Neural networks for condition monitoring and fault diagnosis: the effect of training data on classifier performance

CR Parikh, MJ Pont, Yuhua Li, NB Jones

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    This paper focuses on the development of neural-based condition-monitoring and fault-diagnosis (CMFD) systems. Specifically, we consider the impact of the limited availability of `faulty' training data in real CMFD applications. Where limited data are available we demonstrate two ways in which performance may, in some circumstances, be improved: (1) by using fewer training data made up of roughly equal numbers of,normal' and `fault' samples; or (2) by using a `duplicate-data' training algorithm.
    Original languageEnglish
    Title of host publicationUnknown Host Publication
    Pages237-243
    Number of pages7
    Publication statusPublished - 1999
    EventCONDITION MONITORING `99, PROCEEDINGS -
    Duration: 1 Jan 1999 → …

    Conference

    ConferenceCONDITION MONITORING `99, PROCEEDINGS
    Period1/01/99 → …

    Keywords

    • neural networks
    • condition monitoring
    • fault diagnosis
    • software design

    Fingerprint Dive into the research topics of 'Neural networks for condition monitoring and fault diagnosis: the effect of training data on classifier performance'. Together they form a unique fingerprint.

    Cite this