Machine health prognostics using survival probability and support vector machine

Prognostic of machine health estimates the remaining useful life of machine components. It deals with prediction of machine health condition based on past measured data from condition monitoring (CM). It has benefits to reduce the production downtime, spare-parts inventory, maintenance cost, and safety hazards. Many papers have reported the valuable models and methods of prognostics systems. However, it was rarely found the papers deal with censored data, which is common in machine condition monitoring practice. This work concerns with developing intelligent machine prognostics system using survival analysis and support vector machine (SVM). SA utilizes censored and uncensored data collected from CM routine and then estimates the survival probability of failure time of machine components. SVM is trained by data input from CM histories data that corresponds to target vectors of estimated survival probability. After validation process, SVM is employed to predict failure time of individual unit of machine component. Simulation and experimental bearing degradation data are employed to validate the proposed method. The result shows that the proposed method is promising to be a probability-based machine prognostics system.

Research highlights
► Survival analysis and SVM are utilized to develop machine prognostics.
► Failure time of bearing are estimated by survival probability.
► Experimental bearing degradation is employed to validate the proposed method.
► The trained SVM is used to predict failure time of individual bearing.