Sparsity-based algorithm for detecting faults in rotating machines. (May 2016)

• Record Type: Journal Article
• Authors: He, Wangpeng; Ding, Yin; Zi, Yanyang; Selesnick, Ivan W.
• Is Part Of: Mechanical systems and signal processing. Volume 72/73(2016)
• Page Start: 46
• Page End: 64
• Publication Date: 2016-05
• Subjects: Rotating machinery -- Compound fault diagnosis -- Group sparsity denoising -- Non-convex optimization -- Sparse optimization -- Feature extraction; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2015.11.027
• Languages: English
• ISSNs: 0888-3270

Abstract

Abstract: This paper addresses the detection of periodic transients in vibration signals so as to detect faults in rotating machines. For this purpose, we present a method to estimate periodic-group-sparse signals in noise. The method is based on the formulation of a convex optimization problem. A fast iterative algorithm is given for its solution. A simulated signal is formulated to verify the performance of the proposed approach for periodic feature extraction. The detection performance of comparative methods is compared with that of the proposed approach via RMSE values and receiver operating characteristic (ROC) curves. Finally, the proposed approach is applied to single fault diagnosis of a locomotive bearing and compound faults diagnosis of motor bearings. The processed results show that the proposed approach can effectively detect and extract the useful features of bearing outer race and inner race defect. Abstract : Highlights: A method for estimating periodic-group-sparse signals in noise is proposed. The method is based on the formulation of a convex optimization problem. A fast iterative algorithm is illustrated. The detection performance is evaluated by RMSE values and ROC curves. Effectiveness is verified via simulation study and experimental data.