The scale effect on condition monitoring of large scale rolling bearings

Idriss El-Thalji, Erkki Jantunen

    Research output: Contribution to conferenceConference articleScientificpeer-review

    Abstract

    The predictive maintenance is a key factor to maintain the industrial assets at the ultimate cost effective level. Predictive health monitoring tools are needed to guarantee the healthy state of critical machineries during the operation. Predictive health monitoring tools indicate the upcoming failures providing longer lead time for maintenance planning. The rolling element bearing is one of the most critical components that determine the machinery health and its remaining lifetime in modern production machinery. The scale of modern machineries is rapidly growing and leading to several critical lifetime challenges. At the same time, it is hard to monitor the faults of large scale bearings. Therefore, the purpose of this paper is to provide an illustration of the scale effect of the effectiveness of condition monitoring tools. It is shown that the scale ratio between the rolling element and the defect topology play a significant role in affecting the overall dynamic behaviour of the system beside the significant effect of the rotational speed. The study illustrates the difficulties to monitor large scale bearings. The benefit of such phenomenal illustration is to enhance the condition monitoring tools for large scale bearings.
    Original languageEnglish
    Number of pages10
    Publication statusPublished - 2014
    Event1st International Conference on Industrial, Systems and Manufacturing Engineering, ISME'14 - Amman, Jordan
    Duration: 11 Nov 201413 Nov 2014
    Conference number: 1

    Conference

    Conference1st International Conference on Industrial, Systems and Manufacturing Engineering, ISME'14
    Abbreviated titleISME'14
    CountryJordan
    CityAmman
    Period11/11/1413/11/14

    Fingerprint

    Bearings (structural)
    Condition monitoring
    Health
    Machinery
    Monitoring
    Topology
    Planning
    Defects
    Costs

    Keywords

    • condition monitoring
    • dynamic modelling
    • contact mechanics
    • wear evolution
    • rolling bearing

    Cite this

    El-Thalji, I., & Jantunen, E. (2014). The scale effect on condition monitoring of large scale rolling bearings. Paper presented at 1st International Conference on Industrial, Systems and Manufacturing Engineering, ISME'14, Amman, Jordan.
    El-Thalji, Idriss ; Jantunen, Erkki. / The scale effect on condition monitoring of large scale rolling bearings. Paper presented at 1st International Conference on Industrial, Systems and Manufacturing Engineering, ISME'14, Amman, Jordan.10 p.
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    title = "The scale effect on condition monitoring of large scale rolling bearings",
    abstract = "The predictive maintenance is a key factor to maintain the industrial assets at the ultimate cost effective level. Predictive health monitoring tools are needed to guarantee the healthy state of critical machineries during the operation. Predictive health monitoring tools indicate the upcoming failures providing longer lead time for maintenance planning. The rolling element bearing is one of the most critical components that determine the machinery health and its remaining lifetime in modern production machinery. The scale of modern machineries is rapidly growing and leading to several critical lifetime challenges. At the same time, it is hard to monitor the faults of large scale bearings. Therefore, the purpose of this paper is to provide an illustration of the scale effect of the effectiveness of condition monitoring tools. It is shown that the scale ratio between the rolling element and the defect topology play a significant role in affecting the overall dynamic behaviour of the system beside the significant effect of the rotational speed. The study illustrates the difficulties to monitor large scale bearings. The benefit of such phenomenal illustration is to enhance the condition monitoring tools for large scale bearings.",
    keywords = "condition monitoring, dynamic modelling, contact mechanics, wear evolution, rolling bearing",
    author = "Idriss El-Thalji and Erkki Jantunen",
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    El-Thalji, I & Jantunen, E 2014, 'The scale effect on condition monitoring of large scale rolling bearings', Paper presented at 1st International Conference on Industrial, Systems and Manufacturing Engineering, ISME'14, Amman, Jordan, 11/11/14 - 13/11/14.

    The scale effect on condition monitoring of large scale rolling bearings. / El-Thalji, Idriss; Jantunen, Erkki.

    2014. Paper presented at 1st International Conference on Industrial, Systems and Manufacturing Engineering, ISME'14, Amman, Jordan.

    Research output: Contribution to conferenceConference articleScientificpeer-review

    TY - CONF

    T1 - The scale effect on condition monitoring of large scale rolling bearings

    AU - El-Thalji, Idriss

    AU - Jantunen, Erkki

    N1 - Project code: 76753

    PY - 2014

    Y1 - 2014

    N2 - The predictive maintenance is a key factor to maintain the industrial assets at the ultimate cost effective level. Predictive health monitoring tools are needed to guarantee the healthy state of critical machineries during the operation. Predictive health monitoring tools indicate the upcoming failures providing longer lead time for maintenance planning. The rolling element bearing is one of the most critical components that determine the machinery health and its remaining lifetime in modern production machinery. The scale of modern machineries is rapidly growing and leading to several critical lifetime challenges. At the same time, it is hard to monitor the faults of large scale bearings. Therefore, the purpose of this paper is to provide an illustration of the scale effect of the effectiveness of condition monitoring tools. It is shown that the scale ratio between the rolling element and the defect topology play a significant role in affecting the overall dynamic behaviour of the system beside the significant effect of the rotational speed. The study illustrates the difficulties to monitor large scale bearings. The benefit of such phenomenal illustration is to enhance the condition monitoring tools for large scale bearings.

    AB - The predictive maintenance is a key factor to maintain the industrial assets at the ultimate cost effective level. Predictive health monitoring tools are needed to guarantee the healthy state of critical machineries during the operation. Predictive health monitoring tools indicate the upcoming failures providing longer lead time for maintenance planning. The rolling element bearing is one of the most critical components that determine the machinery health and its remaining lifetime in modern production machinery. The scale of modern machineries is rapidly growing and leading to several critical lifetime challenges. At the same time, it is hard to monitor the faults of large scale bearings. Therefore, the purpose of this paper is to provide an illustration of the scale effect of the effectiveness of condition monitoring tools. It is shown that the scale ratio between the rolling element and the defect topology play a significant role in affecting the overall dynamic behaviour of the system beside the significant effect of the rotational speed. The study illustrates the difficulties to monitor large scale bearings. The benefit of such phenomenal illustration is to enhance the condition monitoring tools for large scale bearings.

    KW - condition monitoring

    KW - dynamic modelling

    KW - contact mechanics

    KW - wear evolution

    KW - rolling bearing

    M3 - Conference article

    ER -

    El-Thalji I, Jantunen E. The scale effect on condition monitoring of large scale rolling bearings. 2014. Paper presented at 1st International Conference on Industrial, Systems and Manufacturing Engineering, ISME'14, Amman, Jordan.