A Classification and Regression Trees (CART) Model of Parallel Structure and Long-term Prediction Prognosis of Machine Condition

Abstract

This paper presents a combined prediction model involving the parallel of classification and regression trees (CART) model, namely p-CART, and a long-term direct prediction methodology of time series techniques to predict the future stages of the machine's operating conditions. p-CART model consists of multiple CART models which are connected in parallel. Each submodel in the p-CART is trained independently. Based on the observations, these sub-models are subsequently used to predict the future values of the machine's operating conditions separately with the same embedding dimension but the different observations' indices. Finally, the predicted results of sub-models are combined to produce the final results of the predicting process. Real trending data acquired from condition monitoring routine of low methane compressor are employed for evaluating the proposed method. A comparative study of the predicted results obtained from traditional CART and p-CART models is also carried out to appraise the prediction capability of proposed model. In addition, a further improvement in predicting capability of p-CART is proposed to ameliorate the accuracy and efficiency of this method.