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 contribution

7 Citations (Scopus)

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 publication2014 14th International Conference on Control, Automation and Systems (ICCAS 2014)
Place of PublicationSeoul, South Korea
Pages561-566
DOIs
Publication statusPublished - 22 Oct 2014

Keywords

  • Signal-to-noise ratio (SNR)
  • Inchoate fault detection
  • Principal component analysis (PCA)
  • Ball bearing fault

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