Implementation of a New Hybrid Methodology for Fault Signal Classification Using Short -Time Fourier Transform and Support Vector Machines

Increasing the safety of a high-speed motor used in aerospace application is a critical issue. So an intelligent fault aware control methodology is highly research motivated area, which can effectively identify the early fault of a motor from its signal characteristics. The signal classification and the control strategy with a hybrid technique are proposed in this paper. This classifier can classify the original signal and the fault signal. The performance of the system is validated by applying the system to induction motor faults diagnosis. According to our experiments in BLDC motor controller results, the system has potential to serve as an intelligent fault diagnosis system in other hard real time system application. To make the system more robust we make the controller more adaptive that give the system response more reliable.

[1]  Ralf Herbrich,et al.  Learning Kernel Classifiers: Theory and Algorithms , 2001 .

[2]  Ye Zhongming,et al.  A review on induction motor online fault diagnosis , 2000, Proceedings IPEMC 2000. Third International Power Electronics and Motion Control Conference (IEEE Cat. No.00EX435).

[3]  Manfred Glesner,et al.  Support vector approaches for engine knock detection , 1999, IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339).

[4]  Tian Han,et al.  Fault diagnosis of rotating machinery based on multi-class support vector machines , 2005 .

[5]  Boualem Boashash,et al.  EEG signal analysis using time-frequency analysis , 2003 .

[6]  Asoke K. Nandi,et al.  FAULT DETECTION USING SUPPORT VECTOR MACHINES AND ARTIFICIAL NEURAL NETWORKS, AUGMENTED BY GENETIC ALGORITHMS , 2002 .

[7]  Rajeev Alur,et al.  A Theory of Timed Automata , 1994, Theor. Comput. Sci..

[8]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[9]  Alexander J. Smola,et al.  Learning with kernels , 1998 .

[10]  Bo-Suk Yang,et al.  Condition classification of small reciprocating compressor for refrigeration using artificial neural networks and support vector machines , 2005 .

[11]  Zhang Zhi Time-frequency Signal Analysis and Application , 2002 .

[12]  Arthur K. Kordon,et al.  Fault diagnosis based on Fisher discriminant analysis and support vector machines , 2004, Comput. Chem. Eng..

[13]  Huang Xi-yue 2PTMC classification algorithm based on support vector machines and its application to fault diagnosis , 2003 .