This article 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 sub-model 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 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 compressor are employed to evaluate 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 the proposed model.
- Machine fault prognosis
- Long-term time series prediction
- Direct prediction methodology