Prognosis and health management depend on sufficient prior knowledge of the degradation process critical components to predict remaining useful life. This task is composed two phases: learning prediction. The first phase uses available information learn system’s behavior. second predicts future behavior based system estimates its lifetime. Deep approaches achieve good prognostic performance but...

The prediction technology of remaining useful life has received a lot attention to ensure the reliability and stability complex mechanical equipment. Due large-scale, non-linear, high-dimensional characteristics monitoring data, machine learning does not need an exact physical model prior expert knowledge. It robust data processing ability, which shows broad prospect in field electrical Therefo...