Unsupervised Condition Change Detection In Large Diesel Engines

Niels Henrik Pontoppidan, Jan Larsen, C. Molina et al. (Editor)

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    Abstract

    This paper presents a new method for unsupervised change detection which combines independent component modeling and probabilistic outlier etection. The method further provides a compact data representation, which is amenable to interpretation, i.e., the detected condition changes can be investigated further. The method is successfully applied to unsupervised condition change detection in large diesel engines from acoustical emission sensor signal and compared to more classical techniques based on principal component analysis and Gaussian mixture models.
    Original languageEnglish
    Title of host publicationIEEE Workshop on Neural Networks for Signal Processing
    PublisherIEEE Press
    Publication date2003
    Pages565-574
    ISBN (Print)0-7803-8177-7
    Publication statusPublished - 2003
    EventIEEE 13th Workshop on Neural Networks for Signal Processing, 2003 -
    Duration: 1 Jan 2003 → …

    Conference

    ConferenceIEEE 13th Workshop on Neural Networks for Signal Processing, 2003
    Period01/01/2003 → …

    Bibliographical note

    Copyright: 2003 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

    Keywords

    • change detection
    • Independent component analysis
    • large diesel engines

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