Fault Prediction in Electrical Valves Using Temporal Kohonen Maps Luiz F. Gonçalves, Eduardo L....
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Transcript of Fault Prediction in Electrical Valves Using Temporal Kohonen Maps Luiz F. Gonçalves, Eduardo L....
![Page 1: Fault Prediction in Electrical Valves Using Temporal Kohonen Maps Luiz F. Gonçalves, Eduardo L. Schneider, Jefferson L. Bosa, Renato Ventura B. Henriques,](https://reader035.fdocuments.in/reader035/viewer/2022070407/56649e4c5503460f94b4226c/html5/thumbnails/1.jpg)
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
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OUTLINE
Introduction Maintenance scheme
Mathematical model Signal processing Temporal Kohonen maps
Experimental results Conclusions
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INTRODUCTION
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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
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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
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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
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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
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TEMPORAL KOHONEN MAPS
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
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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 MAPS
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TEMPORAL KOHONEN MAPS
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
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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
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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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CLASSIFICATION RESULTS
Fault classification map of faults in KR, KM and Ca
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CLASSIFICATION RESULTS
Fault classification map of faults in KR, KM and Ca
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CLASSIFICATION RESULTS
Fault classification map of faults in KR, KM and Ca
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CLASSIFICATION RESULTS
Fault classification map of faults in KR, KM and Ca
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
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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
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CONCLUSIONS
The results obtained point out to a promising solution for the maintenance in electrical valves
Acknowledgements
CNPq CAPES Petrobrás