1 / 36

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

gauri
Télécharger la présentation

Fault Prediction in Electrical Valves Using Temporal Kohonen Maps

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. 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

  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. 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

  8. MAINTENANCE SCHEME

  9. 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

  10. MAINTENANCE SCHEME Mathematical model Temporal Kohonen maps Wavelet packet transform

  11. MATHEMATICALMODEL • Electrical actuator Main components Forces

  12. MATHEMATICALMODEL • Electrical actuator model Differential and algebraic equations Position Fault injection Torque

  13. 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

  14. 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

  15. 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

  16. 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

  17. 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

  18. 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

  19. EXPERIMENTAL RESULTS

  20. 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) 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

  22. MODEL RESULTS • Fault simulation Torque Position

  23. CLASSIFICATION RESULTS • Fault classification map of faults in KR, KM and Ca

  24. CLASSIFICATION RESULTS • Fault classification map of faults in KR, KM and Ca

  25. CLASSIFICATION RESULTS • Fault classification map of faults in KR, KM and Ca

  26. CLASSIFICATION RESULTS • Fault classification map of faults in KR, KM and Ca

  27. 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

  28. PREDICTION RESULTS • Fault prediction map of faults in KR

  29. PREDICTION RESULTS • Fault prediction map of faults in KM

  30. PREDICTION RESULTS • Fault prediction map of faults in Ca

  31. 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

  32. CONCLUSION

  33. 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

  36. Thank you! luizfg@ece.ufrgs.br

More Related