Rolling bearing fault diagnosis based on generalized multiscale mean permutation entropy and GWO-LSSVM

IF 1.4 4区 工程技术 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC
Li Liu, Zijin Liu, Xuefei Qian
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引用次数: 1

Abstract

Faults in rolling bearings are usually observed through pulses in the vibration signals. However, due to the influence of complex background noise and interference from other machine components present in measurement equipment, vibration signals are typically non-stationary and often contaminated by noise. Therefore, in order to effectively extract the random variation and non-linear dynamic variation characteristics of vibration signals, a new method of rolling bearing fault diagnosis based on generalized multiscale mean permutation entropy (GMMPE) and grey wolf optimized least squares support vector machine (GWO-LSSVM) is put forward in this paper. Based on the multiscale permutation entropy (MPE), the multiscale equalization is firstly used to replace the coarse grained process, and the value of mean is extended to variance to avoid the dynamic mutation of the original signal. Finally, the parameters of LSSVM are optimized by the grey wolf optimization algorithm to achieve accurate identification of fault modes. The results of simulation and experiment show that applying the proposed GMMPE to rolling bearing fault feature extraction is feasible and superior, and the method based on GMMPE and GWO-LSSVM has better noise robustness, which can effectively achieve rolling bearing fault diagnosis.

Abstract Image

基于广义多尺度均值排列熵和GWO-LSSVM的滚动轴承故障诊断
滚动轴承的故障通常通过振动信号中的脉冲来观察。然而,由于复杂的背景噪声和测量设备中存在的其他机器部件的干扰的影响,振动信号通常是非平稳的,并且经常受到噪声的污染。因此,为了有效地提取振动信号的随机变化和非线性动态变化特征,本文提出了一种基于广义多尺度均值排列熵和灰狼优化最小二乘支持向量机的滚动轴承故障诊断新方法。基于多尺度排列熵(MPE),首先使用多尺度均衡来代替粗粒度过程,并将均值扩展到方差,以避免原始信号的动态变异。最后,采用灰狼优化算法对LSSVM的参数进行了优化,实现了故障模式的准确识别。仿真和实验结果表明,将所提出的GMMPE应用于滚动轴承故障特征提取是可行和优越的,并且基于GMMPE和GWO-LSSVM的方法具有较好的噪声鲁棒性,可以有效地实现滚动轴承故障诊断。
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来源期刊
Iet Science Measurement & Technology
Iet Science Measurement & Technology 工程技术-工程:电子与电气
CiteScore
4.30
自引率
7.10%
发文量
41
审稿时长
7.5 months
期刊介绍: IET Science, Measurement & Technology publishes papers in science, engineering and technology underpinning electronic and electrical engineering, nanotechnology and medical instrumentation.The emphasis of the journal is on theory, simulation methodologies and measurement techniques. The major themes of the journal are: - electromagnetism including electromagnetic theory, computational electromagnetics and EMC - properties and applications of dielectric, magnetic, magneto-optic, piezoelectric materials down to the nanometre scale - measurement and instrumentation including sensors, actuators, medical instrumentation, fundamentals of measurement including measurement standards, uncertainty, dissemination and calibration Applications are welcome for illustrative purposes but the novelty and originality should focus on the proposed new methods.
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