基于自适应经验傅里叶分解的机械故障诊断方法

doi:10.3901/JME.2020.09.125

机械工程学报 ›› 2020, Vol. 56 ›› Issue (9): 125-136.doi: 10.3901/JME.2020.09.125

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基于自适应经验傅里叶分解的机械故障诊断方法

郑近德¹, 潘海洋¹, 程军圣², 包家汉¹, 刘庆运¹, 丁克勤³

1. 安徽工业大学机械工程学院马鞍山 243032;
2. 湖南大学机械与运载工程学院长沙 410082;
3. 中国特种设备检测研究院北京 100029

收稿日期:2019-08-28 修回日期:2020-02-27 出版日期:2020-05-05 发布日期:2020-05-29
通讯作者: 郑近德(通信作者),男,1986年出生,博士,副教授,硕士研究生导师。主要研究方向为动态信号处理与机械故障诊断。E-mail:jdzheng@ahut.edu.cn
作者简介:潘海洋,男,1989年出生,博士,讲师。主要研究方向为模式识别与机械故障诊断。E-mail:pansea@sina.cn;程军圣,男,1968年出生,博士,教授,博士研究生导师。主要研究方向为动态信号处理与机械故障诊断。E-mail:chengjunsheng@hnu.edu.cn
基金资助:
国家重点研发计划（2017YFC0805100）、国家自然科学基金（51975004）和安徽省高校自然科学研究项目（KJ2019A053，KJ2018ZD005）资助项目。

Adaptive Empirical Fourier Decomposition Based Mechanical Fault Diagnosis Method

ZHENG Jinde¹, PAN Haiyang¹, CHENG Junsheng², BAO Jiahan¹, LIU Qingyun¹, DING Keqin³

1. School of Mechanical Engineering, Anhui University of Technology, Maanshan 243032;
2. School of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082;
3. China Special Equipment Inspection and Research Institute, Beijing 100029

Received:2019-08-28 Revised:2020-02-27 Online:2020-05-05 Published:2020-05-29

摘要/Abstract

摘要： 为了克服傅里叶变换、经验模态分解与傅里叶分解方法在分析非平稳信号方面的不足，提出一种适合非线性和非平稳信号分析的新方法——自适应经验傅里叶分解（Adaptive empirical Fourier decomposition，AEFD）。AEFD方法以快速傅里叶变换为基础，通过对变换系数进行分组重构，能够将一个非平稳信号自适应地分解为若干个瞬时频率具有物理意义的傅里叶本征模态函数（Fourier intrinsic mode function，FIMF）之和。研究了AEFD的分解正交性和精确性，通过仿真信号分析，将其与经验模态分解，变分模态分解和傅里叶分解方法等进行了详细对比，结果表明了AEFD的优越性。最后，为了提高故障诊断的精度和验证AEFD的有效性，将AEFD应用到转子碰摩和滚动轴承局部故障诊断中。试验数据分析结果表明，与经验模态分解等方法相比，AEFD不仅能够有效地诊断故障，而且诊断精度更高。

关键词: 非平稳信号, 经验模态分解, 变分模态分解, 自适应经验傅里叶分解, 故障诊断

Abstract: A novel non-stationary signal analysis method termed adaptive empirical Fourier decomposition (AEFD) is proposed to overcome the deficiencies of Fourier transform, empirical mode decomposition (EMD) and Fourier decomposition method (FDM) in non-stationary signal analysis. AEFD is based on fast Fourier transform and by grouping and reconstructing the coefficients of fast Fourier transform, it can adaptively decompose a given non-stationary signal into several Fourier intrinsic mode functions (FIMF) with instantaneous frequency of physical significance. The decomposition orthogonality and accuracy of AEFD are also studied. The proposed method is compared with EMD, FDM and variational mode decomposition in detail through simulation signal analysis and the results have verified the superiority of AEFD. Finally, AEFD method is applied to the diagnosis of rotor system with local rubbing and rolling bearing with local fault to verify its effectiveness and improve the accuracy of fault diagnosis. The experimental data analysis results show that compared with EMD, AEFD can effectively identify the fault location and get a higher diagnostic accuracy than the methods mentioned above.

Key words: non-stationary signal, empirical mode decomposition, variational mode decomposition, adaptive empirical Fourier decomposition, fault diagnosis

中图分类号:

TN911
TH165

郑近德, 潘海洋, 程军圣, 包家汉, 刘庆运, 丁克勤. 基于自适应经验傅里叶分解的机械故障诊断方法[J]. 机械工程学报, 2020, 56(9): 125-136.

ZHENG Jinde, PAN Haiyang, CHENG Junsheng, BAO Jiahan, LIU Qingyun, DING Keqin. Adaptive Empirical Fourier Decomposition Based Mechanical Fault Diagnosis Method[J]. Journal of Mechanical Engineering, 2020, 56(9): 125-136.

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基于自适应经验傅里叶分解的机械故障诊断方法

Adaptive Empirical Fourier Decomposition Based Mechanical Fault Diagnosis Method

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