A Wavelet Spectrum Technique for Machinery Fault Diagnosis

• 作者: Derek Kanneg Wilson Wang
• 来源期刊: 信号与信息处理(英文)
• 学科: 医学
• 关键词: Machinery Condition Monitoring ROTARY Machines Bearing FAULT Detection NON-STATIONARY Signal WAVELET TRANSFORM RESONANCE Feature
• 年，卷（期）: 2011,（4）
• 页码范围: 322-329
• 页数: 8页
• 分类号: R73

摘要

Rotary machines are widely used in various applications. A reliable machinery fault detection technique is critically needed in industries to prevent the machinery system’s performance degradation, malfunction, or even catastrophic failures. The challenge for reliable fault diagnosis is related to the analysis of non-stationary features. In this paper, a wavelet spectrum (WS) technique is proposed to tackle the challenge of feature extraction from these non-stationary signatures;this work will focus on fault detection in rolling element bearings. The vibration signatures are first analyzed by a wavelet transform to demodulate representative features;the periodic features are then enhanced by cross-correlating the resulting wavelet coefficient functions over several contributive neighboring wavelet bands. The effectiveness of the proposed technique is examined by experimental tests corresponding to different bearing conditions. Test results show that the developed WS technique is an effective signal processing approach for non-stationary feature extraction and analysis, and it can be applied effectively for bearing fault detection.