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UFRGS. 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 11 th LATW Punta del Leste - March 28-31 2010. OUTLINE. Introduction Maintenance scheme

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Fault Prediction in Electrical Valves Using Temporal Kohonen Maps

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Fault prediction in electrical valves using temporal kohonen maps

UFRGS

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


Outline

OUTLINE

  • Introduction

  • Maintenance scheme

    • Mathematical model

    • Signal processing

    • Temporal Kohonen maps

  • Experimental results

  • Conclusions


Fault prediction in electrical valves using temporal kohonen maps

INTRODUCTION


Introduction

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


Introduction1

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


Introduction2

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


Introduction3

Proactive maintenance scheme

Wavelet packet transform

&

Temporal Kohonen maps

INTRODUCTION

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

Electrical valves

Model

Signals of torque and position

Oil distribution network

Predicting the faults


Fault prediction in electrical valves using temporal kohonen maps

MAINTENANCE

SCHEME


Proactive maintenance

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

Focuses on fault prediction and diagnosis based on component lifetimes and on system on-line monitoring

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


Maintenance scheme

MAINTENANCE SCHEME

Mathematical model

Temporal Kohonen maps

Wavelet packet transform


Mathematical model

MATHEMATICALMODEL

  • Electrical actuator

Main components

Forces


Mathematical model1

MATHEMATICALMODEL

  • Electrical actuator model

Differential and algebraic equations

Position

Fault injection

Torque


Signal processing

SIGNALPROCESSING

  • 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


Self organizing maps

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

Synaptic weight vector

Energy vector


Temporal kohonen maps

TEMPORALKOHONENMAPS

  • 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:

Temporal

Activation

Euclidean

Distance


Temporal kohonen maps1

TEMPORALKOHONENMAPS

  • 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


Temporal kohonen maps2

TEMPORAL KOHONEN MAPS

  • 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


Temporal kohonen maps3

TEMPORALKOHONENMAPS

  • 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


Fault prediction in electrical valves using temporal kohonen maps

EXPERIMENTAL

RESULTS


Experimental results

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


Experimental results1

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)

KR simulates the degradation of the internal valve worm gear, till it breaks

100 operation cycles

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


Model results

MODEL RESULTS

  • Fault simulation

Torque

Position


Classification results

CLASSIFICATION RESULTS

  • Fault classification map of faults in KR, KM and Ca


Classification results1

CLASSIFICATION RESULTS

  • Fault classification map of faults in KR, KM and Ca


Classification results2

CLASSIFICATION RESULTS

  • Fault classification map of faults in KR, KM and Ca


Classification results3

CLASSIFICATION RESULTS

  • Fault classification map of faults in KR, KM and Ca


Classification results4

CLASSIFICATION RESULTS

  • Fault classification map of faults in KR, KM and Ca

Each cluster is assigned a different color

During the on-line testing phase, a winner neuron computed for a measured input vector can be easily located in this map


Prediction results

PREDICTION RESULTS

  • Fault prediction map of faults in KR


Prediction results1

PREDICTION RESULTS

  • Fault prediction map of faults in KM


Prediction results2

PREDICTION RESULTS

  • Fault prediction map of faults in Ca


Prediction results3

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


Fault prediction in electrical valves using temporal kohonen maps

CONCLUSION


Conclusions

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


Conclusions1

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


Conclusions2

CONCLUSIONS

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

  • Acknowledgements

  • CNPq

  • CAPES

  • Petrobrás


Fault prediction in electrical valves using temporal kohonen maps

Thank you!

[email protected]


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