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. This paper was recommended for publication in revised form by Associate Editor Eung-Soo Shin Van Tung Tran is a lecturer at the Hochiminh City University of Technology in Vietnam. He received his B.S. and M.S. degrees in mechanical engineering from Hochiminh City University of Technology, Vietnam, in 1997 and 2003, respectively, and Ph.D. from Pukyong National University, South Korea in 2009. His research interests include machine fault diagnosis and condition prognosis. Bo-Suk Yang is a professor at the Puyong National University in Korea. He received his Ph.D. degree in mechanical engineering from Kobe University, Japan in 1985. His main research fields cover machine dynamics and vibration engineering, intelligent optimum design, and condition monitoring and diagnostics in rotating machinery. He has published well over 190 research papers in the research areas of vibration analysis, intelligent optimum design and diagnosis of rotating machinery. He is listed in Who’s Who in the World, Who’s Who in Science and Engineering, among others.