Stochastic Uncertain Degradation Modeling and Remaining Useful Life Prediction Considering Aleatory and Epistemic Uncertainty

Remaining useful life (RUL) prediction based on the degradation model is vital for the maintenance and management of complex equipment. It is necessary to describe the aleatory uncertainty in internal changes during equipment degradation and quantify the epistemic uncertainty caused by a lack of sufficient knowledge simultaneously. This article proposes a novel framework for the degradation model and RUL prediction with aleatory and epistemic uncertainties. First, the probability theory is combined with the uncertainty theory to establish a stochastic uncertain degradation model. Following that, a new stochastic uncertain maximum likelihood estimation (SUMLE) method is proposed based on probability function and uncertainty distribution to identify model parameters. The Bayesian inference is adopted to update the model parameters in the current time. Afterward, the above dual-source uncertainty is incorporated into RUL prediction. The distribution function of the RUL is derived, which can be updated in real-time according to the arrival of new degradation observations. Finally, the experimental studies on gallium arsenide (GaAs) laser and gyroscope degradation data are conducted to interpret the superiority of the proposed method over based on the stochastic process or uncertain process separately for predicting the RUL.

Automated summary

This article proposes a novel framework for degradation model and remaining useful life (RUL) prediction, taking into account both the aleatory uncertainty and epistemic uncertainty. The probability theory and uncertainty theory are used to establish a stochastic uncertain degradation model. A new stochastic uncertain maximum likelihood estimation (SUMLE) method is proposed to identify model parameters. Bayesian inference is used to update the model parameters. The proposed method is demonstrated to outperform methods based solely on stochastic process or uncertain process for RUL prediction through experimental studies on gallium arsenide (GaAs) laser and gyroscope degradation data.