{"title":"Adaptive generalized dispersive mode decomposition: A data-driven approach for nonlinear dispersive component extraction in mechanical systems","authors":"Hongbing Wang, Shiqian Chen, Wanming Zhai","doi":"10.1016/j.jsv.2025.119329","DOIUrl":null,"url":null,"abstract":"<div><div>Dispersive signals are commonly encountered in mechanical systems and dispersion feature extraction is a critical task in various mechanical engineering applications such as fault diagnosis and nondestructive testing. However, traditional methods for decomposing dispersive signals, including generalized dispersive mode decomposition (GDMD) and nonlinear dispersive component decomposition, are heavily reliant on prior information such as initial group delay (GD), bandwidth parameters, and the number of modes. This reliance limits their adaptability in mechanical vibration signal analysis, particularly in extracting close dispersion features. To address these issues, this paper proposes a novel data-driven approach termed adaptive generalized dispersive mode decomposition (AGDMD). First, a frequency-varying low-pass filter based on dispersion compensation is constructed and then a fully data-driven GD estimation algorithm is proposed to determine effective initial GD. Subsequently, an adaptive bandwidth estimation strategy guided by initial GDs is devised to initialize and refine bandwidth parameters of the optimal dispersion compensation algorithm. Finally, according to the initial GDs and bandwidth parameters, the AGDMD adopts a recursive framework to sequentially extract nonlinear dispersive modes from the original signal. Simulated examples and real-life applications to railway fault diagnosis and Lamb wave analysis demonstrate that the proposed AGDMD has both high adaptability and good noise robustness, and can accurately extract close dispersive modes without excessive prior information.</div></div>","PeriodicalId":17233,"journal":{"name":"Journal of Sound and Vibration","volume":"618 ","pages":"Article 119329"},"PeriodicalIF":4.3000,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Sound and Vibration","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0022460X25004031","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ACOUSTICS","Score":null,"Total":0}
引用次数: 0
Abstract
Dispersive signals are commonly encountered in mechanical systems and dispersion feature extraction is a critical task in various mechanical engineering applications such as fault diagnosis and nondestructive testing. However, traditional methods for decomposing dispersive signals, including generalized dispersive mode decomposition (GDMD) and nonlinear dispersive component decomposition, are heavily reliant on prior information such as initial group delay (GD), bandwidth parameters, and the number of modes. This reliance limits their adaptability in mechanical vibration signal analysis, particularly in extracting close dispersion features. To address these issues, this paper proposes a novel data-driven approach termed adaptive generalized dispersive mode decomposition (AGDMD). First, a frequency-varying low-pass filter based on dispersion compensation is constructed and then a fully data-driven GD estimation algorithm is proposed to determine effective initial GD. Subsequently, an adaptive bandwidth estimation strategy guided by initial GDs is devised to initialize and refine bandwidth parameters of the optimal dispersion compensation algorithm. Finally, according to the initial GDs and bandwidth parameters, the AGDMD adopts a recursive framework to sequentially extract nonlinear dispersive modes from the original signal. Simulated examples and real-life applications to railway fault diagnosis and Lamb wave analysis demonstrate that the proposed AGDMD has both high adaptability and good noise robustness, and can accurately extract close dispersive modes without excessive prior information.
期刊介绍:
The Journal of Sound and Vibration (JSV) is an independent journal devoted to the prompt publication of original papers, both theoretical and experimental, that provide new information on any aspect of sound or vibration. There is an emphasis on fundamental work that has potential for practical application.
JSV was founded and operates on the premise that the subject of sound and vibration requires a journal that publishes papers of a high technical standard across the various subdisciplines, thus facilitating awareness of techniques and discoveries in one area that may be applicable in others.