TY - GEN
T1 - Visualization of induction machine fault detection using self-organizing map and support vector machine
AU - Wu, Sitao
AU - Chow, Tommy W. S.
AU - Huang, Di
PY - 2006
Y1 - 2006
N2 - Induction machines play an important role in today's industries. How to monitoring, detection, classification, and diagnosis of induction machine faults have been the essential problems. Although there have been many methods proposed to deal with these problems, there is lack of visualization tool for understanding the problems more easily. In this paper, a visualization method is proposed to help users understand the mechanism of induction machine fault detection in a transparent way. Furthermore, user can also tell the status (normal or faulty) just directly from the visualization results. The visualization is implemented by hybridizing two neural networks: self-organizing map and support vector machine. Experimental results demonstrate the novelty and effectiveness of the proposed visualization method used for induction machine fault detection.
AB - Induction machines play an important role in today's industries. How to monitoring, detection, classification, and diagnosis of induction machine faults have been the essential problems. Although there have been many methods proposed to deal with these problems, there is lack of visualization tool for understanding the problems more easily. In this paper, a visualization method is proposed to help users understand the mechanism of induction machine fault detection in a transparent way. Furthermore, user can also tell the status (normal or faulty) just directly from the visualization results. The visualization is implemented by hybridizing two neural networks: self-organizing map and support vector machine. Experimental results demonstrate the novelty and effectiveness of the proposed visualization method used for induction machine fault detection.
KW - Self-organizing map (SOM)
KW - Support vector machines (SVMs)
KW - SVM visualization (SVMV)
KW - Visualization
UR - http://www.scopus.com/inward/record.url?scp=84864916906&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-84864916906&origin=recordpage
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 9781932415957
VL - 1
SP - 138
EP - 144
BT - Proceedings of the 2006 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV'06
T2 - 2006 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV'06
Y2 - 26 June 2006 through 29 June 2006
ER -