wings to your thoughts….. preventive measures … measures for failure of power... · the problem...

www.ijaret.org Vol. 1, Issue III, April 2013 ISSN 2320-6802

INTERNATIONAL JOURNAL FOR ADVANCE RESEARCH IN

ENGINEERING AND TECHNOLOGY WINGS TO YOUR THOUGHTS…..

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Preventive Measures for Failure of Power Electronics

Component 1Rahul U. Kale,

2Pavan M. Ingale,

3Rameshwar T. Murade

Shaaz College of Engineering, Hyderabad1,2,3

India

[email protected], [email protected]

2

Abstract— The assessment of the useful lifetime of equipment has been a focus of intense interest of electric utilities, especially

since the industry-wide restructuring and competition have imposed tightening of the operation and maintenance budgets. An

accurate model of power apparatus life time should contain a large number of operational and environmental factors, most of

which are not practical for monitoring - like the history of exposure to moisture, electrical stress, mechanical stress, and for many

other factors whose only partial information is available e.g. installation dates and amounts, as well as failure and replace ment

rates. A novel approach of prediction of failure of electrical equipment based on the past data is proposed in the present research

work. In today’s competitive market, production costs, lead time and optimal machine utilization are crucial values for

companies. Since machine or process breakdowns severely limit their effectiveness, methods are needed to predict products’ life

expectancy. This work will be simulated on PC and the results will be validated using appropriate metrics. Information about the

remaining life of products and their components is crucial for their disassembly and reuse, which in turn leads to a more efficient

and environmentally roundly usage of products and resources. Development of the Watchdog Agent answers the aforementioned

needs through enabling multi-sensor assessment and prediction of performance of products and machines.

Keywords—IGBT, Watchdog Agent, TFC

1. INTRODUCTION

The problem of resource management has long been

recognized as one of the burning issues in electric utilities.

Knowing how much to invest in creating a reliable and

successfully performing resource pool (i.e . power equipments,

distribution cable network), when to repair or rep lace, and

what human financial resources are needed from year to year

in order fo r such a network to operate successfully, the

answers to those question may represent substantial savings

for the utility .

The purpose of proposed work is to propose a methodology

for identification of failure performance of electrical

equipments from past data. It is assumed that a population of

equipments of the same type is being tracked over a sufficient

period, with knowledge of time of installation, and failure

record is available. If a large amount of statistical information

about equipment failu re rate performance is availab le (which it

is usually), then it is possible to develop a predictive strategy.

Industry invests more money on Electrical & Control

Equipments. A failure of any equipment affects on the

production & consumes time. Avoid Failure & Fix

Mechanism. Power electronics devices must have reliab ility,

lifetime, and health monitoring & predict ive maintenance.

Power semiconductor devices are the main components of

power electronic systems, as well as one of their most critical

parts in terms of reliability. Considering the increasing role of

power electronic converters in crit ical functions, particularly

in human transport applications with significant safety

requirements, features such as reliability, lifet ime, health

monitoring, and predict ive maintenance become increasingly

important. In addition, in this application context, the

operating conditions often induce stress, particularly high

ambient temperatures and strong temperature variations . In

first section we have see the what is effect aging in IGBT.

Second section present system which used to diagnosis the

failure of equipment. Third section what is the prognostic

method how it is beneficial. Fourth section examples to how

benefit the prognostic method

2. AGEING AND FAILURE MODES OF

IGBT

Thermal Cycling and Power Cycling in High Temperature

Thermal stresses have two origins in power electronics. The

first origin is power cycling, obtained when using converters

subject to load variations due to mission profiles that induce

loss variations in the power devices. The second origin is the

thermal cycling due to the variations of the surrounding

thermal environment in which the converters are placed.

mailto:[email protected]

mailto:[email protected]




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Power converters used in transport applications typically

undergo both cycling modes, with different levels of

temperature swings and frequency. In ground or air transport

applications, converters operate under intermittent and/or

variable-load conditions that create power cycling. Thus, the

temperature range on chips is typically situated between 30 ◦C

and 60 ◦C, leading to lifet imes greater than one million cycles.

In addition, the power module can be submitted for a few

thousand cycles with ambient temperatures that can vary over

a very broad range (−40 ◦C to −55 ◦C in co ld ground zones

and 120 ◦C near engines).

Main Effects on Semiconductor Power Devices At this time,

the main effects of thermal and power cycling on IGBT power

modules have been quite well known for about ten years.

These effects are thermo mechanical and main ly concern the

layer assembly under the chips and the connections.

A. Study Goals

Different methods are investigated on the topic related to the

reliability and failure modes of power devices and the

consequences on converters and systems. A first main

direction is the analysis and modeling of ageing mechanisms

in order to estimate lifetimes or improve technologies. The

resulting works are based on thermo mechanical

considerations and are frequently sustained by the use of

fin ite-element (FE) simulation tools. These models must be

validated for any components or systems by experimental

results, which are very difficult and very time consuming to

obtain. [1]

Other considerations to be addressed are the consequences and

management of fau lts during converter operation. In this

respect, it is essential to consider redundancy options

or degraded operating modes. This concerns many

applications for which availab ility and/or safety are critical,

such as embedded power systems for human transport, which

are typical in this area. Health monitoring is another important

point of study. It is an essential step for developing efficient

approaches to predictive maintenance.

B. Context-Tested Devices

Different kinds of power-cycling tests were used on various

power modules. The orig inality of this work arises from the

particular test conditions applied to the power devices

(switching under nominal conditions and pulse width

modulation (PWM) operating mode) from high thermal stress

values (up to 90 ◦C for the base plate temperature and up to

170 ◦C on chips) and from the high number of tested samples.

The tested IGBT power modules were chosen because they

represent current technologies. The general aim is not to

characterize these specific devices in part icular, but to obtain

as many generic results as possible. Modules include three

inverter legs and use the most current assembly technology,

alumina-copper DBC, and aluminum wire bonds. Further

connection technologies have been developed and seem more

robust, but wire bonds still represent the most frequently used

technology.

Figure 1: Interio r view of the tested module.

Typical electrical ratings are 600 V and 200 A, which are

specifications in accordance with the target context, and the

maximal junction temperature is 175 ◦C. The three-phase

structure allows ageing four or six IGBTs simultaneously.

C. Connections-Metallization-Acceleration Factor

Conversely to the results usually provided by power cycling in

long term cycle operations, the ageing modes here mainly

concern the connections (wire bonds) and metallization, while

the DBC and solders are lightly damaged by the hardest

cycles.[11]

The following damage modes were observed on the wire

bonds

1) Heel crack and fractures (mechanical constraints in the

wires and fatigue phenomenon due to the deformation

related to temperature swings);

2) W ire bond liftoff (mechanical stress on Al–Si joints due to

the difference in the thermal expansion coefficient between Al

and Si);

3) Metallurgic damage due to the thermo mechanical stress on

aluminum resulting in part from the difference in the thermal

expansion coefficient between Al and Si.

The previous order gives the predominant mode appearing in

the different protocols from the least to the most severe stress.




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Figure 2: Examples of bonding damages. (a) Metallurg ic

damage. (b) Heel crack. (c) Fracture. (d) Liftoff.

Figure 3: Examples of bonding damage cartographies.

3. PRESENT MAINTAINS SYSTEM

Figure 4: Maintains System

Maintenance management is a systematic process. Here all

activities are involved in keeping a system working. Fig.

shows the structure of maintenance management.

Work identification: - It is the first step of maintenance work

cycle. It is necessary to know on which equipment

maintenance has to be done. For this maintenance manager

first sets some criteria to select the equipment. Depending

upon those criteria , equipment can be selected.

Prioritising: - Maintenance manager should develop a prio rity

system, which will be applied to all p lanned and scheduled

work according to the importance of the job. The main

objective of priority system is to ensure that the most needed

work orders are scheduled first. It will also help to allocate the

resources in suitable proportion. (Lawrence Mann, 1982)

Planning : - “Maintenance planning is the administrative

preparation of selected major jobs in advance so that upon

execution they can be completed more efficiently”

(Tomlingson, 1993) Planning determines the resource

requirements such as labour, materials etc. for each job. It

helps to do the work more efficiently and with less downtime.

Scheduling: - It is the next step of planning. It determines the

best time to do the job so that there will be least interruption of

operations. It must consider the job prio rity, resources and

labour etc before scheduling any job. It also helps to use the

maintenance resources effectively. In other words good

planning and scheduling improves the quality of work.




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Reporting : - Here all the activit ies are recorded and are

closely monitored. Then report is made for evaluation and to

determine further improvements that are necessary.

Evaluation: - In this step the evaluation of maintenance

activities are done. Then if necessary maintenance manager

can make changes such as selection of another maintenance

policy etc.

D. Comparison for Preventive & Predictive Maintains

Figure 5: Comparison of Breakdown, preventive and

predictive maintenance policies

In Predictive Maintenance, inspection depends upon the

condition of machine. This is the main difference between

preventive maintenance and predictive maintenance.

Preventive maintenance works on time-scheduled basis

regardless of machine conditions while predictive maintenance

works on condition-based in which inspection is carried out

after checking the machine condition. It uses the diagnostic

equipment for checking the machine condit ion. When

indicator reaches a specified level, work is undertaken to

repair or replace the part. It means that part is repaired only

when diagnostic equipment gives the proof that part is not

working to its standard. In predictive maintenance some

special sensor devices are connected to machines to detect

changes in the different parameter like temperature, v ibration

etc related with the normal operation of the machines. These

devices generate the signals. When some of these values are

out of control limit, it is possible to predict the number of

hours expected for the machine to perform properly, before the

predicted breakdown. This continuous analysis helps the

maintenance person to check the equipment condition to avoid

the failu re to happen.

Implementing pred ictive maintenance is a long process. It

includes many steps, [5]

Monitoring the parameter: The first thing is to decide

which parameters have to be monitored, hence all the

critical parameters have to be listed.

A schedule of the activities: After deciding the

parameters, then it is necessary to decide the schedule

of activit ies, which act ivities are to be carried out first

etc.

Diagnostics to identify problems: Pred ictive

maintenance uses the diagnostic equipment to

monitor the parameters.

A record of monitored values: Diagnostic equipment

generates the signals and these signals have to be

converted into values.

Data analysis and corrective actions: It is important to

analyse the data (values) so that if any value is going

out of control limit, maintenance person can take the

corrective action to correct the problem.

4. SMART PROGNOSTICS AGENTS

For different industries or sectors, the performance of

machines or processes degrades due to aging and wear, which

decreases their reliab ility and increases the potential of failure

and downtime. On the other hand, highest possible quality of

products and services is indispensable for attain ing or

retaining market domination. Therefore, to achieve near-zero

downtime and optimal quality of products and services,

prognostics is increasingly needed to predict future failures.

And the proactive Predict and Prevent (PAP) maintenance

paradigm will consequently replace the currently prevalent

Fail and Fix (FAF) parad igm, which reactively addresses and

fixes failures once they occur.

For different industries or sectors, the performance of

machines or processes degrades due to aging and wear, which

decreases their reliab ility and increases the potential of failure

and downtime. On the other hand, highest possible quality of

products and services is indispensable for attain ing or

retaining market domination. Therefore, to achieve near-zero

downtime and optimal quality of products and services,

prognostics is increasingly needed to predict future failures.

And the proactive Predict and Prevent (PAP) maintenance

paradigm will consequently replace the currently prevalent

Fail and Fix (FAF) parad igm, which reactively addresses and

fixes failures once they occur.

E. Watchdog Agent for multi-sensor performance

assessment & prediction

The Watchdog Agent bases its degradation assessment on the

readings from multiple sensors that measure critical propert ies

of the process, or machinery that is being considered. It is

expected that the degradation process will alter the sensor

readings that are being fed into the Watchdog Agent, and thus

enable it to assess and quantify the degradation through




6

quantitatively describing the corresponding change of sensor

signatures. In addition, a model of the process or piece of

equipment that is being considered, available application

specific knowledge, or prior h istorical records of equipment

behavior can be used to aid the degradation process

description, provided that such a model, expert knowledge or

historical records exist. The prognostic function of the

Watchdog is realized through trending and modeling of the

dynamics of the observed process performance signatures

and/or model parameters. This allows one to predict the future

behavior of these patterns and thus forecast the behavior of the

process, or piece of machinery that is being considered.

Furthermore, the Watchdog Agent also has the diagnostic

capabilit ies through memorizing the significant signature

patterns in order to recognize situations that have been

observed in the past, or be aware of the situation that was

never observed before. Thus, the Watchdog Agent has

elements of intelligent behavior that enable it to answer the

questions: When the observed process, or equipment is going

to fail, or degrade to the point when its performance becomes

unacceptable. Why the performance of the observed process,

or equipment is degrading, or in other words, what is the cause

of the observed process or machinery degradation. The answer

to the first question enables the prognostic Watchdog function

and the answer to the second question enables its diagnostic

function. Thus, in essence, the functionality of the Watchdog

Agent can be summarized in the following three tasks:

† Quantitative mult i-sensor assessment of performance

degradation.

† Forecasting of performance degradation.

† Diagnosis of the reasons of the current or predicted

performance degradation. The prognostic and diagnostic

outputs of Watchdogs mounted on all the processes and

machinery of interest can then be fed into a decision support

tool (DST) that addresses the question:

† What is the most crit ical object or process in the system with

respect to maintenance, or repair. The answer to this question

is obtained through taking into account the risks of taking, or

not taking the maintenance action at a given t ime, and then

optimizing the costs associated with the maintenance

operation if the decision to perform maintenance is made, or

the cost of downtime and repair if the maintenance is omitted

and the process or machine fails. Thus, the output of the DST

module is an optimal maintenance policy for a number of

objects in the system. Those objects are tradit ionally processes

and/or equipment, and the system could be a manufacturing

line, or a p lant. However, there is no reason why an object

could not be a hardware, or software component of a vehicle,

and the associated system can be the vehicle itself, or the

population of similar vehicles in the field .Therefore, the

operation of an IMS can be summarized in answering the

previously described ‘when’, ‘why’ and ‘what’ questions in

order to postulate an optimal maintenance/ repair set of

decisions that facilitate an optimal set of maintenance/repair

actions that enable near zero downtime of the

production/service system and maximizes the cost benefits of

the predictive machine-level informat ion. Such a system of

Watchdogs integrated by a DST thus enables maintenance that

is in the same t ime condition based, as well as predictive and

proactive. Furthermore, as indicated in Fig. , information

about current and predicted performance degradation of

components in a product is indispensable in assessing

remain ing life of those components and possibility of their

cost-effective and safe disassembly and reuse in other

products.

Figure 6: Schematic representation of an intelligent

maintenance system

Schemat ic representation of the structure of IMS operations

and the use of predictive performance-related informat ion is

illustrated in Fig. The three main functionalit ies of the

Watchdog Agent are accomplished through several functional

modules, as schematically illustrated in Fig. 3. Assessment of

performance degradation is accomplished through a module

performing processing of multiple sensory inputs and

extraction of features relevant to description of product’s

performance, fo llowed by a module executing the

multisensory performance assessment based on the extracted

signal features, with the multi-sensor assessment component

being realized through the feature- level, or decision-level

sensor fusion, as defined by the Joint Directors of Laboratories

(JDL) standard of mult i-sensor data fusion . Performance

prediction function is realized through extract ion of

performance-related features and application of tools capable

of capturing dynamic behavior of the extracted performance-

related features and extrapolating it over time in order to




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predict their future behavior and future behavior of the

underlying process.

Finally, performance diagnosis function of the Watchdog is

realized through matching of the currently ext racted or

predicted performance-related features with signatures

describing different modes (healthy or faulty) of process

behavior. Thus, condition diagnosis is a step relevant to both

the health assessment functionality (pertaining to the present

performance of the monitored process of piece of Equipment)

and the performance forecasting functionality (pertain ing to

the predicted performance of the monitored process of piece of

equipment). The mult i-sensor performance assessment,

diagnosis and prediction functionalities of the Watchdog

Agent could be even further enhanced if Watchdog Agents

mounted on identical products operating under similar

conditions could exchange informat ion and thus assist each

other in building the world model. Furthermore, this

communicat ion can be used to benchmark the performance of

‘brother-products’ and thus rapidly and efficiently identify

under-performing units before they cause any serious damage

and losses.

This paradigm of communication and benchmarking between

identical products operating in similar conditions is referred to

as the ‘peer-to-peer’ (P2P) paradigm. Furthermore, as

mentioned earlier, engineering mode of the monitored process,

application specific expert knowledge about the process as

well as historical records of process behavior over t ime can be

utilized to improve all functional modules of the Watchdog

Agent (sensory signal [12] processing and feature extraction,

health assessment, condition diagnosis and performance

prediction). One can now easily observe a parallel between the

Watchdog Agent structures illustrated in Fig. 3 with the well-

known open system architecture for condition-based

maintenance (OSA-CBM) standard, according to which a

typical CBM system consists of the following seven layers:

† Sensor module

† Signal p rocessing

† Condition monitoring

† Health assessment

† Prognostics

† Decision-making support

† Presentation

Figure 7: Flow chart Functionality of the Watchdog

5. EXAMPLES FOR WATCHDOG

AGENT

F. Signal processing & features watchdog agent

Complex nature of a number of today’s products necessitates

that in order to describe their performance, the relevant sensor

readings first need to be transformed into domains that are

most informative o f product’s performance. Time-series

analysis or frequency domain analysis could be used to

process stationary signals (signals with time invariant

frequency content), while wavelet, or joint time–frequency

domains could be used to describe non-stationary signals

(signals with time-varying frequency content). Fig. Depicts

inadequacy of applying a stationary signal processing

technique, such as Fourier Transform to non-stationary signals

such as simple frequency-hopping signals shown in Fig. .

Fourier analysis is able to discern three sinusoids present in

the signal, but is unable to deduce when each one of those

sinusoids occurred. Therefore, when the order o f sinusoids is

altered, the Fourier analysis is unable to detect this change, as

indicated in the figure. On the other hand, a powerful non-

stationary signal analysis tool such as the binomial joint t ime–

frequency distribution (TFD) readily reveals this change, as

indicated in Fig. Most real life signals, such as speech, music,

machine tool vibration, acoustic mission, etc.




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Figure 8: TFD Signal

G. Welding electrode wear out is depicted in decreasing

trend of performance confidence values

Figure 9: Welding Electrode Performance

Watchdog Agents assess and predict the process or equipment

performance based on the inputs from the sensors mounted on

it. Performance-related informat ion is extracted from multip le

sensor inputs through signal processing, feature extraction and

sensor fusion techniques. Historical behavior of process

signatures is utilized to pred ict their behavior and thus forecast




Page 59

the process or machine performance. Based on the forecasted

performance, proactive maintenance can be facilitated through

the prediction of potential failure before it occurs.

Furthermore, this proactive maintenance infrastructure can be

supported by the information learnt at Watchdog and this Peer

to Peer (P2P) parad igm will be utilized to improve diagnos tic

and forecasting functionalities of the Watchdog.

H. Through fault current (TFC) monitoring system [15]

Figure 10: Architecture of TFC monitoring system

The Architecture of TFC Monitoring System

The architecture of TFC monitoring system can be seen in Fig.

10. This system consists of three main components. There are

TFC logger, PC concentrator and server. Fig 10, Architecture

of TFC monitoring system

TFC Logger: TFC logger is a device mounted on the

secondary side of distribution transformer to monitor the

current flowing through that transformer. When the current

exceeds the setting value (transformer overload current) the

logger will record the fau lt current and duration, and then

transmit them to the PC concentrator via wireless

communicat ion.

PC Concentrator:

PC concentrator is used to collect and pass the data to the

server. By using TFC driver installed on the PC concentrator,

the users can change the instrument settings and access the

data in TFC logger.

Server: On the server, there is a web-based database

application. These servers collect all data TFC and then

predict the transformer remain ing withstands capability. This

server is equipped with an early warn ing system that can send

a short message when the cumulat ive TFC reach a limit value.

This setting limit is adjustable.

6. CONCLUSION

Using the historical data, achieve and sustain near-zero

breakdown performance & develop "predict and prevent"

methodology. The parametric model presented relies solely on

basic chronological failure data and the assumption of

modeling of the time to failure o f installed units. Using the

historical data, we are able to estimate the parameters of the

model and form it to pred ict future failures. The parametric

model can be used to forecast how actions in the present will

import overall failure t rends.

Due to this method we can save the time & also failure of

equipment so cost of maintains also reduced. But requirement

is for that is previous data of equipments required.

REFERENCES

[1] Francois Forest, Member, IEEE Ageing and Failure

Modes of IGBT Modules in High-Temperature Power

Cycling, IEEE TRANSACTIONS ON INDUS TRIAL

ELECTRONICS, VOL. 58, NO. 10, OCTOBER 2011.

[2] Terrance D. Nielsen, Member, IEEE, “Improving

Outage Restoration Efforts Using Rule-Based Prediction

and Advanced Analysis”, IEEE/PES , Page: 866, 2002.

[3] Bach Quoc Khanh, Dong-Jun Won, Member, IEEE,

and Seung-Il Moon, Member, IEEE “Fault Distribution

Modeling Using Stochastic Bivariate Models for Prediction

of Voltage Sag in Distribution Systems”, IEEE,0885-8977,

Page: 347,2007

[4] Anil Pahwa Shalini Gupta Richard E. Brown,” Data

Needs For Reliability Assessment Of Distribution

Systems”, IEEE, 0-7803-7519-X, Page:783, 2002

[5] Rohit Moghe, Student Member, IEEE, Mirrasoul J.

Mousavi, Member, IEEE, “Trend Analysis Techniques for

Incipient Fault Prediction” IEEE 978-1-4244-4241-6,

2009

[6] Jawa Bali West Jawa Regional Offices – Maintenance

Department. Through Fault Current Monitoring System

to Predict the Degradation of Transformer Withstand

Capability.2011 International Conference on Electrical

Engineering and Informatics17-19 July 2011, Bandung,

Indonesia.

[7] WEI LI , XUE-LING SONG,ELECTRICAL

EQUIPMENT FAULT MONITORING S YSTEM BAS ED

ON TEMPERATURE CHANGE INFORMATION

FUS ION TECHNOLOGY, Proceedings of the 2011

International Conference on Machine Learning and

Cybernetics, Guilin, 10-13 July, 2011

[8] Ra´ul Guanche∗ Component Failure Simulation Tool

for Optimal Electrical Configuration and Repair Strategy

Design of Off-Shore Wind Farms.

[9] U.S. Army Research Lab 2800 Powder Mill Road

Adelphi, MD 2078, A Fault Detection and Protection




Page 60

Scheme for Three-Level DC-DC Converters Based on

Monitoring Flying Capacitor Voltage .

[10] Jay Lee, Ohio Eminent Scholar and L.W. Scott Alter

Chair Professor Univ. of Cincinnati, Intelligent

Maintenance S ystems (IMS ), www.imscenter.net

[11] Francois Forest, Member, IEEE Ageing and Failure

Modes of IGBT Modules in High-Temperature Power

Cycling, IEEE TRANSACTIONS ON INDUS TRIAL

ELECTRONICS, VOL. 58, NO. 10, OCTOBER 2011.

[12] Terrance D. Nielsen, Member, IEEE, “Improving

Outage Restoration Efforts Using Rule-Based Prediction

and Advanced Analysis”, IEEE/PES, Page: 866, 2002.

[13] Bach Quoc Khanh, Dong -Jun Won, Member, IEEE,

and Seung-Il Moon, Member, IEEE“Fault Distribution

Modeling Using Stochastic Bivariate Models for Prediction

of Voltage Sag in Distribution Systems”, IEEE,0885-8977,

Page: 347,2007

[14] Jay Lee, Ohio Eminent Scholar and L.W. Scott Alter

Chair Professor Univ. of Cincinnati, Intelligent

Maintenance S ystems (IMS ), www.imscenter.net

[15] “Through fault monitoring system predict the

degradation of transformer withstand capability “

H.Muhlis, M.N. Nugraha, H. Maryano , H.I. Septiyono,

2011 International conference Electrical Engineering

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