Fault Prediction in Electrical Valves Using Temporal Kohonen Maps Luiz F. Gonçalves, Eduardo L....

Fault Prediction in Electrical Valves Using Temporal

Kohonen Maps

Luiz F. Gonçalves, Eduardo L. Schneider, Jefferson L. Bosa, Renato Ventura B. Henriques, Paulo M. Engel, Marcelo S. Lubaszewski

11th LATW

Punta del Leste - March 28-31 2010

UFRGS

2

OUTLINE

Introduction Maintenance scheme

Mathematical model Signal processing Temporal Kohonen maps

Experimental results Conclusions

3

INTRODUCTION

4

INTRODUCTION

The prediction of certain phenomena, processes or failures (or time series prediction) is particularly interesting and useful in many cases

It has been the subject of research in several areas: Medicine (saving lives) Meteorology (predicting the rain precipitation) Engineering (increasing equipment reliability) Economics (predicting changes in the stock market)

Main motivation: is the need to predict the future conditions and to understand the underlying phenomena and processes of the system under study

Building models of the system using the knowledge and information that is available

5

INTRODUCTION

Many methods for system prediction have been developed with very different approaches

Statistics: Autoregressive Autoregressive Moving Average

Neural networks: Multi-Layer Perceptrons Radial Basis Networks Self-Organizing Maps (SOM)

In the last years, models based on self-organizing maps have been raising much interest

6

INTRODUCTION

Self-organizing map algorithms perform a vector quantization of data, leading to representatives in each portion of the space

The temporal models, built from SOM such as: Temporal Kohonen maps (TKM) Merge self-organizing maps (MSOM) Recurrent self-organizing maps (RSOM)

Use a leaky integrator memory to preserve the

temporal context of the input signals

7

INTRODUCTION

In this work, a proactive maintenance scheme is proposed for fault prediction in electrical valves

Electrical valves

Model

Signals of torque and position

Predicting the faults

Oil distribution network

Proactive maintenance

scheme

Wavelet packet transform

&

Temporal Kohonen maps

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MAINTENANCE

SCHEME

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PROACTIVE MAINTENANCE

Recent advances in: Electronics Computing

Proactive ≠ corrective, preventive or predictive

To automate and integrate proactive(also know as intelligent) maintenance

tasks into embedded system

That are based either on post-failure correction or on off-line periodic

system checking

Focuses on fault prediction and diagnosis based on

component lifetimes and on system on-line monitoring

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MAINTENANCE SCHEME

Mathematical model

Wavelet packet transformTemporal Kohonen maps

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MATHEMATICAL MODEL

Electrical actuator

Main components Forces

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MATHEMATICAL MODEL

Electrical actuator model

Differential and algebraic equations

Fault injection

Position

Torque

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SIGNAL PROCESSING

Wavelet packet transform Preserves timing and spectral information Suitable for the analysis of non-stationary signals Capable of decomposing the signal in frequency bands

Energy (spectral density) Torque Position

The energy is used by the self-organizing maps

Divided into N frequency bands

The WPT runs in a PC station during the training phase

During on-line testing, the WPT shall be part of the embedded system

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SELF-ORGANIZING MAPS

SOM or Kohonen maps (class of neural networks) Unsupervised learning paradigm based on:

Competition (search the winner neuron) Cooperation (identify direct neighbors) Adaptation (update synaptic weights)

The goal of a SOM is, after trained, mapping any input data from a Rn space representation into R2 lattice-like matrix

Energy vector

Synaptic weight vector

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TEMPORAL KOHONEN MAPS

The temporal Kohonen map (TKM) Unsupervised approach for prediction derived from the SOM algorithm Uses leaky integrators to maintain the activation history of each neuron These neurons gradually loose their activity and are added to the

outputs of the other normal competitive units

These integrators, and consequently the decay of activation, are modeled through the difference equation:

Where:

EuclideanDistance

TemporalActivation

16


The internal processing of SOM and TKM algorithms can be simplified and divided in three different steps:1. Start up

2. Training

3. Recovery Winner neuron:

SOM: the neuron with the shortest distance TKM: the neuron with the highest activation

Except for the determination of the winner neurons (recovery step), all other steps of the TKM are the same as in the SOM

17

For fault prediction, in recovery step, the map is colored such that the distance between neighboring neurons can be seen

The distance is given by the difference between the synaptic weights of neighboring neurons Closer neurons will appear clustered in the map and will be assigned

the same color Different colors will denote neurons under different operation

conditions: normal, degraded or faulty

Once the winner neuron is computed for a particular input vector, E, the current system status can be identified in the colored map and, in deviated behavior, the degradation trajectory can be visualized in the map


18


In the TKM the system state can be visualized as a trajectory on the map and it is possible to follow the dynamics of the process

This trajectory is described based on the winning neurons

In a normal operation mode, the winners ought to follow a path inside the normal behavior region

When a failure occurs, the winner will deviate from the normal region

19

EXPERIMENTAL

RESULTS

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EXPERIMENTAL RESULTS

Steps to generate the results:1. Generate data (W) for normal (N), degraded (D)

and faulty (F) behavior (obtained from the model)

2. Obtain the classification map (N, D and F data) using temporal Kohonen maps

3. Generate new N, D and F data (E) for three faults

4. Obtain the prediction map for each kind of faulty

21

EXPERIMENTAL RESULTS

A lot of simulations is performed to obtain typical values of torque and opening position under N, D and F valve operation to train the fault prediction map

The fault simulation is needed to generate the F and D data (some parameters are gradually incremented)

KM deviations simulate the elasticity loss of the valve

spring along time

Ca deviations simulate an increase of friction between

the valve stem and seal

100 operation cyclesKR simulates the degradation of the

internal valve worm gear, till it breaks

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MODEL RESULTS

Fault simulation

0 10 20 30 40 50 60 70 80 90 1000

50

100

150

200

250

300

Time [s]

Tor

que

[Nm

]

Normal

Faulty

a)

• • •

•

•

•

•

•

• • • ••

•

•

•

•

• • • • •• • •

•

•

0 10 20 30 40 50 60 70 80 90 1000

10

20

30

40

50

60

70

80

90

100

Time [s]Po

sitio

n [%

]

Faulty

Normal

b)

• • • ••••

••

•• • •

••

•••• • • •

• • •

•

Torque Position

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CLASSIFICATION RESULTS

Fault classification map of faults in KR, KM and Ca

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25



26



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During the on-line testing phase, a winner neuron computed for a measured input vector can be

easily located in this map

Each cluster is assigned a

different color

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PREDICTION RESULTS

Fault prediction map of faults in KR

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PREDICTION RESULTS

Fault prediction map of faults in KM

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PREDICTION RESULTS

Fault prediction map of faults in Ca

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PREDICTION RESULTS

It can be seen in these figures, three different paths (one for each simulated fault)

The trajectories started from neurons classified as normal, passed through neurons classified as degradation, and arrived to a neuron that represents the failure

It is noteworthy that in this work, the temporal Kohonen map is just used as a visualization tool

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CONCLUSION

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CONCLUSIONS

A proactive maintenance scheme is proposed for the prediction of faults in electrical valves, used for flow control in an oil distribution network

This is the first attempt to apply a proactive maintenance methodology to this sort of valves

A implementation of temporal Kohonen maps is proposed to solve the valve maintenance problem

34

CONCLUSIONS

An system implements these maps for the prediction of faults in this valves

This technique can clearly be extended to any type of maintenance scheme including the on-line testing of heterogeneous chip with some kind of electro-mechanical systems (sensors or actuators) or other, for example

35

CONCLUSIONS

The results obtained point out to a promising solution for the maintenance in electrical valves

Acknowledgements

CNPq CAPES Petrobrás

Thank you!

[email protected]

Fault Prediction in Electrical Valves Using Temporal Kohonen Maps Luiz F. Gonçalves, Eduardo L....

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