Data-driven approach to machine condition prognosis using least square regression tree

Machine fault prognosis techniques have been profoundly considered in the recent time due to their substantial profit for reducing unexpected faults or unscheduled maintenance. With those techniques, the working conditions of components, the trending of fault propagation, and the time-to-failure are precisely forecasted before they reach the failure thresholds. In this work, we propose the least square regression tree (LSRT) approach, which is an extension of the classification and regression tree (CART), in association with one-step-ahead prediction of time-series forecasting techniques to predict the future machine condition. In this technique, the number of available observations is first determined by using Cao’s method and LSRT is employed as a prediction model in the next step. The proposed approach is evaluated by real data of a low methane compressor. Furthermore, a comparative study of the predicted results obtained from CART and LSRT are carried out to prove the accuracy. The predicted results show that LSRT offers the potential for machine condition prognosis.