Weak fault signature identification of rolling bearings based on improved adaptive compressed sensing method. (15th June 2021)

• Record Type: Journal Article
• Authors: Zhang, Jianyu; Wang, Guofeng
• Is Part Of: Measurement science & technology. Volume 32:Number 10(2021)
• Publication Date: 2021-06-15
• Subjects: rolling bearing -- compressed sensing -- adaptive sparse representation -- improved SWOMP -- weak fault diagnosis; 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/ac0560
• Languages: English
• ISSNs: 0957-0233

Abstract

Abstract: In order to extract the weak fault feature mixed in the background noise and escape the constraints of traditional sampling theorems on large-capacity data collection, transmission and storage, an improved compressed sensing (CS) algorithm, in which an adaptive sparse representation based on learning dictionary and an improved reconstruction scheme are integrated, is presented in this paper. The adaptive sparse representation method includes the K-singular value decomposition based dictionary learning algorithm, and the stagewise weak orthogonal matching pursuit reconstruction is improved by the regularization principle, backtracking mechanism, adaptive mechanism and relevant stop criteria. The compressive measurement was completed through Gaussian random matrix. One simulation signal and two different experimental data sets of rolling bearing are utilized to validate the effectiveness of the proposed method, and the results have shown that the reconstruction accuracy and calculation efficiency can be improved simultaneously. Most importantly, the weak fault feature shaded by strong background noise can be effectively identified, which cannot be obtained by traditional CS method.