Adaptive single-mode variational mode decomposition and its applications in wheelset bearing fault diagnosis. (1st December 2022)

• Record Type: Journal Article
• Authors: Li, Cuixing; Liu, Yongqiang; Liao, Yingying; Liu, Wenpeng
• Is Part Of: Measurement science & technology. Volume 33:Number 12(2022)
• Publication Date: 2022-12-01
• Subjects: VMD -- PSO -- U-shaped convergence region -- initial center frequency -- balancing parameter; Physical measurements -- Periodicals; Scientific apparatus and instruments -- Periodicals; Equipment and Supplies -- Periodicals; Science -- instrumentation -- Periodicals; Technology -- instrumentation -- Periodicals; Mesures physiques -- Périodiques; Physical measurements; Scientific apparatus and instruments; Periodicals; 502.87
• DOI: 10.1088/1361-6501/ac856c
• Languages: English
• ISSNs: 0957-0233

Abstract

Abstract: In recent years, many studies on variational mode decomposition (VMD) have mainly focused on choosing the number of modes and balancing parameter, while less research focuses on the internal properties of VMD. This paper proposes an adaptive single-mode VMD (ASMVMD) method based on the convergence characteristics of VMD and the adaptivity of particle swarm optimization (PSO). Firstly, we study the convergence characteristics of single-mode VMD and find that the U-shaped convergence region related to fault impact is very wide in the whole frequency domain. Secondly, based on the characteristics of the U-shaped convergence region, a new population position initialization strategy is proposed. Finally, the improved PSO is used to optimize the initial center frequency and balancing parameter of single-mode VMD. The effectiveness of the proposed method is verified by analyzing the simulated signal and wheelset bearing fault signals. Compared with the fast kurtogram and Autogram, it is shown that ASMVMD has a stronger capability of fault feature extraction.