An Uncertain Random Process-Based Degradation Model for Remaining Useful Life Prediction Considering Triple Uncertainty

This paper proposes a novel degradation model and remaining useful life prediction (RUL) prediction framework to quantify the aleatory, epistemic and measurement uncertainty of complex devices. First, an uncertain random process-based degradation model is established to describe the implicit degradation features of the device. Then, the prior parameters are identified by the stochastic uncertain maximum likelihood estimation (SUMLE) method. Afterward, a similarity-based weighted SUMLE method is proposed to update the uncertain parameter and combined with the Kalman filter to update the implicit degradation states. Finally, the approximate analytical expression for the probability density function of RUL under triple uncertainty is derived to achieve RUL prediction dynamically. The effectiveness of the proposed method is validated by the GaAs laser degradation dataset.