INTELLIGENT DIAGNOSIS FOR INCIPIENT FAULT BASED ON LIFTING WAVELET PACKAGE TRANSFORM AND
SUPPORT VECTOR MACHINES ENSEMBLE

HU Qiao  HE Zhengjia  ZHANG Zhousuo  ZI Yanyang  LEI Yaguo

(State Key Laboratory for Manufacturing Systems Engineering, Xi’an Jiaotong University, Xi’an 710049)

Abstract: In order to solve the problem of correctly identifying incipient fault for electromechanical equipment and improve classification ability, a novel method of incipient fault intelligent diagnosis based on lifting wavelet package transform (LWPT) and support vector machines (SVMs) ensemble, is proposed. Firstly, a biorthogonal wavelet with impact fault property is constructed via lifting scheme, and the LWPT is carried out to extract sensitive frequency-band features from orginal signals. Then the fault characteristic frequencis can be detected by envelope spectrum analysis of wavelet package coefficients (WPCs) of the most sensitive frequency-band. Secondly, with the distance evaluation technique, the optimal features are obtained from the statistical characteristics of original signals and WPCs. Finally, the optimal features are input into the SVMs ensemble to identify the different fault cases. This method was applied to incipient fault diagnosis of rolling element bearings. Testing results show that the proposed method can effectively extract the fault features, and has better classification performance than the single SVMs, with a high classification success rate.

Key words: Lifting wavelet package transform  Feature extraction Support vector machines ensemble  Incipient fault diagnosis

CLC No: TH17  TP18

国家自然科学基金重点项目(50335030)、机械制造系统工程国家重点实验室科研基金、国家自然科学基金(50575171，50505033)和国家重点基础研究发展计划(973计划，2005CB724106)资助项目.Received 20050627,received in revised form 20060310