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Rail Wagon Bearings Health Management Based on Imperfect Acoustic Information

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Engineering Asset Management 2011

Part of the book series: Lecture Notes in Mechanical Engineering ((LNME))

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Abstract

In this paper we develop and discuss a prognostics model to estimate the Mean Residual Life of Rail Wagon Bearings within certain confidence intervals. The prognostics model is constructed using a Proportional Hazards Model approach informed by imperfect data from a bearing acoustic monitoring system and related failure database. We have been able to predict failure within a defined maintenance planning window from the receipt of the latest acoustic condition monitoring information. We use the model to decide whether to replace a bearing or leave it until collection of the next condition monitoring indicators. The model is tested on a limited number of cases and demonstrates good predictive capability. Opportunities to improve the performance of the model are identified.

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Author information

Authors and Affiliations

  1. Industrial Engineering Department, Dalhousie University, 5269 Morris Street, Halifax, NS, B3J 2X4, Canada

    A. Ghasemi

  2. Mechanical Engineering, University of Western Australia, M050, Crawley, WA, 6009, Australia

    M. R. Hodkiewicz

Authors
  1. A. Ghasemi
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  2. M. R. Hodkiewicz
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Corresponding author

Correspondence to M. R. Hodkiewicz .

Editor information

Editors and Affiliations

  1. Dept. Mechanical, Industrial & Nuclear Engineering (MINE), University of Cincinnati College of Engineering, Cincinnati, Ohio, USA

    Jay Lee

  2. University of Michigan Dept. Mechanical Engineering, Ann Arbor, Michigan, USA

    Jun Ni

  3. Missouri University of Technology, Rolla, USA

    Jagnathan Sarangapani

  4. Management (CIEAM), Queensland University of Technology Centre for Integrated Engineering Asset, Brisbane, Queensland, Australia

    Joseph Mathew

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© 2014 Springer-Verlag London

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Ghasemi, A., Hodkiewicz, M.R. (2014). Rail Wagon Bearings Health Management Based on Imperfect Acoustic Information. In: Lee, J., Ni, J., Sarangapani, J., Mathew, J. (eds) Engineering Asset Management 2011. Lecture Notes in Mechanical Engineering. Springer, London. https://doi.org/10.1007/978-1-4471-4993-4_16

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  • DOI: https://doi.org/10.1007/978-1-4471-4993-4_16

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  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-4992-7

  • Online ISBN: 978-1-4471-4993-4

  • eBook Packages: EngineeringEngineering (R0)

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