Intelligent fault diagnosis of rolling bearings using variational mode decomposition and self-organizing feature map. (6th March 2020)

• Record Type: Journal Article
• Authors: Zhang, Jialing; Wu, Jimei; Hu, Bingbing; Tang, Jiahui
• Is Part Of: Journal of vibration and control. Volume 26:Number 21/22(2020)
• Page Start: 1886
• Page End: 1897
• Publication Date: 2020-03-06
• Subjects: Variational mode decomposition -- self-organizing feature map -- intelligent fault diagnosis -- cluster analysis -- rolling bearings; Vibration -- Periodicals; Damping (Mechanics) -- Periodicals; 620.3
• DOI: 10.1177/1077546320911484
• Languages: English
• ISSNs: 1077-5463

Abstract

Rotating machinery contains numerous rolling bearings, which are critical for ensuring the normal working position and accurate operation of individual shaft systems. However, damage to rolling bearings can change their damping, stiffness, and elastic force. As a result, fault signals appear nonlinear and nonstationary. Vibration signals thus become difficult to diagnose clearly, especially in the incipient fault stage. To solve this problem, this article proposes an intelligent approach based on variational mode decomposition and the self-organizing feature map for rolling bearing fault diagnosis. First, the intrinsic mode function components of rolling bearing vibration signals are effectively separated by variational mode decomposition. Then, permutation entropy is used to extract feature vectors, which are used as training and testing data for the self-organizing feature map network. Finally, the various fault types of states are clustered on an intuitive visualization map. Clustering results of the experimental signal and the measured signal prove that the proposed method can successfully extract and cluster the rolling bearing faults in engineering applications. The proposed method improves the fault recognition rate to some extent over traditional methods.