Automated trend diagnosis using neural networks

Herath K U Samarasinghe, Shuji Hashimoto

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

    3 Citations (Scopus)

    Abstract

    This paper present a new method for trend diagnosis system using neural networks. Most of dynamical systems are not easy to analyze and detect faults because the observed parameters are not directly expressing the state of the system. We have to measure the temporal tendencies of the parameters, which is not easy not only for testing machine but also for human work. Here, the effectiveness of the trend fault diagnosis system using recurrent neural networks is examined for the air-conditioning system. The network was trained with the fault and correct data sequences obtained from the system simulation. The experimental fault detection results by using actual data proved that the proposed method is effective to perform the trend diagnosis of dynamic system.

    Original languageEnglish
    Title of host publicationProceedings of the IEEE International Conference on Systems, Man and Cybernetics
    PublisherIEEE
    Pages1186-1191
    Number of pages6
    Volume2
    Publication statusPublished - 2000
    Event2000 IEEE Interantional Conference on Systems, Man and Cybernetics - Nashville, TN, USA
    Duration: 2000 Oct 82000 Oct 11

    Other

    Other2000 IEEE Interantional Conference on Systems, Man and Cybernetics
    CityNashville, TN, USA
    Period00/10/800/10/11

    ASJC Scopus subject areas

    • Hardware and Architecture
    • Control and Systems Engineering

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