基于改进全息希尔伯特谱分析的旋转机械故障诊断方法

doi:10.3901/JME.2023.01.162

机械工程学报 ›› 2023, Vol. 59 ›› Issue (1): 162-174.doi: 10.3901/JME.2023.01.162

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基于改进全息希尔伯特谱分析的旋转机械故障诊断方法

郑近德¹, 应万明¹, 潘海洋¹, 童靳于¹, 刘庆运¹, JI Jinchen²

1. 安徽工业大学机械工程学院马鞍山 243032;
2. 悉尼科技大学机械与机电工程学院新南威尔士州澳大利亚 2007

收稿日期:2022-03-07 修回日期:2022-09-19 出版日期:2023-01-05 发布日期:2023-03-30
通讯作者: 郑近德(通信作者),男,1986年出生,博士,教授,硕士研究生导师。主要研究方向为动态信号处理、时频分析及机械设备故障诊断。E-mail:lqdlzheng@126.com
作者简介:应万明,男,1995年出生,硕士研究生。主要研究方向为动态信号处理。E-mail:wmying033@126.com
基金资助:
国家自然科学基金（51975004）和安徽省自然科学基金（2008085QE215）资助项目。

Improved Holo-Hilbert Spectrum Analysis-Based Fault Diagnosis Method for Rotating Machines

ZHENG Jinde¹, YING Wanming¹, PAN Haiyang¹, TONG Jinyu¹, LIU Qingyun¹, JI Jinchen²

1. School of Mechanical Engineering, Anhui University of Technology, Maanshan 243032;
2. School of Mechanical and Mechatronic Engineering, University of Technology Sydney, NSW 2007, Australia

Received:2022-03-07 Revised:2022-09-19 Online:2023-01-05 Published:2023-03-30

摘要/Abstract

摘要： 时频分析方法能够有效同时提取故障设备振动信号的时间和频率信息，但在全面反映非线性振动信号幅值调制与频率调制特征之间的跨尺度耦合关系方面仍存在局限，且容易受到噪声干扰。对此，创新性地将全息希尔伯特谱分析（Holo-Hilbert spectral analysis，HHSA）方法引入到机械故障诊断中。HHSA通过双层经验模态分解（EMD）结构可完整地描述振动信号的内部调制特性，非常适合机械局部故障的检测。同时，为了进一步提升HHSA的诊断精度、抑制EMD模态混叠和噪声干扰，提出一种基于改进再生相移正弦辅助经验模式分解（Improved regenerated phase-shifted sinusoid-assisted EMD，IRPSEMD）的改进HHSA方法（IHHSA）。通过仿真信号验证IHHSA方法用于局部故障检测和诊断的有效性。最后，将IHHSA应用于齿轮裂纹故障和滚动轴承局部故障诊断中，结果表明，提出的IHHSA方法能够更全面地反映和呈现非线性故障振动信号的内部调制关系，且具有更好的故障识别能力。

关键词: 全息希尔伯特谱分析, 时频分析, 非线性耦合, 故障调制, 故障诊断

Abstract: Although the time-frequency analysis method can extract both the time and frequency domains information of vibration signal for the faulty equipment simultaneously, its use in reflecting the cross-scale coupling relationship between the amplitude-modulation and frequency-modulation characteristics of the nonlinear vibration signal has so far been hindered, and it is prone to be interfered by noises. On this base, the Holo-Hilbert spectral analysis (HHSA) method is innovatively introduced into mechanical fault diagnosis. The internal modulation characteristics of vibration signals can be completely described by the HHSA method with double-layer empirical mode decomposition (EMD) structure, making it an ideal tool for detecting the local faults of mechanical components. At the same time, to improve the diagnosis accuracy of HHSA and suppress the noise interference and the mode aliasing caused by EMD, an improved HHSA (IHHSA) method based on improved regenerated phase shifted sinusoidal assisted EMD (IRPSEMD) is proposed. The usefulness of the IHHSA method for local fault feature diagnosis are validated by the analysis of simulation signals. Finally, the IHHSA method is applied to the detection of gear crack fault and the diagnosis of rolling bearings with local fault. The results show that the internal modulation relationship of nonlinear fault vibration signal can be reflected by the proposed IHHSA method comprehensively, which shows a better fault identification ability.

Key words: Holo-Hilbert spectral analysis, time frequency analysis, nonlinear coupling, fault modulation, fault diagnosis

中图分类号:

TH165

郑近德, 应万明, 潘海洋, 童靳于, 刘庆运, JI Jinchen. 基于改进全息希尔伯特谱分析的旋转机械故障诊断方法[J]. 机械工程学报, 2023, 59(1): 162-174.

ZHENG Jinde, YING Wanming, PAN Haiyang, TONG Jinyu, LIU Qingyun, JI Jinchen. Improved Holo-Hilbert Spectrum Analysis-Based Fault Diagnosis Method for Rotating Machines[J]. Journal of Mechanical Engineering, 2023, 59(1): 162-174.

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基于改进全息希尔伯特谱分析的旋转机械故障诊断方法

Improved Holo-Hilbert Spectrum Analysis-Based Fault Diagnosis Method for Rotating Machines

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