Machine condition prognosis based on regression trees and one-step-ahead prediction

Predicting degradation of working conditions of machinery and trending of fault propagation before they reach the alarm or failure threshold is extremely importance in industry to fully utilize the machine production capacity. This paper proposes a method to predict future conditions of machines based on one-step-ahead prediction of time-series forecasting techniques and regression trees. In this study, the embedding dimension is firstly estimated in order to determine the necessary available
observations for predicting the next value in the future. This value is subsequently utilized for
regression tree predictor. Real trending data of low methane compressor acquired from condition
monitoring routine are employed for evaluating the proposed method. The results indicate that the
proposed method offers a potential for machine condition prognosis