Adaptive variational mode decomposition and its application to multi-fault detection using mechanical vibration signals. (May 2021)

• Record Type: Journal Article
• Authors: He, Xiuzhi; Zhou, Xiaoqin; Yu, Wennian; Hou, Yixuan; Mechefske, Chris K.
• Is Part Of: ISA transactions. Volume 111(2021)
• Page Start: 360
• Page End: 375
• Publication Date: 2021-05
• Subjects: Multi-fault detection -- Variational mode decomposition -- Artificial bee colony -- Envelope power spectrum analysis -- Mechanical vibration signals; Engineering instruments -- Periodicals; Engineering instruments; Periodicals; Electronic journals; 629.805
• DOI: 10.1016/j.isatra.2020.10.060
• Languages: English
• ISSNs: 0019-0578

Abstract

Abstract: Vibration-based feature extraction of multiple transient fault signals is a challenge in the field of rotating machinery fault diagnosis. Variational mode decomposition (VMD) has great potential for multiple faults decoupling because of its equivalent filtering characteristics. However, the two key hyper-parameters of VMD, i.e., the number of modes and balancing parameter, require to be predefined, thereby resulting in sub-optimal decomposition performance. Although some studies focused on the adaptive parameter determination, the problems in these improved methods like mode redundancy or being sensitive to random impacts still need to be solved. To overcome these drawbacks, an adaptive variational mode decomposition (AVMD) method is developed in this paper. In the proposed method, a novel index called syncretic impact index ( SII ) is firstly introduced for better evaluation of the complex impulsive fault components of signals. It can exclude the effects of interference terms and concentrate on the fault impacts effectively. The optimal parameters of VMD are selected based on the index SII through the artificial bee colony (ABC) algorithm. The envelope power spectrum, proved to be more capable for fault feature extraction than the envelope spectrum, is applied in this study. Analysis on simulated signals and two experimental applications based on the proposed method demonstrates its effectiveness over other existing methods. The results indicate that the proposed method outperforms in separating impulsive multi-fault signals, thus being an efficient method for multi-fault diagnosis of rotating machines. Highlights: A new syncretic impact index SII is proposed for detecting impulsive fault signals. The AVMD method is developed for multi-fault feature separation. Effectiveness and merits of AVMD are verified by simulated and experimental signals.