Improving signal-to-noise ratio (SNR) for inchoate fault detection based on principal component analysis (PCA)

Moussa Hamadache, Dongik Lee

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

8 Scopus citations

Abstract

Detection of inchoate fault demands high level of fault classification accuracy under poor signal-to-noise ratio (SNR) which appears in most industrial environment. Vibration signal analysis methods are widely used for bearing fault detection. In order to guarantee improved performance under poor SNR, feature extraction based on statistical parameters which are free from Gaussian noise become inevitable. This paper proposes a feature extraction framework based on principal component analysis (PCA) for improving SNR. Features extracted based on PCA have the tendency to alleviate the impact of non-Gaussian noise. PCA algorithm provides useful time domains analysis for no-stationary signals such as vibration in which spectral contents vary with respect to time. Experimental studies on vibration caused by ball bearing faults show that the proposed algorithm demonstrates the improvements in term of classification accuracy under poor signal-to-noise ratio (SNR).

Original languageEnglish
Title of host publicationInternational Conference on Control, Automation and Systems
PublisherIEEE Computer Society
Pages561-566
Number of pages6
ISBN (Electronic)9788993215069
DOIs
StatePublished - 16 Dec 2014
Event2014 14th International Conference on Control, Automation and Systems, ICCAS 2014 - Gyeonggi-do, Korea, Republic of
Duration: 22 Oct 201425 Oct 2014

Publication series

NameInternational Conference on Control, Automation and Systems
ISSN (Print)1598-7833

Conference

Conference2014 14th International Conference on Control, Automation and Systems, ICCAS 2014
Country/TerritoryKorea, Republic of
CityGyeonggi-do
Period22/10/1425/10/14

Keywords

  • ball bearing fault
  • inchoate fault detection
  • principal component analysis (PCA)
  • Signal-to-noise ratio (SNR)

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