Empirical variational mode extraction and its application in bearing fault diagnosis

IF 3.4 2区物理与天体物理 Q1 ACOUSTICS

Applied Acoustics Pub Date : 2024-10-19 DOI:10.1016/j.apacoust.2024.110349

Bin Pang , Yanjie Zhao , Changqi Yu , Ziyang Hao , Zhenduo Sun , Zhenli Xu , Pu Li

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引用次数: 0

Abstract

Bearing fault signals typically contain rich interference components such as random pulses, harmonics, and environmental noise, posing significant challenges for bearing fault feature identification. Derived from variational mode decomposition (VMD), variational mode extraction (VME) stands out due to its specialized narrowband filtering capabilities, enabling effective extraction of targeted components from complex signals. However, VME’s capability notably depends on two key parameters: the penalty factor, which controls the bandwidth of extracted mode, and the central frequency, determining the frequency band’s center for extraction. An empirical variational mode extraction (EVME) method, inspired by the structure of empirical wavelet transform (EWT), is introduced to guide optimal filtering and demodulation analysis of fault components. Firstly, the effects of central frequency and penalty factor on the filtering characteristics of VME are thoroughly investigated and the mathematical relationship between bandwidth and penalty parameter is established through mathematical simulations. Secondly, a spectrum background scale-space division (SBSSD) method which incorporates adaptive clutter separation (ACS) and scale-space division is proposed to implement an optimal spectrum division, guiding the parameter determination of VME. Finally, each component is recursively extracted by VME from low to high frequencies following the segmentation outcomes of frequency bands. Simulated and experimental validations confirm the capability of EVME for extracting bearing fault features. Furthermore, comparisons with VMD and EWT underscore its superiority.

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经验变分模式提取及其在轴承故障诊断中的应用

轴承故障信号通常包含丰富的干扰成分，如随机脉冲、谐波和环境噪声，这给轴承故障特征识别带来了巨大挑战。变异模态提取（VME）源自变异模态分解（VMD），因其专业的窄带滤波能力而脱颖而出，能从复杂信号中有效提取目标成分。然而，VME 的能力主要取决于两个关键参数：控制提取模式带宽的惩罚因子和决定提取频带中心的中心频率。受经验小波变换（EWT）结构的启发，本文介绍了一种经验变分模式提取（EVME）方法，用于指导故障成分的优化滤波和解调分析。首先，深入研究了中心频率和惩罚因子对 VME 滤波特性的影响，并通过数学模拟建立了带宽和惩罚参数之间的数学关系。其次，提出了一种结合自适应杂波分离（ACS）和尺度空间划分的频谱背景尺度空间划分（SBSSD）方法，以实现最优频谱划分，指导 VME 参数的确定。最后，VME 根据频段划分结果，从低频到高频递归提取每个分量。模拟和实验验证证实了 EVME 提取轴承故障特征的能力。此外，与 VMD 和 EWT 的比较也凸显了其优越性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Applied Acoustics 物理-声学

CiteScore

7.40

自引率

11.80%

发文量

618

审稿时长

7.5 months

期刊介绍： Since its launch in 1968, Applied Acoustics has been publishing high quality research papers providing state-of-the-art coverage of research findings for engineers and scientists involved in applications of acoustics in the widest sense. Applied Acoustics looks not only at recent developments in the understanding of acoustics but also at ways of exploiting that understanding. The Journal aims to encourage the exchange of practical experience through publication and in so doing creates a fund of technological information that can be used for solving related problems. The presentation of information in graphical or tabular form is especially encouraged. If a report of a mathematical development is a necessary part of a paper it is important to ensure that it is there only as an integral part of a practical solution to a problem and is supported by data. Applied Acoustics encourages the exchange of practical experience in the following ways: • Complete Papers • Short Technical Notes • Review Articles; and thereby provides a wealth of technological information that can be used to solve related problems. Manuscripts that address all fields of applications of acoustics ranging from medicine and NDT to the environment and buildings are welcome.