Remaining useful life (RUL) prediction is one of key technologies to realize prognostics and health management that is being widely applied in many industrial systems to ensure high system availability over their life cycles. The present work proposes a data-driven method of RUL prediction based on multiple health state assessment for rolling element bearings. Instead of finding a unique RUL
prediction model, the life cycle of bearings is clustered into three health states: the normal state, the degradation state, and the failure state. A local RUL prediction model is separately built in each health state. Support vector machine is the technology to implement both health state assessment (classification) and RUL prediction modeling (regression). Experimental results on two accelerated life tests of rolling element bearings demonstrate the effectiveness of the proposed method.