Title :
Investigation of the Mechanical Faults Classification Using Support Vector Machine Approach
Author :
Jiang, Zhiqiang ; Feng, Xilan ; Feng, Xianzhang ; Li, Lingjun
Author_Institution :
Sch. of Mechatron. Eng., Zhengzhou Inst. of Aeronaut. Ind. Manage., Zhengzhou, China
Abstract :
Support Vector Machine (SVM) is a machine learning algorithm based on the Statistical Learning Theory (SLT), which can get good classification effects with a few learning samples. SVM represents a new approach to pattern classification and has been shown to be particularly successful in many fields such as image identification and face recognition. It also provides us with a new method to develop intelligent fault diagnosis. This paper presents a SVM based approach for fault diagnosis of rolling bearings. Experimentation with vibration signals of bearing was conducted. The vibration signals acquired from the bearings were directly used in the calculating without the preprocessing of extracting its features. Compared with the Artificial Neural Network (ANN) based method, the SVM based method has desirable advantages. Also a multi-faults SVM classifier based on binary classifiers is constructed for gear faults in this paper. Other experiments with gear fault samples showed that the multi-fault SVM classifier has good classification ability and high efficiency, and suitable for on line diagnosis for mechanical system.
Keywords :
fault diagnosis; gears; learning (artificial intelligence); mechanical engineering computing; neural nets; pattern classification; rolling bearings; statistical analysis; support vector machines; SVM classifier; artificial neural network based method; bearing vibration signals; binary classifiers; gear faults; intelligent fault diagnosis; machine learning algorithm; mechanical faults classification; mechanical system; online diagnosis; pattern classification; rolling bearings; statistical learning theory; support vector machine approach; Artificial neural networks; Classification algorithms; Fault diagnosis; Feature extraction; Kernel; Support vector machines; Training; Fault diagnosis; Intelligent diagnosis; Multi-fault classification; Support Vector Machine (SVM);
Conference_Titel :
Intelligent Human-Machine Systems and Cybernetics (IHMSC), 2010 2nd International Conference on
Conference_Location :
Nanjing, Jiangsu
Print_ISBN :
978-1-4244-7869-9
DOI :
10.1109/IHMSC.2010.30