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Intelligent Gearbox Diagnosis Methods Based on SVM, Wavelet Lifting and RBR
Key Laboratory of Advanced Manufacturing Technology, Beijing University of Technology, Chao Yang District, Beijing, 100124, China
School of Mechanical & Electrical Engineering, Beijing University of Chemical Technology, Chao Yang District, Beijing, 100029, China
Graduate School of Bioresources, Mie University, 1577 Kurimamachiya-cho, Tsu, Mie, 514-8507, Japan
* Author to whom correspondence should be addressed.
Received: 22 March 2010; in revised form: 19 April 2010 / Accepted: 27 April 2010 / Published: 4 May 2010
Abstract: Given the problems in intelligent gearbox diagnosis methods, it is difficult to obtain the desired information and a large enough sample size to study; therefore, we propose the application of various methods for gearbox fault diagnosis, including wavelet lifting, a support vector machine (SVM) and rule-based reasoning (RBR). In a complex field environment, it is less likely for machines to have the same fault; moreover, the fault features can also vary. Therefore, a SVM could be used for the initial diagnosis. First, gearbox vibration signals were processed with wavelet packet decomposition, and the signal energy coefficients of each frequency band were extracted and used as input feature vectors in SVM for normal and faulty pattern recognition. Second, precision analysis using wavelet lifting could successfully filter out the noisy signals while maintaining the impulse characteristics of the fault; thus effectively extracting the fault frequency of the machine. Lastly, the knowledge base was built based on the field rules summarized by experts to identify the detailed fault type. Results have shown that SVM is a powerful tool to accomplish gearbox fault pattern recognition when the sample size is small, whereas the wavelet lifting scheme can effectively extract fault features, and rule-based reasoning can be used to identify the detailed fault type. Therefore, a method that combines SVM, wavelet lifting and rule-based reasoning ensures effective gearbox fault diagnosis.
Keywords: gearbox; support vector machines (SVM); wavelet lifting; rule-based reasoning (RBR); intelligent diagnosis
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Cite This Article
MDPI and ACS Style
Gao, L.; Ren, Z.; Tang, W.; Wang, H.; Chen, P. Intelligent Gearbox Diagnosis Methods Based on SVM, Wavelet Lifting and RBR. Sensors 2010, 10, 4602-4621.
Gao L, Ren Z, Tang W, Wang H, Chen P. Intelligent Gearbox Diagnosis Methods Based on SVM, Wavelet Lifting and RBR. Sensors. 2010; 10(5):4602-4621.
Gao, Lixin; Ren, Zhiqiang; Tang, Wenliang; Wang, Huaqing; Chen, Peng. 2010. "Intelligent Gearbox Diagnosis Methods Based on SVM, Wavelet Lifting and RBR." Sensors 10, no. 5: 4602-4621.