Compressive sensing strategy for classification of bearing faults

H. O. A. Ahmed, M. L. Dennis Wong, A. K. Nandi

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

10 Citations (Scopus)

Abstract

Owing to the importance of rolling element bearings in rotating machines, condition monitoring of rolling element bearings has been studied extensively over the past decades. However, most of the existing techniques require large storage and time for signal processing. This paper presents a new strategy based on compressive sensing for bearing faults classification that uses fewer measurements. Under this strategy, to match the compressed sensing mechanism, the compressed vibration signals are first obtained by resampling the acquired bearing vibration signals in the time domain with a random Gaussian matrix using different compressed sensing sampling rates. Then three approaches have been chosen to process these compressed data for the purpose of bearing fault classification these includes using the data directly as the input of classifier, and extract features from the data using linear feature extraction methods, namely, unsupervised Principal Component Analysis (PCA) and supervised Linear Discriminant Analysis (LDA). Classification performance using Logistic Regression Classifier (LRC) achieved high classification accuracy with significantly reduced bandwidth consumption compared with the existing techniques.

Original languageEnglish
Title of host publication2017 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)
PublisherIEEE
Pages2182-2186
Number of pages5
ISBN (Electronic)9781509041176
DOIs
Publication statusPublished - 19 Jun 2017
Event42nd IEEE International Conference on Acoustics, Speech, and Signal Processing 2017 - New Orleans, United States
Duration: 5 Mar 20179 Mar 2017

Publication series

NameIEEE International Conference on Acoustics, Speech, and Signal Processing
PublisherIEEE
ISSN (Electronic)2379-190X

Conference

Conference42nd IEEE International Conference on Acoustics, Speech, and Signal Processing 2017
Abbreviated titleICASSP 2017
CountryUnited States
CityNew Orleans
Period5/03/179/03/17

Keywords

  • Bearing Fault Classification
  • Compressive Sensing
  • Linear Discriminant Analysis
  • Machine Condition Monitoring
  • Principal Component Analysis

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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  • Cite this

    Ahmed, H. O. A., Wong, M. L. D., & Nandi, A. K. (2017). Compressive sensing strategy for classification of bearing faults. In 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) (pp. 2182-2186). (IEEE International Conference on Acoustics, Speech, and Signal Processing). IEEE. https://doi.org/10.1109/ICASSP.2017.7952543