Effects of compressed sensing on classification of bearing faults with entropic features

M. L. Dennis Wong, M. Zhang, Asoke K. Nandi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

14 Citations (Scopus)


The ability of automatically determining the underlying fault type in-situ for a roller element bearing is highly desired in machine condition monitoring applications nowadays. In this paper, we classify roller element fault types under a compressed sensing framework. Firstly, vibration signals of roller element bearings are acquired in the time domain and resampled with a random Bernoulli matrix to emulate the compressed sensing mechanism. Sample entropy based features are then computed for both the normalized raw vibration signals and the reconstructed compressed sensed signals. Classification performance using Support Vector Machine (SVM) shows slight per formance degradation with significant reduction of the bandwidth requirement.

Original languageEnglish
Title of host publication2015 23rd European Signal Processing Conference, EUSIPCO 2015
Number of pages5
ISBN (Electronic)9780992862633
Publication statusPublished - Dec 2015
Event23rd European Signal Processing Conference 2015 - Nice, France
Duration: 31 Aug 20154 Sept 2015


Conference23rd European Signal Processing Conference 2015
Abbreviated titleEUSIPCO 2015


  • Bearing Fault Classification
  • Com pressed Sensing
  • Machine Condition Monitoring
  • Sample Entropy

ASJC Scopus subject areas

  • Media Technology
  • Computer Vision and Pattern Recognition
  • Signal Processing


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