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国外文献 > 1.轴承 > 7. 智能方法与计算 > 神经网络

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标 题： A Fault Diagnosis Method for Rotating Bearings Based on EWT Multi-Scale Entropy and PSO Algorithm to Optimize SVM
基于EWT多尺度熵与PSO算法优化SVM的旋转轴承故障诊断方法
作 者： Huabin Qin
Mingliang Liu
Zijian Guo
关键词： Rotary bearings  [旋转轴承]
Fault diagnosis  [ 故障诊断]
Support vector machine  [ 支持向量机]
Empirical wavelet transform  [ 经验小波变换]
Particle swarm optimization  [ 粒子群优化]
Multi-scale entropy  [ 多尺度熵]
出版年： 2019
会议名： 38th Chinese Control Conference (CCC 2019)
第38届中国控制会议（CCC 2019）
届 次： 38th
摘 要： The mechanical fault diagnosis results of the rotary bearings are mainly determined by the feature vector and classifier used. In order to obtain more significant signal characteristics, a new mechanical fault diagnosis method is proposed. First, the empirical wavelet transform (EWT) method is used to decompose the rotary bearing vibration signals into several intrinsic modal functions (IMFs). Secondly, by applying a Hilbert transform to each IMF component, the frequency characteristics of signals are illustrated in a time-frequency representation. Thirdly, the multi-scale entropies (MSEs) of components being highly correlated with the original signals are calculated to construct the eigenvectors of rotary bearing signals. Finally, the optimal support vector machine (SVM) classifier is established by particle swarm optimization (PSO). The experimental results show that the diagnosis rate of unknown samples is as high as 95% by using the method proposed in this paper.

旋转轴承的机械故障诊断结果主要取决于所采用的特征向量和分类器。为了获取更显著的信号特征，本文提出了一种新的机械故障诊断方法。首先，采用经验小波变换（EWT）将旋转轴承振动信号分解为多个固有模态函数（IMFs）。其次，对每个IMF分量应用希尔伯特变换，以时频表示方式展示信号的频率特性。第三，计算与原始信号高度相关的分量的多尺度熵（MSEs），从而构建出旋转轴承信号的特征向量。最后，通过粒子群优化（PSO）建立最优的支持向量机（SVM）分类器。实验结果表明，采用本文提出的方法，未知样本的诊断准确率高达95%。
中文内容为网络AI翻译，专业术语未必准确，仅供参考。

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