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Multi-step ahead direct prediction for machine condition prognosis using regression trees and neuro-fuzzy systems

Tran, Van Tung, Yang, Bo-Suk and Tan, Andy Chit Chiow (2009) Multi-step ahead direct prediction for machine condition prognosis using regression trees and neuro-fuzzy systems. Expert Systems With Applications, 36 (5). pp. 9378-9387. ISSN 0957-4174

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Abstract

This paper presents an approach to predict the operating conditions of machine based on
classification and regression trees (CART) and 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 machines’ operating conditions. The performance of the proposed approach is then evaluated by using real trending data of low methane compressor. A comparative study of the predicted results obtained from CART and ANFIS models is also carried out to appraise the prediction capability of these models. 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: Article
Subjects: T Technology > TJ Mechanical engineering and machinery
Schools: School of Computing and Engineering > Diagnostic Engineering Research Centre
School of Computing and Engineering
Depositing User: Van Tran
Date Deposited: 31 Jan 2013 12:28
Last Modified: 06 Dec 2016 00:02
URI: http://eprints.hud.ac.uk/id/eprint/16577

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