改进的正弦辅助多元经验模式分解及其在滚动轴承故障诊断中的应用

doi:10.3969/j.issn.1004-132X.2022.11.010

摘要/Abstract

摘要： 正弦辅助多元经验模式分解算法（SA-MEMD）通过在额外的通道中加入正弦辅助信号来减少模式混合，但该算法对噪声敏感，辅助信号的主频率比需要根据经验确定，为此，提出了一种改进的正弦辅助多元经验模式分解算法。首先使用非局部均值降噪对原始信号进行预处理，减少噪声对算法的干扰，其次使用短时傅里叶变换确定信号频谱范围，然后以最小集成EMD能量熵准则选择最优主频率比，最后根据正弦辅助多元经验模式分解算法的步骤进行信号处理。模拟信号和实际信号的对比分析结果证明，改进的方法可以减少传统的多元经验模式分解方法存在的模式混合现象。

关键词: 故障诊断, 正弦辅助多元经验模式分解, 模式混合, 短时傅里叶变换, 能量熵

Abstract: The SA-MEMD algorithm reduced mode mixing by adding a sine assisted signal to an additional channel, but the algorithm was sensitive to noises, and the main frequency ratio of the auxiliary signals needed to be determined empirically. For this reason, an improved SA-MEMD was proposed. First, The non-local mean noise reduction was used to preprocess the original signals to reduce noise interference for the algorithm, then the short-time Fourier transform was used to determine the signal spectrum range, thus the optimal main frequency ratio was selected based on the minimum ensemble EMD energy entropy criterion. The steps of the SA-MEMD algorithm were used to complete signal processing. The analysis of simulation signals and actual signals proves that the improved method may alleviate the mode mixing phenomenon that exists in the traditional multivariate empirical mode decomposition methods.

Key words: fault diagnosis, sine-assisted multivariate empirical mode decomposition(SA-MEMD), modal aliasing, short-time Fourier transform, energy entropy

中图分类号:

吴利锋, 吕勇, 袁锐, 朱熹, 游俊, . 改进的正弦辅助多元经验模式分解及其在滚动轴承故障诊断中的应用[J]. 中国机械工程, 2022, 33(11): 1336-1344.

WU Lifeng, , LYU Yong, YUAN Rui, ZHU Xi, YOU Jun, . Improved SA-MEMD with Applications to Fault Diagnosis of Rolling Bearings[J]. China Mechanical Engineering, 2022, 33(11): 1336-1344.

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链接本文: http://www.cmemo.org.cn/CN/10.3969/j.issn.1004-132X.2022.11.010

http://www.cmemo.org.cn/CN/Y2022/V33/I11/1336

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