基于小波旁瓣相消器的轴承故障特征提取  

Bearing Fault Feature Extraction Based on Wavelet Generalized Sidelobe Canceller

作　　者：苏润凡 廖爱华[1,2] 胡定玉 师蔚[1,2] 高伟民 丁亚琦[3] SU Run-fan;LIAO Ai-hua;HU Ding-yu;SHI Wei;GAO Wei-min;DING Ya-qi(School of Urban Railway Transportation,Shanghai University of Engineering Science,Shanghai 201620,China;Shanghai Engineering Research Center of Railway Noise and Vibration Control,Shanghai 201620,China;Vehicle Branch,Shanghai Metro Maintenance&Support Co.,Ltd.,Shanghai 200235,China)

机构地区：[1]上海工程技术大学城市轨道交通学院,上海201620 [2]上海市轨道交通振动与噪声控制技术工程研究中心,上海201620 [3]上海地铁维护保障有限公司车辆分公司,上海200235

出　　处：《测控技术》2022年第12期29-35,共7页Measurement & Control Technology

基　　金：国家自然科学基金青年科学基金项目(51605274);上海市地方院校能力建设项目(20030501000)。

摘　　要：为解决强背景噪声下声信号提取的轴承故障特征不显著问题,提出一种基于小波旁瓣相消器的故障特征提取方法。该方法利用小波滤波器组将含噪故障轴承声信号变换到小波域,进行小波域阵列广义旁瓣相消自适应波束形成,再通过小波滤波器组重构增强后的故障轴承信号,最后对重构增强后的信号进行包络解调并提取故障特征频率进行故障诊断。实验结果表明,该方法能够在强背景噪声下有效提取滚动轴承故障特征,并且相较于传统的延时求和波束形成器具有更好的降噪和故障特征增强效果。In order to solve the problem of insignificant bearing fault features extracted from acoustic signals under strong background noise, a fault feature extraction method based on wavelet beamforming is proposed.This method uses a wavelet filter bank to transform the acoustic signal of a noisy faulty bearing into the wavelet domain and performs an array of wavelet domain generalized sidelobe cancellation adaptive beamforming, and then reconstructs the enhanced fault through the wavelet filter bank bearing acoustic signal, and finally envelops demodulation of the reconstructed and enhanced signal and extract the fault characteristic frequency for fault diagnosis.The experimental results show that the method can effectively extract the fault features of rolling bearings under strong background noise, and has better noise reduction and fault feature enhancement effects than the traditional time-delay summation beamformer.

关 键 词：轴承故障诊断 声学诊断 小波滤波器组 波束形成 旁瓣相消技术 

分 类 号：TH133.3[机械工程—机械制造及自动化] TP206[自动化与计算机技术—检测技术与自动化装置]