Machine performance degradation assessment and remaining useful life prediction using proportional hazard model and support vector machine

Machine performance degradation assessment and remaining useful life (RUL) prediction are of crucial importance in condition-based maintenance to reduce the maintenance cost and improve the reliability. They provide a potent tool for operators in decision-making by specifying the present machine state and estimating the remaining time. For this ultimate purpose, a three-stage method for assessing the machine health degradation and forecasting the RUL is proposed. In the first stage, only the normal operating condition of machine is used to create identification model for recognizing the dynamic system behavior. Degradation index which is used for indicating the machine degradation is subsequently created based on the root mean square of residual errors. These errors are the difference between identification model and behavior of system. In the second stage, the Cox’s proportional hazard model is generated to estimate the survival function of the system. In the last stage, support vector machine, which is one of the remarkable machine learning techniques, in association with time-series techniques is utilized to forecast the RUL. The data of low methane compressor acquired from condition monitoring routine is used for validating the proposed method. The result shows that the proposed method could be used as a reliable tool to machine prognostics.

► A novel method for assessing the machine degradation using only normal condition.
► Combine ARMA, PHM, and SVM with prediction techniques for estimating the RUL.
► The proposed system is verified with real industrial data.