Abstract:
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 al...Show MoreMetadata
Abstract:
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.
Published in: IEEE Transactions on Instrumentation and Measurement ( Volume: 72)