Machine condition prognosis using multi-step ahead prediction and neuro-fuzzy systems

V.T. Tran, Bo-Suk Yang

    Research output: Contribution to conferencePaper

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    Abstract

    This paper presents an approach to predict the operating conditions of machine based on adaptive neuro-fuzzy inference system (ANFIS) in association with direct prediction strategy for multi-step ahead prediction of time series techniques. In this study, the number of available observations and the number of predicted steps are initially determined by using false nearest neighbor method and auto mutual information technique, respectively. These values are subsequently utilized as inputs for prediction models to forecast the future values of the machine’s operating conditions. The performance of the proposed approach is then evaluated by using real trending data of low methane compressor. The results show that the ANFIS prediction model can track the change in machine conditions and has the potential for using as a tool to machine fault prognosis.
    Original languageEnglish
    Number of pages6
    Publication statusPublished - 2008
    EventInternational Symposium on Advanced Mechanical and Power Engineering, ISAMPE 2008 - Pukyoung National University, Busan, Korea, Republic of
    Duration: 9 Oct 200812 Oct 2008

    Conference

    ConferenceInternational Symposium on Advanced Mechanical and Power Engineering, ISAMPE 2008
    Abbreviated titleISAMPE
    CountryKorea, Republic of
    CityBusan
    Period9/10/0812/10/08

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  • Cite this

    Tran, V. T., & Yang, B-S. (2008). Machine condition prognosis using multi-step ahead prediction and neuro-fuzzy systems. Paper presented at International Symposium on Advanced Mechanical and Power Engineering, ISAMPE 2008, Busan, Korea, Republic of.