A summary of fault modelling and predictive health monitoring of rolling element bearings

Idriss El-Thalji, Erkki Jantunen

    Research output: Contribution to journalArticleScientificpeer-review

    163 Citations (Scopus)

    Abstract

    The rolling element bearing is one of the most critical components that determine the machinery health and its remaining lifetime in modern production machinery. Robust Predictive Health Monitoring tools are needed to guarantee the healthy state of rolling element bearing s during the operation. A Predictive Health Monitoring tool indicates the upcoming failures which provide sufficient lead time for maintenance planning. The Predictive Health Monitoring tool aims to monitor the deterioration i.e. wear evolution rather than just detecting the defects. The Predictive Health Monitoring procedures contain detection, diagnosis and prognosis analysis, which are required to extract the features related to the faulty rolling element bearing and estimate the remaining useful lifetime. The purpose of this study is to review the Predictive Health Monitoring methods and explore their capabilities, advantages and disadvantage in monitoring rolling element bearings. Therefore, the study provides a critical review of the Predictive Health Monitoring methods of the entire defect evolution process i.e. over the whole lifetime and suggests enhancements for rolling element bearing monitoring.
    Original languageEnglish
    Pages (from-to)252-272
    JournalMechanical Systems and Signal Processing
    Volume60-61
    DOIs
    Publication statusPublished - 2015
    MoE publication typeA1 Journal article-refereed

    Fingerprint

    Bearings (structural)
    Health
    Monitoring
    Machinery
    Defects
    Deterioration
    Wear of materials
    Planning

    Keywords

    • condition monitoring
    • signal analysis
    • diagnostics
    • prognosis
    • dynamic modelling
    • rolling bearings

    Cite this

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    A summary of fault modelling and predictive health monitoring of rolling element bearings. / El-Thalji, Idriss; Jantunen, Erkki.

    In: Mechanical Systems and Signal Processing, Vol. 60-61, 2015, p. 252-272.

    Research output: Contribution to journalArticleScientificpeer-review

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    AU - El-Thalji, Idriss

    AU - Jantunen, Erkki

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    AB - The rolling element bearing is one of the most critical components that determine the machinery health and its remaining lifetime in modern production machinery. Robust Predictive Health Monitoring tools are needed to guarantee the healthy state of rolling element bearing s during the operation. A Predictive Health Monitoring tool indicates the upcoming failures which provide sufficient lead time for maintenance planning. The Predictive Health Monitoring tool aims to monitor the deterioration i.e. wear evolution rather than just detecting the defects. The Predictive Health Monitoring procedures contain detection, diagnosis and prognosis analysis, which are required to extract the features related to the faulty rolling element bearing and estimate the remaining useful lifetime. The purpose of this study is to review the Predictive Health Monitoring methods and explore their capabilities, advantages and disadvantage in monitoring rolling element bearings. Therefore, the study provides a critical review of the Predictive Health Monitoring methods of the entire defect evolution process i.e. over the whole lifetime and suggests enhancements for rolling element bearing monitoring.

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    KW - signal analysis

    KW - diagnostics

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    KW - dynamic modelling

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