A sparse approach to fault severity classification for gearbox monitoring

Chuang Sun, Peng Wang, Ruqiang Yan, Robert X. Gao

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

13 Scopus citations

Abstract

Fault detection and severity classification are critical to gearbox structural health monitoring. A common approach to fault severity classification is to identify the patterns associated with features extracted from raw sensor data that vary with fault deterioration. Since however features only represent partial information contained in the raw data, they may indicate different interactions as faults deteriorate. Consequently, data fusion using inappropriate features may lead to decreased classification accuracy. To address this problem, a signal-level classifier based on raw sensor data through sparse representation is presented in this paper. A library containing covariance matrices calculated from the raw data corresponding to different faults or levels of fault severity is first constructed. Classification of new observations is then performed through a sparse representation of the library. A sparse mapping vector, as the result of sparse representation, reveals the classification result. Experimental study performed on a gearbox test rig with multiple vibration sensors installed is conducted to evaluate the effectiveness of the developed sparse classifier. Results confirmed good performance of the developed data fusion method for gearbox fault severity classification.

Original languageEnglish
Title of host publicationFUSION 2016 - 19th International Conference on Information Fusion, Proceedings
Pages2303-2308
Number of pages6
ISBN (Electronic)9780996452748
StatePublished - Aug 1 2016
Event19th International Conference on Information Fusion, FUSION 2016 - Heidelberg, Germany
Duration: Jul 5 2016Jul 8 2016

Publication series

NameFUSION 2016 - 19th International Conference on Information Fusion, Proceedings

Conference

Conference19th International Conference on Information Fusion, FUSION 2016
Country/TerritoryGermany
CityHeidelberg
Period7/5/167/8/16

Bibliographical note

Publisher Copyright:
© 2016 ISIF.

Keywords

  • data fusion
  • fault severity classification
  • kernel sparse representation

ASJC Scopus subject areas

  • Statistics, Probability and Uncertainty
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Signal Processing

Fingerprint

Dive into the research topics of 'A sparse approach to fault severity classification for gearbox monitoring'. Together they form a unique fingerprint.

Cite this