Fault Pattern Recognition of Rolling Bearing Based on Wavelet Packet and Support Vector Machine

• Published in: 2006 6th World Congress on Intelligent Control and Automation Vol. 2; pp. 5516 - 5520
• Main Authors: Shuang Lu, Weizeng Chen, Meng Li
• Format: Conference Proceeding
• Language: Chinese; English
• Published: IEEE 2006
• Subjects: Fault diagnosis; Feature extraction; Frequency domain analysis; Pattern recognition; rolling bearing; Rolling bearings; Signal processing; support vector machine; Support vector machine classification; Support vector machines; wavelet packet; Wavelet packets; Wavelet transforms
• ISBN: 9781424403325; 1424403324
• DOI: 10.1109/WCICA.2006.1714128

The method of fault diagnosis of rolling bearings based on wavelet packet transform and support vector machine is presented. The key to fault bearings diagnosis is feature extracting and feature classifying. Wavelet packet transform, as a new technique of signal processing, possesses excellent characteristic of time-frequency localization and is suitable for analyzing the time-varying or transient signals. Support vector machine is capable of pattern recognition and nonlinear regression. According to the frequency domain feature of rolling bearing vibration signal, energy eigenvector of frequency domain is extracted using wavelet packet transform method. Fault pattern of rolling bearing is recognized using support vector machine multiple fault classifier. Theory and experiment shows that such method is available to recognize the fault pattern accurately and provides a new approach to intelligent fault diagnosis