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System Monitoring

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S Y S T E M M O N I T O R I N G The use of machine learning techniques in structural and motor monitoring…
I N T R O D U C T I O N ,[object Object],[object Object],[object Object],[object Object],Pattern Recognition
Structural Health Monitoring ,[object Object],[object Object],[object Object],[object Object],[object Object],sensor
Fault Detection In Motor Measurement of current , voltage… ,[object Object],[object Object]
From Sensor Data to the Training set ,[object Object],[object Object],[object Object],[object Object],Time x 1 ………x n y 1 …….y m … ...…………… … ...…………… … ...…………… … ...…………… … ...…………… Feature Extraction
Feature Extraction in the time-domain ,[object Object],[object Object],[object Object],Known signal Unknown sensor signal Area Between the 2 Root Mean Square of each curve Root Mean Square of the difference Correlation Coefficient x 1 x n Input vector
Switching from time to frequency Domain ,[object Object],[object Object],[object Object],Time Fourier Transformation x 1 x n Input vector Principal Component Analysis
Machine Learning techniques ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Feed Forward Neural Networks 1 1 Input Layer Hidden Layer Output Layer x 1 x n Input vector y 1 y m Output vector The size of the network is usually small: Input vector: 3-20 nodes
Back-Propagation algorithm ,[object Object],Step1: Initialize weights. Step2: Present Inputs vector and desired outputs Present training vectors from the training set to the network; calculate the output of every node by propagating inputs through the network using the activation function selected (sigmoid, step…) Step3: Update Weights Adapt weight starting at the output nodes and working back to the first hidden layer by: Wij(t+1)=Wij(t) + ηδjX’i δj = yj(1-yj)(oj-yj) for output node δj=X’j (1-X’j) Σ δk Wjk for hidden node Step4: Repeat by going to step 2 until weights do not change.
Real-world distribution. 0 for class A 1 for class B t=50 t=100 t=150 t=200 Training Architecture of the network Back-Propagation algorithm B A
Kohonen Self Organizing Map (SOM) ,[object Object],[object Object],Thanks to its clustering ability, Kohonen networks are used in system monitoring, to perform a preliminary organization of the input space. Because it is an unsupervised learning technique, it needs to be associated with another intelligent tool.
Kohonen Self Organizing Map (SOM) x 1 x n Input vector To each node of the network is associated a vector in the input space mi 1 mi n When the input vector is presented to the map, its distance to the weight vector of each node is computed. The map returns the closest node which is called the Best Matching Unit. BMU The output of the map is usually sent to another learning machine which will finish the process of pattern recognition.
Kohonen SOM training ,[object Object],Step1: Initialize weights (randomly or with sample from the input space) Step2: Update each node in the map in proportion with the distance from its weight vector to the input vector: mi = mi + η(t) * hci(t) * [x(t)-mi(t)] Where: mi is the weight vector of the ith node η (t) is the learning rate h ci (t) is the neighborhood function (the more a node is far from the BMU the smaller value is returned by this function)
Kohonen SOM training (example) 10x10 Kohonen Map X 1 X2 X3 Y … ...…………… … ...…………… … ...…………… 0 1 33 Training Set Where Y is the number of short-circuit terms and x1,x2,x3 are the amplitude of specific frequency in the spectrum
Kohonen SOM training (example) 10x10 Kohonen Map 0 4 5 6 7 8 9 10 11 13 15 18 19 21 25 23 28 29 29 31 33 X 1 X2 X3 Y … ...…………… … ...…………… … ...…………… 0 1 33 Training Set
Results ,[object Object],[object Object],[object Object],[object Object],Monitoring systems are often composed of several networks.
Conclusion Despite good results more research needs to be done in system monitoring especially in the case where several faults or damages occur at the same time.

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System Monitoring

  • 1. S Y S T E M M O N I T O R I N G The use of machine learning techniques in structural and motor monitoring…
  • 2.
  • 3.
  • 4.
  • 5.
  • 6.
  • 7.
  • 8.
  • 9. Feed Forward Neural Networks 1 1 Input Layer Hidden Layer Output Layer x 1 x n Input vector y 1 y m Output vector The size of the network is usually small: Input vector: 3-20 nodes
  • 10.
  • 11. Real-world distribution. 0 for class A 1 for class B t=50 t=100 t=150 t=200 Training Architecture of the network Back-Propagation algorithm B A
  • 12.
  • 13. Kohonen Self Organizing Map (SOM) x 1 x n Input vector To each node of the network is associated a vector in the input space mi 1 mi n When the input vector is presented to the map, its distance to the weight vector of each node is computed. The map returns the closest node which is called the Best Matching Unit. BMU The output of the map is usually sent to another learning machine which will finish the process of pattern recognition.
  • 14.
  • 15. Kohonen SOM training (example) 10x10 Kohonen Map X 1 X2 X3 Y … ...…………… … ...…………… … ...…………… 0 1 33 Training Set Where Y is the number of short-circuit terms and x1,x2,x3 are the amplitude of specific frequency in the spectrum
  • 16. Kohonen SOM training (example) 10x10 Kohonen Map 0 4 5 6 7 8 9 10 11 13 15 18 19 21 25 23 28 29 29 31 33 X 1 X2 X3 Y … ...…………… … ...…………… … ...…………… 0 1 33 Training Set
  • 17.
  • 18. Conclusion Despite good results more research needs to be done in system monitoring especially in the case where several faults or damages occur at the same time.