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

Authors:
Van Tung Tran;Bo-Suk Yang;Andy Chit Chiow Tan
Affiliations:
School of Mechanical Engineering, Pukyong National University, San 100, Yongdang-dong, Nam-gu, Busan 608-739, South Korea;School of Mechanical Engineering, Pukyong National University, San 100, Yongdang-dong, Nam-gu, Busan 608-739, South Korea;School of Mechanical, Manufacturing and Medical Engineering, Queensland University of Technology, G.P.O. Box 2343, Brisbane, Qld. 4001, Australia
Venue:
Expert Systems with Applications: An International Journal
Year:
2009

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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.