machine condition monitoring by verhaert

20.10.2006

Machine condition monitoring Machine condition monitoring

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Machine condition monitoring

Dany Robberecht

[email protected]

www.verhaert.com

VERHAERTINNOVATIONDAY – OCTOBER 20th, 2006

www.mastersininnovation.com

Commercially confidence – This presentation contains ideas and information which are proprietary of VERHAERT, Masters in Innovation®*, it is given in confidence. You are authorized to open and view the electronic copy of this document and to print a single copy. Otherwise, the material may not in whole or in part be copied, stored electronically or communicated to third parties without prior agreement of VERHAERT, Masters in Innovation®*.

* VERHAERT, Masters in Innovation is a registered trade name of Verhaert Consultancies N.V.

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The challenge of future maintenance programs

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The Maintenance Paradigm - general

The problem of operating potentially damaged equipmentLost efficiency through downtime and ineffective performanceCost of precautionary replacement Cost of repair after catastrophic failure Cost of precautionary spares inventoryCost of recovery to normal operation after failureUncertain yield and quality

Condition monitored maintenance programs envisage toImprove downtimeIncreased effectivenessReduced maintenance costReduced spares inventory

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Approaches towards maintenance

What maintenance mix does your organisation have?Is there a difference in process control and our equipment we manufacture?How good earning-generating capabilities has your mix?

Preventative

MaintenanceCorrective

Maintenance

Predictive

Maintenance

Condition Monitoring

Detective

Maintenance

On-site Diagnose

Remote Diagnose

Spare parts prorgrams

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The challenge for R&D

Need to predict equipment failureFrequency of monitoring is the lead time to failure (PF interval)Interval is variable (type of failure, lubrification, ...)Estimates most of the time conservative, but why can we all easily quote many undetected errors?

Need for an hollistic view of equipment conditionNeed for greater accuracy in failure predictionNeed to reduce the cost of condition monitoringNeed to improve equipment and process reliability (prolonge MTBF)

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Global Industry report Jan 06

Machine condition monitoring todayVibration analysisThermographyLubrication / oil analysisMotor current monitoringCorrosion monitoring

Growing need for continuous monitoring equipment

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Beyond conventional sensors

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Signal processing for machine monitoring

a) Time-frequency data analysis or time-scale data processing have been investigated

These techniques enable the detection of incipient faults– computationally demanding – difficult to identify data features for automated and online condition monitoring from this

representation

b) Higher order statistics are a relatively new tool in the area of data processing (turbo-pump, rolling element bearing and electric motor faults)

– high computational overhead– only effective only for a limited range of faults.

c) independent component analysis is difficult to interpret physically

> Techniques require an expert and hardly give an early warning indicator of wear!

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The power of neural networks

Neural networks have the ability to learn, to form a model from their training data alone

Storing information as patternsUse those patterns and then solve problems

Used inSignature analysisInvestment analysisProcess control

Where can neural networks be used in?Where we can’t formulate an algorithmic solutionWhere we can get lots of examples of the behavior we requireWhere we need to pick out the structure from existing data

Neural Network Technology falls under the umbrella of ArtificialIntelligence (AI)

AI suffered from overhype and inability to deliver

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AI for machine condition monitoring

Technology developed at QinetiQ ltd early 80’sExperiments with different AI technologySingle neuron layer networks provided good results in military applicationsQQ developed AI with tech transfer opportunity to an early warning anomaly detection system to predict wear and failure of equipment

Condition of machine: failure & faultSeverity of faultTime to failure

Patented technology

Malvern Technology CentreCentre for Signal &

Information Processing

St. Andrews RoadMalvern, Worcestershire

WR14 3LGwww.qinetiq.com

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Concept of the technology

Componential coding is an innovative approachUnsupervisedAdaptiveIt derives the features of the data based on training (no labeling of known faults, nor pre-processing to enhance known fault characteristics)

High fidelity, accurate modeling of the actual signal behavior leads to more sensitive and robust indicator = it allows to detect changes in the deep structure of the signal

Automatic alignment of the model to multi-dimensional datasets (convolution techniques)

Universal and rapidly applicable to any piece of equipment

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QinetiQ’s technology and others

Componential coding provides benefits over more familiar techniquesCompared to Principal Component Analysis(Statistical techniques -> averaging technique, blurs the signal)– Fine resolution (Impulsive noise of a bearing) - Better for subtle changes– Very good to detect intermittent faults– High fidelity, because it models the actual signal

Compared to RBF– No expert required to design the signal features (quality of the expert)– No bias/assumptions (typically build into expert derived features)– It does more than classification into healthy and non-healthyCompared to multi-layered networks– Less complex, able to deliver results

When is it particularly suited– When no machine-specific tailored techniques have been developed– When no previous monitoring experience is available

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Proof-of-technology

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Technology background

Prior application: Military (MOD)Little prior information on characteristics of target

Further research outlined technology transfer opportunitiesIn-house research to detect wear of mechanical equipmentCustomer funding to detect anomalies of an electric motor

The technology was benchmarked by the University of ManchesterOutperformed other techniques

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An independent benchmarkGoal of comparative study – electrical motor

Detect robustly produced anomalies in measurements from a range of sensors detecting electrical current, vibration, etc.No engineering model available

QinetiQ’s Network did not need the expert knowledge to set up the technologyIt outperformed every rule based system

Anomalies detected in a novel motorindependent st udy

QinetiQ

4

5

6

7

8

9

Sentinel ICAN Translat ionvariant PCA

PCA Cepstraldif ference

Spectraldif ference

Stat ist icalanalysis

(combined RMSlevel andKurtosis)

QinetiQ’s technology was the only one that was able to detect all 9 anomalies

Industry standard

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Neural technology proven in the fieldEarly and robust detection of a trend to failure

a small oil cooler fan mounted on UK military equipmenthelicopter main gearbox

Robustly detecting faults under variable conditions in complex systems (ATM)

complex multi-component electro-mechanical system PAT test

Multiple data types and multiple failure modes with no engineering model

electric motor Identifying progressive wear states with variable loading

large naval gearbox

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Neural technology proven in the field

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11,5

22,5

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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Fault Fault starts herestarts hereHumans Humans notice notice

herehere

Single mounted accelerometer sampled over time (20sec)Identify small cooler fan in army equipment

The resultsData driven with ‘Out of the box’ settingsUsed here as trend detector and alarm signalVery sensitive, yet very low false

alarm rate

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00,10,20,30,40,50,60,70,80,9

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EEaarlyrly roburobusst t indicatindicatiionon

CatastCatastrrophicophicfailurefailure

Single mounted accelerometer sampled over timeIdentify anomalies in helicopter gearbox

The resultsData driven with ‘Out of the box’ settingsUsed here as trend detectorEliminates subjective interpretation

Gearbox behaves differently in almost each helicopter

Experts give subjective thresholds

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Fault detection accuracy and robustness

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0.150.2

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0.350.4

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0.550.6

0.650.7

0.750.8

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0 1 2 3 4 5 6 7 8 9Fault detection threshold

Prob

abilit

y

Prob(false alarm)Prob(missed detection

Decrease number of infant mortalitiesMoney jam, mechanical failuresset-up/configuration not accurate

Goals of the projectIdentify overall signatureObjective judgement it worksLocation of anomalie

Transaction time is <40 secondsVibration + acoustic signals

+10.000 samples of 4 sensors (20KHz)<0.5 seconds to translate inputs and testDistributed sensor set-up

The resultsData driven with ‘Out of the box’ settingsUsed here as alarmVery low false alarmsVery high detection rate

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How does it work?

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Detect ion/ location System capabilityforecast

Component prognosis

Condit ion indication

Sensors/ data streams

Degradation models

Usage profile

Detection/ location Component prognosis

Condit ion indication

How does it work?

• Early damage detection technology• Enables on-condition maintenance strategies by

detecting the early onset of wear and damage

• It can be supplied as a simple, easy to use and reliable MS Windows signal and information processing software library.

•

00,10,20,30,40,50,60,70,80,9

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How does it work?• It is an advanced statistical model

• Layered statistical templates• Non linear transformation of data• Robust statistics to output one simple scalar

• It is not a typical rule-based system, that means• The templates are learned• The parameters are learned• It works unsupervised and takes decisions itself• It learns from the daily operations (e.g. From faulty data

to interprete severity of faults)

• Compare it to a lossy data compression algorithm• It tranforms input data into a coded form and back again• To achieve minimum quantity of information (scalar

output) with maximum relevant information

Signal Templates

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How does its core work?

The basic principle of the modelThe system is designed not to be able to reconstruct all data sets accurately, but to measure how inaccurately each data set gets reconstructed

Ability to differentiate between data sets having different statistical characteristics

It can reconstruct a model-based replica of any sensor typeIt is designed to optimise the accuracy with which the model reconstructs the input data signal

There are more than 90 parameters in the modelValidation process optimises the settings of the model

Sensitive & robust anomalie detection through multiple comparisons

Difference with reconstructed modelVariance of the size (shape of the movement)

Time into Recording

Data model defined by neurons’ parameters, used to non -linearly transform the input data signal into the encoding neural outputs

Reconstruction of an estimated replica of the input data signal:a linear transformation of the encoding neural outputs

The current input data signal: a vector of several sample -sequences from several sensors

Data model defined by neurons’ parameters, used to non-linearly transform the input data signal into the encoding neural outputs.

-

Reconstruction of an estimated replica of the input data signal:a linear transformation of the encoding neural outputs.

The current input data signal: a vector of several sample - sequences from several sensors.-

Sensor

Cha

nnel

Model-based reconstructed estimate of the current input data signal.

Time into Recording

Data model defined by neurons’ parameters, used to non -linearly transform the input data signal into the encoding neural outputs

Reconstruction of an estimated replica of the input data signal:a linear transformation of the encoding neural outputs

The current input data signal: a vector of several sample -sequences from several sensors

Data model defined by neurons’ parameters, used to non-linearly transform the input data signal into the encoding neural outputs.

-

Reconstruction of an estimated replica of the input data signal:a linear transformation of the encoding neural outputs.

The current input data signal: a vector of several sample - sequences from several sensors.-

Sensor

Cha

nnel

Model-based reconstructed estimate of the current input data signal.

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How does its core work?

The reconstruction modelSingle layer of neurons, each of which uses the same input vectorEach neuron calculates a different neural output which is a function of both the input vector and neural parameters, whose values/settings differ from neuron to neuronNon linear transformation of the input signals is analogous to the way Fourrier analysis linearly transforms an input vector into Fourrier coefficients or that principal component analysis linearly transforms inputs into a set of coefficients (projections on the basis set of the eigenvectors of the covariance matrix)Robust statistics for reliable output (proprietary information, not disclosed)

Patent InformationFiled: 17.02.2000Title: Signal processing technique

PublicationsWO0161526 (A3)WO0161526 (A2)US6954721 (B2)

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What sensor inputs can be used?

The technology resolves some fundamental sensor challengesNoiseTypes of sensorsLocation of sensors

The neural network technology has documented performance withMicrophonesAccelerometersSearch coils (Magnetic flux)Strain gaugesCurrent (Voltages)

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What kind of output does it provide?

Robust statistics to output one simple scalarDicrimination factor

Scalar can indicatePresent general condition – anomalie identificationSeverity of faultTime-to-critical-failureFailurePhysical location of faultWhat caused the failure

Scalar can be normalised to customer preferenceStandard: 0 – 1PercentageGraph – Diagram

From real time continuous monitoring to monitoring on request

Capability assuredCapability com

promised

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What are the typical modes available?

A Simple Application Programmers InterfaceDefine Data

Where to store my dataConfigure mode

search parameters learn the settings (what it should be)

Build modelearn statistical templates for actual machine Learn typical faults

Detect mode Operational monitoring mode

Report modeConfigure output

report (void)void

detect (void)void

build (void)void

configure (void)void

defineData (void)void

CsentinelAE (const string &init_working_folder)

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Is it difficult to configure?

Simple XML -configuration

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How do you train and calibrate the model?Training Process

Run down healthy data setsKnown configuration of sensorsNominal operating conditions

The model minimise the mean-squared-error (MSE)Validation ProcessMeasure how closely the model matches with new ‘unseen’ healthy data (collect under the same conditions as training data)The MSE of the new data set provides a natural variability of that machine

Make the system more robust with ‘faulty’ data

Faulty data is not to train the model and not to look for a particular faultStabilise the process - learn from a particular fault

If particular failure modes are known, then specific data on them can be added

Use different sensors to characterise input

Training Phase

Iterate to minimise error

Adapt the model parameters to reduce the mean-squared model-error for the training data-set as far as possible.

Validation Phase

Monitoring Phase

For the trained values of the model parameters, calculate the discrimination index for the data-set(s) to be monitored.

For the current values of the model parameters, measure the mean-squared model-error for the training data-set.

For the trained values of the model parameters, measure the mean-squared model-error for the validation data-set.

For the trained values of the model parameters, measure the mean-squared model-error for the data-set(s) to be monitored.

Optional: check statistical significance of discrimination index by repeating previous step (left) with different validation data-sets.

> Repeat this process whenever necessary

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What processing footprint would it require?This data has been estimated from previous applications

TimingsScale your timing to your applicable processing platformTo analyse the health of a new machine would take in the order of 0.14s.The timings are for a PC with in an Intel Pentium 4 CPU 2.80GHz processor and 512 MB of RAM. A machine of this specification is not required but slower machines would obviously affect speed.

Memory usageThe technology used approximately 7.5Mbytes of RAM during these experiments. The use of memory has not been optimized for this application.

389M0.139Testing

554M0.198Validation

26.26G9.38Learning

Mean clock cycles per transaction

Mean time per transaction (s)

ModeElapse times for processing

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Can you use or make it smarter?Predict the time-to-failure

Physics of failure model – FMECAData on usage

Predict the severity of an errorSimply run several faulty data sets to understand discrimination indexDifferentiation between different faulty data sets is the basis for severity discrimination

Integrate an array of sensors: e.g. Improve fault recognitionCriticality of sensorsCost of sensors (more, but low cost components)Accessibility of component

Filter out spatial noise factor

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Roadmap to implementation

Outputs

Installation Feasibility

• FMECA - Failure modes effect and criticality analysis

• CSA - Condition sensor analysis

FMECA Critical components

CSA Sensor specific-cation

Agreement Annual licence, Service level agreement

Report Suitability and performance

Systems analysis•FMECA1

•CSA2

Trials •Data collection

•Configuration

•Performance assessment

Operation

AcceptanceTest report

Installation •Sensors,

•Communications

•Host

•Software

•Acceptance tests

Operation Upgrades Sustained support Consultancy

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Why is it different?

It is easy to use for health detector on EVERY system

It easy monitors complex systems (where expert analysis takes too long)

It easy monitors newly systems (where no or few expert data / knowledge)

It can be tuned as an expert system by adding expert knowledge or data in the setup phase

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Industrial areas of interest

Learn a particular failure mode (engineering)Array of sensors on specific locationUse different type of sensorsUse different networks to isolate a failure location

Test toolFactory acceptance test (general condition)Highly accelerated life test or HALT (onderling vgl geteste systemen)Objectieve test maintenance personeel (instinctieve test!)

Real time continuous test mode of field equipmentTest for critical faultsOn-site test to improve management of time-intervalsOnline processing monitoring

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Verhaert New Products & Services nvHogenakkerhoekstraat 219150 KruibekeBelgiumTel +32 (0)3 250 19 00Fax +32 (0)3 254 10 [email protected]

www.mastersininnovation.com

machine condition monitoring by verhaert

Technology

machine monitoring

online condition

maintenance mix

damaged equipment

failure pfintervalinterval

failure predictionneed

timescale data processing

lead time