基于IEWT-MOMEDA-FSC的滚动轴承故障诊断  

Fault Diagnosis of Rolling Bearings Based on IEWT-MOMEDA-FSC

作　　者：吴振雄 王林军 邹腾枭 陈梦华 陈保家 WU Zhenxiong;WANG Linjun;ZOU Tengxiao;CHEN Menghua;CHEN Baojia(Hubei Provincial Key Laboratory of Hydropower Machinery Design and Maintenance,China Three Gorges University,Yichang 443002,China;College of Mechanical and Power Engineering,China Three Gorges University,Yichang 443002,China;Hubei Special Equipment Inspection and Testing Institute Yichang Institutional Division,Yichang 443000,China)

机构地区：[1]三峡大学水电机械设备设计与维护湖北省重点实验室,湖北宜昌443002 [2]三峡大学机械与动力学院,湖北宜昌443002 [3]湖北特种设备检验检测研究院宜昌分院,湖北宜昌443000

出　　处：《三峡大学学报（自然科学版）》2024年第1期92-98,共7页Journal of China Three Gorges University：Natural Sciences

基　　金：国家自然科学基金项目(51975324)。

摘　　要：针对滚动轴承故障信号常伴有噪声干扰且故障特征难以提取的问题,本文提出一种基于改进经验小波变换(IEWT)、多点优化最小熵解卷积(MOMEDA)和快速谱相关(FSC)的滚动轴承故障诊断方法.首先,将原始信号进行快速谱相关分析得到增强包络谱,通过增强包络谱的极值点来自适应地划分频谱,以分割的频谱为边界构建小波滤波器组将信号分解为多个IMF分量,利用相关峭度准则筛选出有效的分量进行叠加;其次,用MOMEDA对其进行降噪处理,将降噪后的信号进行快速谱相关分析,得到增强包络谱图;最后,将增强包络谱图中幅值较高的频率与故障频率对比,判定其失效形式,用所提出的方法对实测轴承故障信号进行分析验证.结果表明,所提出的方法能有效降低噪音干扰且增强信号故障冲击特性,在噪声环境下具有较强的故障特征提取能力.Aiming to the problem that the fault signal of rolling bearings is often mixed with noise interference and the fault characteristics are more difficult to be extracted accurately,a novel fault diagnosis method of rolling bearing based on the improved empirical wavelet transform(IEWT),multi-point optimized minimum entropy deconvolution algorithm(MOMEDA),and fast spectral enhancement(FSC)is proposed.Firstly,the original signal is subjected to fast spectral correlation analysis to obtain the enhanced envelope spectrum.The adaptive division of the spectrum is determined based on the extremum points of the enhanced envelope spectrum,and a wavelet filter bank is constructed with the segmented spectrum as the boundary to decompose the signal into the multiple intrinsic mode function components.The effective components are selected by the method of kurtosis-correlation coefficient threshold and they are superimposed.Secondly,the noise reduction processing of the signal is conducted by the algorithm MOMEDA.The denoised signal is subjected to fast spectral correlation analysis to obtain the enhanced envelope spectrum graph.Finally,the high-amplitude frequency in the enhanced envelope spectrum graph is compared with the fault frequency to determine the failure mode.The proposed method is validated by analyzing the fault signals of the measured bearings.The results show that the proposed method can effectively reduce the noise interference and enhance the fault impact characteristics of the signal.It proves that the presented method can effectively extract the faults features of rolling bearing in the complex noise environment.

关 键 词：改进经验小波变换 多点最优最小熵解卷积 快速谱相关 峭度 互相关 

分 类 号：TH133.33[机械工程—机械制造及自动化]