Sparsity-based algorithm for detecting faults in rotating machines

Wangpeng He, Yin Ding, Yanyang Zi, Ivan W. Selesnick

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)46-64
Number of pages19
JournalMechanical Systems and Signal Processing
StatePublished - May 1 2016


  • Compound fault diagnosis
  • Feature extraction
  • Group sparsity denoising
  • Non-convex optimization
  • Rotating machinery
  • Sparse optimization

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Signal Processing
  • Civil and Structural Engineering
  • Aerospace Engineering
  • Mechanical Engineering
  • Computer Science Applications


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