Machinery fault diagnosis-oriented regularization for nonlinear system identification: Framework and applications

作     者：Zhao, Yulai Liu, Zepeng Yang, Zhiwei Han, Qingkai Ma, Hui 

作者机构：Northeastern Univ Sch Mech Engn & Automat Shenyang 110819 Liaoning Peoples R China Newcastle Univ Sch Engn Newcastle NE1 7RU England 

出 版 物：《APPLIED ACOUSTICS》 (Appl Acoust)

年 卷 期：2025年第231卷

核心收录：

学科分类：07[理学] 082403[工学-水声工程] 08[工学] 070206[理学-声学] 0824[工学-船舶与海洋工程] 0702[理学-物理学] 

基　　金：National Natural Science Foundation of China 

主　　题：Nonlinear system identification Regularization Nonlinear output frequency response functions (NOFRFs) Fault diagnosis (FD) 

摘      要：In this article, we propose a novel framework for machinery fault diagnosis based on nonlinear system identification, called Identification for Fault Diagnosis (I4FD) The focus and necessity of the framework is that it can mitigate the effects of external environmental changes and enhance diagnostic accuracy. The framework integrates regularized data-driven modeling and frequency analysis. During the modeling process, prior physical knowledge about the diagnostic target is incorporated through a penalty parameter, leading to fault diagnosisoriented regularization (FDoR). FDoR tailors the model specifically for fault diagnosis (FD) applications, offering new insights into FD-oriented system identification. The regularized NARX modeling in this paper does not end when a model is built by using information in a period of time, but uses the updated data for continuous dynamic modeling. After the model is identified, frequency analysis is then used to extract model-based features, which change significantly when faults occur. The effectiveness of the I4FD framework is demonstrated through simulations and real cases, highlighting its advantages over traditional methods and its industrial potential.