase, as well as the potential for failure. Sufficient data on the system have not yet been acquired to fully validate the incident rules.

Recently (after the latest results shown), the antenna system was changed to a different model. This resulted in some signal improvement. Differences in the vibration patterns among the three rolls (with the north roll vibration signal as reference) have been routinely occurring, and spikes in vibration amplitude are being observed. Local readings on Pulverizer 3A have been compared with other pulverizers and show higher than average activity on Pulverizer 3A. Based on these observations, Pulverizer 3A is being monitored closely because the current belief is that the roller tension could need to be adjusted and that is the cause for the periodic increases in vibrations.

Resolving signal reliability is the final step to gain confidence for detecting, alerting, and classifying failures for actionable early warning of failure in the pulverizers using the vibration signals with wireless technology. Based on observed trends when data are received, the value proposition of achieving 12 hours of early warning for pulverizer failure is achievable.


5-1

5 CONCLUSIONS

5.1 Technology Shows Promise for Coal Pulverizers

Success was demonstrated for the SmartSignal technology and SmartStart methodology in application to coal-fired plants. This has led to solutions for detecting, alerting, and classifying failures for actionable early warning of failure in components such as boiler feed pumps and recirculation valves, steam turbines, condenser efficiency, boilers, and precipitators. Knowledge capture has been achieved in the implementation of rules that trigger incidents to the WatchList. It was understood and accepted that the diagnostic capability of modeling a pulverizer could be limited by several factors. The quality of the pressure, flow, and temperature readings was suspect given the harsh sensor environment of the pulverizer. Plugging of the pressure impulse lines resulted in readings that were not reliable. The ability to confidently diagnose subtle shifts in bowl differential pressure was diminished because each incident had to be first reviewed as a possible line plug that needed purging.

The majority of the initial incidents detected by SmartSignal eCM monitoring were in fact pressure tap line plugs that needed to be manually purged. There was one incident that preceded a roller seize by several days, but there were two mechanical failures on the pulverizer that did not provide any warning because the necessary signals were not yet monitored.

The key piece of information that was missing and that could have helped detect these failures and differentiate between sensor issues and mechanical faults was vibration information from the bearings on the pulverizer and motor. Key points are that:

• Only a small percentage of mill failure modes (10%) can be detected by monitoring process variables and vibration.

• A larger percentage of mill failure modes (46%) can be detected by monitoring process variables and vibration data.

• Project results so far have shown vibration magnitude data can be modeled and correlated with process variables.

Figure 5-1 is a pareto chart that demonstrates the increased capability to sense failure with the addition of the wireless network as compared to the original configuration (with 11% of failure modes) and all possible failure modes. The addition of a vibration system increased the number of catchable failure modes to 46% (see Section 3.2.2 and Figure 3-2 for the breakdown). This chart demonstrates that SmartSignal monitoring, enhanced by using a wireless vibrations network, enables the ability to catch nearly half of all failure modes before they occur.

EPRI Licensed Material Conclusions

5-2

Figure 5-1 Cumulative Failure Mode Pareto Chart of Catchable Failures

5.2 Wireless System Obstacles and Lessons Learned

There were two primary problems that were experienced from the onset following the 3eTI wireless technology installation. First, an erroneously high vibration reading, on the order of ten times higher, was being obtained from one of the pulverizer roll trunion shafts. A portable vibration analyzer was utilized to compare signals from all three rolls locally with that observed in the data archive program and confirm the problem.

Troubleshooting revealed a suspected sensor cable anomaly. The cable was replaced and the signal was reduced considerably but still indicated a magnitude that was approximately double the locally obtained reading. More in-depth troubleshooting performed by the plant instrumentation and control (I&C) technicians revealed a missing ground screw on the channel PC card. Replacement of the ground screw corrected the problem.

The second problem encountered was an unreliable wireless signal that transmitted intermittently.

The NCAP transmitter was verified to be functioning properly, but the diagnostic log for the Access Point revealed a weak signal. 3eTI provided DMG with a directional antenna to channel a


Conclusions

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more concentrated signal for the Access Point. Although the new antenna did help reduce the frequency and duration of signal loss, the problem was not completely remedied. DMG encountered an increasing number of interruptions over the course of the next two months. 3eTI mobilized an engineer to troubleshoot the system and optimize its performance. Troubleshooting revealed a faulty Access Point, and a follow-up signal strength survey revealed the original location of the Access Point had a marginal signal. The Access Point was replaced by 3eTI, and its location was moved to a point approximately 40 ft (12.19 m) closer to the NCAP transmitter. In addition, the flat directional antenna was replaced with a parabolic antenna to further focus the signal from the NCAP.

5.3 Technology Works - Reliability of Signal Is an Issue

SmartStart methodology was used to develop the pulverizer model philosophy. This philosophy was to detect grinding problems caused by roller journal bearings seizing and insufficient fineness of coal leaving the pulverizer. This philosophy also included the early detection of motor problems relating coal grind rate to current draw and motor vibration. In the value analysis, the value drivers were determined to be roller bearing outage interval extension, sensor plugging duration reduction, and motor problems shifted from unplanned to planned outages. The system analysis split the equipment and process into easily developed models. Fingerprint charts were developed to define residual patterns that will be developed into eCM rules that will generate an eCM incident to the WatchList. In data analysis, the raw data from time-stamped sensor readings were prepared for building eCM models. Tools with the eCM software were applied to filter the data to eliminate abnormal data spikes. Once the sensors were grouped, the initial models were built and tested. During the iterative process of refining and testing the built eCM models, it was realized that motor failure bearing and lubrication temperatures did not correlate with the other process variables to predict grinding problems. These sensors were removed from the model. The eCM models ran with 15 sensors for about six months. In the six-month project review, determination was made that the modeling was capable of predicting some failure modes and that significantly more failure modes could be detected if vibration sensor data were applied to the modeling.

Secondary models were developed, including the vibration sensor utilizing wireless technology to transmit the signal. The project is still in the iterative stage of model testing. The preliminary model rules utilizing the vibration sensor are being monitored for their capability to predict failure. As more experience is gained, the eCM rules will be modified to enhance failure detection.

© 2003 Electric Power Research Institute (EPRI), Inc.All rightsreserved. Electric Power Research Institute and EPRI are registered service marks of the Electric Power Research Institute, Inc.EPRI. ELECTRIFY THE WORLD is a service mark of the ElectricPower Research Institute, Inc.

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on-line predictive condition monitoring system for coal pulverizers

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