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

Tran, Van Tung (2008) Machine condition prognosis using multi-step ahead prediction and neuro-fuzzy systems. In: International Symposium on Advanced Mechanical and Power Engineering, 2008, Busan, Korea.

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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.

Item Type: Conference or Workshop Item (Paper)
Subjects: T Technology > TJ Mechanical engineering and machinery
Schools: School of Computing and Engineering
School of Computing and Engineering > Diagnostic Engineering Research Centre
Depositing User: Van Tran
Date Deposited: 07 Feb 2013 13:48
Last Modified: 08 Dec 2016 07:00
URI: http://eprints.hud.ac.uk/id/eprint/16568

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