基于小波阈值去噪与HHT滚动轴承故障诊断  

Wavelet Threshold Denoising and HHT Rolling Bearing Fault Diagnosis

作　　者：金志浩[1] 陈广东 汪红 韩林洋 JIN Zhi-hao;CHEN Guang-dong;WANG Hong;HAN Lin-yang(School of Mechanical and Power Engineering,Shenyang University of Chemical Technology,Shenyang Liaoning 110142,China)

机构地区：[1]沈阳化工大学机械与动力工程学院,辽宁沈阳110142

出　　处：《计算机仿真》2023年第8期467-472,共6页Computer Simulation

基　　金：国家自然科学基金重点项目(U1708254);国家自然科学基金青年基金(11702178)。

摘　　要：针对噪音比较强烈环境下的故障诊断问题,提出了一种小波阈值去噪与希尔伯特黄变换(HHT)相结合提取轴承信号故障的方法。首先采集滚动轴承在运作时的振动信号,选取合适的小波阈值对采集到的时域信号进行去噪处理。采用HHT对信号进行时频分析,将去噪信号分解为若干个固有模态函数分量(IMF)并对其进行希尔伯特变换(HT),最终从希尔伯特边际谱得到故障频率从而判定轴承是否发生故障。试验结果表明,上述方法可以有效地降低噪声信号对HHT对轴承故障诊断的影响,更好的保留了有用的信息,提高故障诊断的准确性和可靠性。Aiming at the problem of fault diagnosis in an environment with strong noise,a method of wavelet threshold denoising combined with Hilbert Yellow Transform(HHT)to extract bearing signal faults is proposed.First,the vibration signal of the rolling bearing was collected during operation,and an appropriate wavelet threshold was selected to denoise the collected time-domain signal.The time-frequency analysis of the signal was carried out by HHT,the denoising signal was decomposed into several intrinsic modal function components(IMF)and subjected to Hilbert transform(HT),and finally the fault frequency was obtained from the Hilbert marginal spectrum to determine whether the bearing is malfunctioning.The test results show that this method can effectively reduce the influence of noise signals on HHT′s bearing fault diagnosis,better retain useful information,and improve the accuracy and reliability of fault diagnosis.

关 键 词：滚动轴承 小波阈值去噪 希尔伯特黄变换 时频分析 故障诊断 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]