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Data Mining for Gearbox Condition Monitoring

Baqqar, Mabrouka, Ahmed, Mahmud and Gu, Fengshou (2011) Data Mining for Gearbox Condition Monitoring. In: Proceedings of the 17th International Conference on Automation & Computing. Chinese Automation and Computing Society, Huddersfield. ISBN 978-1-86218-098-7

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    Abstract

    Engineering datasets have growing rapidly in size and diversity as data acquisition technology has developed in recent years. However, the full use of the datasets for maximizing machine operation and design has not been investigated systematically because of the complexity of the datasets and huge amounts of data. This also means that data analysis based on traditional statistic based methods are no longer efficient in obtaining useful knowledge from these datasets.
    Thus this paper discusses dynamic and static datasets collected from a gearbox test rig with a typical drive system such that the datasets are considered representative for condition monitoring purposes. Dynamic datasets were
    analyzed to diagnose the condition of the gear: Healthy or Fault, using conventional signal processing techniques such as time-domain and frequency-domain analysis. The static data was also analyzed for comparative evaluation of
    detection performances.

    This procedure of data collection and analysis allowed a full understanding to be gained of condition monitoring datasets
    and paved the way for developing a more effective Data mining approach and efficient database.
    Moreover, to evaluate the effectiveness of using these new techniques, a prototype database was developed based on a gearbox test system and tested using these methods. The results obtained from a number of conventional methods have shown that data mining can obtain information for condition monitoring efficiently but not so accurately to give fault severity information, which is often sufficient for making maintenance decisions.

    Item Type: Book Chapter
    Additional Information: Gearbox condition monitoring, Data mining methods, Conventional methods
    Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
    T Technology > TJ Mechanical engineering and machinery
    Schools: School of Computing and Engineering
    School of Computing and Engineering > Automotive Engineering Research Group
    School of Computing and Engineering > Diagnostic Engineering Research Centre
    School of Computing and Engineering > Diagnostic Engineering Research Centre > Energy, Emissions and the Environment Research Group
    School of Computing and Engineering > Diagnostic Engineering Research Centre > Machinery Condition and Performance Monitoring Research Group
    School of Computing and Engineering > Diagnostic Engineering Research Centre > Measurement System and Signal Processing Research Group
    School of Computing and Engineering > Informatics Research Group
    School of Computing and Engineering > Informatics Research Group > XML, Database and Information Retrieval Research Group
    Related URLs:
    Depositing User: Cherry Edmunds
    Date Deposited: 16 Sep 2011 13:51
    Last Modified: 09 Aug 2012 16:28
    URI: http://eprints.hud.ac.uk/id/eprint/11490

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