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Diagnostics of Slow Rotating Bearings Using a Novel DAI Based on Acoustic Emission

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Book cover Advances in Condition Monitoring of Machinery in Non-Stationary Operations (CMMNO 2014)

Part of the book series: Applied Condition Monitoring ((ACM,volume 4))

Abstract

This study develops a novel integrated non-linear method for the effective feature extraction from an acoustic emission (AE) signal and the construction of a degradation assessment index (DAI) which is subsequently used for the fault diagnostics of slow rotating bearings. A slow rotating bearing test rig was developed to measure AE data under variable operational conditions. The aim of the study was to detect incipient damage and develop diagnostics which would be robust under changing operating conditions. The proposed model consists of a combination of polynomial kernel principal component analysis (PKPCA), a Gaussian mixture model (GMM) and an exponentially weighted moving average (EWMA). The proposed novel DAI is shown to be effective and suitable for monitoring the degradation of slow rotating bearings under investigation and is robust under variable operating conditions.

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Author information

Authors and Affiliations

  1. Centre for Asset Integrity Management, Department of Mechanical and Aeronautical Engineering, University of Pretoria, Pretoria, 0002, South Africa

    Sylvester A. Aye, P. Stephan Heyns & Coenie J. H. Thiart

Authors
  1. Sylvester A. Aye
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  2. P. Stephan Heyns
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  3. Coenie J. H. Thiart
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Corresponding author

Correspondence to Sylvester A. Aye .

Editor information

Editors and Affiliations

  1. National School of Engineers of Sfa, Sfax, Tunisia

    Fakher Chaari

  2. Diagnostics and Vibro-Acoustics Science Laboratory, Wrocław University of Technology Institute of Mining Engineering, Wroclaw, Poland

    Radoslaw Zimroz

  3. Diagnostics and Vibro-Acoustics Science Laboratory, Wrocław Univ of Technology Inst of Mining Engineering, Wroclaw, Poland

    Walter Bartelmus

  4. Mechanical Engineering Department, National School of Engineers of Sfa, Sfax, Tunisia

    Mohamed Haddar

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© 2016 Springer International Publishing Switzerland

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Aye, S.A., Heyns, P.S., Thiart, C.J.H. (2016). Diagnostics of Slow Rotating Bearings Using a Novel DAI Based on Acoustic Emission. In: Chaari, F., Zimroz, R., Bartelmus, W., Haddar, M. (eds) Advances in Condition Monitoring of Machinery in Non-Stationary Operations. CMMNO 2014. Applied Condition Monitoring, vol 4. Springer, Cham. https://doi.org/10.1007/978-3-319-20463-5_24

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  • DOI: https://doi.org/10.1007/978-3-319-20463-5_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-20462-8

  • Online ISBN: 978-3-319-20463-5

  • eBook Packages: EngineeringEngineering (R0)

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