A review on the application of blind source separation in vibration analysis of mechanical systems

Vibration is a widespread phenomenon in mechanical systems, and vibration analysis is imperative for assessing the dynamic characteristics of mechanical structures. Vibration analysis extensively relies on analyzing the vibration signals. Blind source separation (BSS) was introduced as a pattern separation technique in vibration signal analysis in the 21st century. BSS has provided innovative solutions for various vibration problems by effectively recovering desired source signals from mixed signals despite limited prior information. This paper reviews the application research of BSS in vibration analysis in the past decade, focusing on detailed explanations in the areas of fault diagnosis, operational modal analysis (OMA), and load identification. Firstly, the paper presents the fundamental theory of BSS, encompassing several prominent algorithms: independent component analysis (ICA), non-negative matrix factorization (NMF), sparse component analysis (SCA), bounded component analysis (BCA) and deep learning-based BSS. Subsequently, corresponding to the three key elements of structural dynamics, namely, response, system, and excitation, the paper categorizes and organizes the research on the application of BSS in the areas of fault diagnosis, OMA, and load identification, summarizing the methodological approaches in relevant literature for specific problems in practical applications. Finally, the paper presents a future perspective on the application prospects of BSS in vibration analysis.