Underdetermined blind separation of bearing faults in hyperplane space with variational mode decomposition. (1st April 2019)

• Record Type: Journal Article
• Authors: Li, Guozheng; Tang, Gang; Luo, Ganggang; Wang, Huaqing
• Is Part Of: Mechanical systems and signal processing. Volume 120(2019)
• Page Start: 83
• Page End: 97
• Publication Date: 2019-04-01
• Subjects: Underdetermined blind source separation -- Matrix estimation -- Variational mode decomposition -- Normal vector of hyperplane; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2018.10.016
• Languages: English
• ISSNs: 0888-3270

Abstract

Highlights: The underdetermined BSS problem is considered and solved in hyperplane space. The source number is estimated by Bayesian information criterion with VMD. The normal vector of hyperplane with VMD is constructed to estimate the mixed matrix. The propose method in hyperplane space outperforms that in low-dimensional space. Abstract: In the health monitoring of rotating machinery, there often coexists multiple fault sources. Thus a multi-source compound fault signal will be excited and collected by sensors. Moreover, due to the practical limitations, the number of sensors is usually less than that of the source signals, which makes it an underdetermined blind source separation (BSS) problem to identify the fault signals. Because the observed signals are usually not sparse enough in low-dimension transform domains, it is not ideal to solve the underdetermined BSS problem in the low-dimension space with traditional methods. In this study, we solve the problem by exploring more effective features of the signal in the hyperplane space with variational mode decomposition. We construct the hyperplane from a variational mode decomposition of the compound fault signal, and develop a mixed matrix estimation model based on that in the hyperplane space. Simulation and experiments show that the proposed methodology can effectively separate the compound fault signals of rotating machinery and detect the fault features.