Wavelet Function Suitable for Fault Feature Extraction of
Acoustic Emission Signal

LI Xuejun^{1, 2} LIAO Chuanjun¹ CHU Fulei²

(1. Key Laboratory of Health Maintenance for Mechanical Equipment of Hunan Province, Hunan Science and Technology University, Xiangtan 411201;
2. Department of Precision Instruments and Mechanology, Tsinghua University, Beijing 100084)

Abstract: It is pointed that wavelet analysis has powerful ability for weak signal detection, which helps it to be used for detection and fault diagnosis of acoustic emission signals well. However, in wavelet analysis of fault acoustic emission(AE) signals nowadays, the diagnosis results using the general wavelet functions are not the best, and some new wavelet functions need to be created at once. By analyzing the characteristics of typical AE signals initiated by mechanical faults or damages, and according to the extracting principle of fault characters of AE signals and the construction method of continuous wavelet function, a wavelet function is designed. When applying the function and Daubechies wavelet for fault diagnosis of rolling bearings based on AE technique at the same time, the results using the former are more clear, accurate and reliable. Both theory analysis and experiment research prove that the wavelet function is scientific and effective.

Key words: Wavelet function  Wavelet transform  Acoustic emission  Fault diagnosis  Feature extraction  Rolling bearing

CLC No: TG115.28 TH113

国家自然科学基金(50675066)和湖南省科技计划(2007FJ3025)资助项 目. Received 20070509, received in revised form 20071224

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