{"title":"A novel compound fault diagnosis for rolling bearing based on sparse harmonic feature mode decomposition and enhanced spectral amplitude modulation","authors":"Weiliang Sun, Zong Meng, Jingbo Liu, Dengyun Sun, Kai Chen, Yonglei Ren","doi":"10.1016/j.ymssp.2025.113343","DOIUrl":null,"url":null,"abstract":"<div><div>To effectively tackle the challenge of accurately extracting compound fault features from rolling bearing vibration signals, a novel method termed sparse harmonic feature mode decomposition and enhanced spectral amplitude modulation is proposed. The method combines the superior mode extraction capability of feature mode decomposition with the nonlinear feature identification advantage of spectral amplitude modulation. First, a sparsity factor is introduced to refine the output signal of a finite impulse response filter, enhancing the sparsity of the signal. Second, the filter coefficients are optimized with the envelope harmonic noise ratio as the objective function, which facilitates the extraction of essential feature components. Then, the extracted components undergo nonlinear frequency and amplitude modulation. The spectral coherence of the modulate modal signals is calculated to capture modulation characteristics. Finally, an enhanced envelope spectrum strategy is adopted to improve the accuracy of fault feature identification. Results from both simulations and experimentals confirm that the SHFMD-ESAM method can accurately separate and identify multiple compound fault features in rolling bearings. Compared with existing methods, it achieves higher accuracy and robustness, demonstrating strong potential for rolling bearing fault diagnosis.</div></div>","PeriodicalId":51124,"journal":{"name":"Mechanical Systems and Signal Processing","volume":"239 ","pages":"Article 113343"},"PeriodicalIF":8.9000,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Mechanical Systems and Signal Processing","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0888327025010441","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, MECHANICAL","Score":null,"Total":0}
引用次数: 0
Abstract
To effectively tackle the challenge of accurately extracting compound fault features from rolling bearing vibration signals, a novel method termed sparse harmonic feature mode decomposition and enhanced spectral amplitude modulation is proposed. The method combines the superior mode extraction capability of feature mode decomposition with the nonlinear feature identification advantage of spectral amplitude modulation. First, a sparsity factor is introduced to refine the output signal of a finite impulse response filter, enhancing the sparsity of the signal. Second, the filter coefficients are optimized with the envelope harmonic noise ratio as the objective function, which facilitates the extraction of essential feature components. Then, the extracted components undergo nonlinear frequency and amplitude modulation. The spectral coherence of the modulate modal signals is calculated to capture modulation characteristics. Finally, an enhanced envelope spectrum strategy is adopted to improve the accuracy of fault feature identification. Results from both simulations and experimentals confirm that the SHFMD-ESAM method can accurately separate and identify multiple compound fault features in rolling bearings. Compared with existing methods, it achieves higher accuracy and robustness, demonstrating strong potential for rolling bearing fault diagnosis.
期刊介绍:
Journal Name: Mechanical Systems and Signal Processing (MSSP)
Interdisciplinary Focus:
Mechanical, Aerospace, and Civil Engineering
Purpose:Reporting scientific advancements of the highest quality
Arising from new techniques in sensing, instrumentation, signal processing, modelling, and control of dynamic systems