Stochastic methodology for prognostics under continuously varying environmental profiles

摘要

Abstract We present a stochastic modeling framework for sensor-based degradation signals that predicts, in real time, the residual lifetime of individual components subjected to a time-varying environment. We investigate the future environmental profile that is deterministic and evolves continuously. Unique to our model is the union of historical data with real-time sensor-based data to update the degradation model and the residual life distribution (RLD) of the component within a Bayesian fram.