Non-stationary condition monitoring through event alignment

Niels Henrik Pontoppidan, Jan Larsen

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    We present an event alignment framework which enables change detection in non-stationary signals. change detection. Classical condition monitoring frameworks have been restrained to laboratory settings with stationary operating conditions, which are not resembling real world operation. In this paper we apply the technique for non-stationary condition monitoring of large diesel engines based on acoustical emission sensor signals. The performance of the event alignment is analyzed in an unsupervised probabilistic detection framework based on outlier detection with either Principal Component Analysis or Gaussian Processes modeling. We are especially interested in the true performance of the condition monitoring performance with mixed aligned and unaligned data, e.g. detection of fault condition of unaligned examples versus false alarms of aligned normal condition data. Further, we expect that the non-stationary model can be used for wear trending due to longer and continuous monitoring across operating condition changes.
    Original languageEnglish
    Title of host publicationIEEE Workshop on Machine Learning for Signal Processing
    PublisherIEEE Press
    Publication date2004
    ISBN (Print)0-7803-8608-4
    Publication statusPublished - 2004
    Event14th IEEE Signal Processing Society Workshop Machine Learning for Signal Processing -
    Duration: 1 Jan 2004 → …


    Conference14th IEEE Signal Processing Society Workshop Machine Learning for Signal Processing
    Period01/01/2004 → …

    Bibliographical note

    Copyright: 2004 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE


    • Condition Monitoring
    • Non-stationarity
    • Diesel Engine

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