基于FVMD多尺度排列熵和GK模糊聚类的故障诊断

doi:10.3901/JME.2018.14.016

机械工程学报 ›› 2018, Vol. 54 ›› Issue (14): 16-27.doi: 10.3901/JME.2018.14.016

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基于FVMD多尺度排列熵和GK模糊聚类的故障诊断

陈东宁^1,2, 张运东³, 姚成玉⁴, 孙飞^1,2, 周能元^1,2

1. 燕山大学河北省重型机械流体动力传输与控制重点实验室秦皇岛 066004;
2. 先进锻压成型技术与科学教育部重点实验室(燕山大学) 066004;
3. 航空工业金城南京机电液压工程研究中心南京 211106;
4. 燕山大学河北省工业计算机控制工程重点实验室秦皇岛 066004

收稿日期:2017-06-20 修回日期:2018-03-19 出版日期:2018-07-20 发布日期:2018-07-20
通讯作者: 陈东宁(通信作者),女,1978年出生,博士,副教授。主要研究方向为可靠性分析及优化。E-mail:dnchen@ysu.edu.cn
作者简介:张运东,男,1990年出生。主要研究方向为机械故障诊断。姚成玉,男,1975年出生,博士后,教授。主要研究方向为系统可靠性及故障诊断;孙飞,男,1991年出生。主要研究方向为故障诊断;周能元,男,1992年出生。主要研究方向为故障诊断。
基金资助:
国家自然科学基金（51675460，51405426）、中国博士后科学基金（2017M621101）和河北省自然科学基金（E2016203306）资助项目。

Fault Diagnosis Based on FVMD Multi-scale Permutation Entropy and GK Fuzzy Clustering

CHEN Dongning^1,2, ZHANG Yundong³, YAO Chengyu⁴, SUN Fei^1,2, ZHOU Nengyuan^1,2

1. Hebei Provincial Key Laboratory of Heavy Machinery Fluid Power Transmission and Control, Yanshan University, Qinhuangdao 066004;
2. Key Laboratory of Advanced Forging & Stamping Technology and Science(Yanshan University), Ministry of Education of China, Qinhuangdao 066004;
3. AVIC Jincheng Nanjing Engineering Institute of Aircraft System, Nanjing 211106;
4. Key Laboratory of Industrial Computer Control Engineering of Hebei Province, Yanshan University, Qinhuangdao 066004

Received:2017-06-20 Revised:2018-03-19 Online:2018-07-20 Published:2018-07-20

摘要/Abstract

摘要： 针对设备故障信号的非线性、非平稳特征，提出了基于快速变分模态分解、参数优化多尺度排列熵和特征加权GK模糊聚类的故障诊断方法。首先，在变分模态分解的基础上，引入快速迭代的思想，提出快速变分模态分解方法，以减少算法运行时间与迭代次数；其次，针对多尺度排列熵算法的参数确定问题，综合考虑参数之间的交互影响，提出一种基于多作用力微粒群算法的参数优化方法，并通过快速变分模态分解和参数优化多尺度排列熵算法提取故障特征；之后，考虑到样本特征矢量中各维特征在聚类过程中的贡献不同，提出基于ReliefF特征加权的GK模糊聚类方法，由特征加权GK模糊聚类确定标准聚类中心，通过择近原则实现故障模式的分类识别；最后，以在机械故障试验平台上采集到的轴承不同故障类型的振动信号为研究对象，应用所提方法进行分析。结果表明，相对于改进前的变分模态分解、多尺度排列熵和GK模糊聚类方法，本文所提方法不仅能够有效提取故障特征，还能准确实现故障模式的分类识别，而且故障识别率得到提高。

关键词: 参数优化多尺度排列熵, 故障诊断, 快速变分模态分解, 特征加权GK模糊聚类

Abstract: Aiming at the nonlinear and non-stationary characteristics of equipment fault signals, a fault diagnosis method based on the fast variational mode decomposition (FVMD), the parameter optimized multi-scale permutation entropy and the feature weighted GK fuzzy clustering is proposed. Firstly, to reduce the running time and the number of iterations, the idea of fast iteration is introduced on the basis of variational mode decomposition, and the fast variational mode decomposition method is proposed. Secondly, aiming at the problem of parameter determination of multi-scale permutation entropy and considering the interaction among parameters comprehensively, a method of parameters optimization based on multi force particle swarm optimization is proposed, then the parameter optimized multi-scale permutation entropy is combined with fast variational mode decomposition for fault feature extraction. Thirdly, considering the contribution of each feature in the feature vector to the clustering process, a new method of GK fuzzy clustering based on ReliefF feature weighting is proposed, the feature weighted GK fuzzy clustering is used to determine the standard clustering center, and the fault pattern recognition is realized by the principle of choosing the nearest. Finally, the fault data of rolling bearing collected on the machinery fault simulator is taken as the research object, and the proposed method is applied to the analysis. The results show that compared with the original variational mode decomposition, multi-scale permutation entropy and GK fuzzy clustering, the proposed method not only can extract the fault features effectively, but also can realize the classification and recognition of fault modes accurately, and the fault recognition rate is improved.

Key words: fast variational mode decomposition, fault diagnosis, feature weighted GK fuzzy clustering, parameter optimized multi-scale permutation entropy

中图分类号:

TH165
TH133

陈东宁, 张运东, 姚成玉, 孙飞, 周能元. 基于FVMD多尺度排列熵和GK模糊聚类的故障诊断[J]. 机械工程学报, 2018, 54(14): 16-27.

CHEN Dongning, ZHANG Yundong, YAO Chengyu, SUN Fei, ZHOU Nengyuan. Fault Diagnosis Based on FVMD Multi-scale Permutation Entropy and GK Fuzzy Clustering[J]. Journal of Mechanical Engineering, 2018, 54(14): 16-27.

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基于FVMD多尺度排列熵和GK模糊聚类的故障诊断

Fault Diagnosis Based on FVMD Multi-scale Permutation Entropy and GK Fuzzy Clustering

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