The Single-channel blind source separation based on VMD and Tukey M estimation for rolling bearing composite fault diagnosis

Abstract

Rolling bearing is one of the core components in rotating machinery, and its running status directly affects the operation of the whole equipment. Faults of rolling bearings in the actual working process are often multiple faults. To effectively separate fault sources, the blind source separation method is used for the compound fault diagnosis of rolling bearings. Because of the impact of the number of artificially limited decompositions and quadratic penalty factor on VMD in the decomposition process, and the slow convergence and low accuracy in the objective function of traditional FastICA operation, the VMD algorithm based on the energy loss coefficient and the information entropy is proposed, which adaptively determines the number of modal components and the quadratic penalty factor; The Tukey M estimation is selected as the objective convergence function of the FastICA algorithm to improve its robustness. First, VMD is used to decompose the signal; Secondly, the original signal and the decomposed IMF component are reconstructed, the covariance matrix and the singular value decomposition are constructed, the number of fault sources is estimated by the proximity dominance method, and the decomposed IMF components are filtered through correlation analysis and kurtosis index to build a multi-channel feature set; Finally, the constructed multi-channel feature set is input to the FastICA algorithm based on the Tukey M estimation for the separation of fault source signals to achieve composite fault diagnosis. The compound fault experiment shows that the proposed method in this paper can effectively realize the blind source separation of rolling bearing fault features to realize the compound fault diagnosis in different positions.

Core consistency blind source separation based on compressive sensing trilinear decomposition

“…Wang et al [26] proposed an algorithm based on cross-validation to estimate the dynamic number of signal sources. At present, source estimation methods have made great progress in the mechanical fault field [27][28][29]. However, in these methods, the number of sensors is required to be more than that of sources.…”

Adaptive local binarization feature mode decomposition and its application in combined failure identification of rolling bearings

“…The VMD algorithm decomposes the original signal into several modal components with distinct central frequencies using a Wiener filter bank. The efficacy of the decomposition of VMD hinges on the proper setting of modal number and penalty factor [20].…”