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This brief 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.

For a category of complex equipment with multiphase degradation characters subject to changes in internal mechanisms or environmental stresses, existing remaining useful life (RUL) prediction methods only consider aleatory uncertainty but ignore epistemic uncertainty caused by the lack of data and insufficient knowledge. For quantifying the aleatory and epistemic uncertainty of multiphase degradation equipment, this article proposes a novel multiphase degradation model and RUL prediction method based on the uncertain random process. First, a 2-D multiphase degradation model is presented in probability space and uncertainty space to describe multiphase degradation patterns with aleatory and epistemic uncertainty. Second, the two-stage empirical estimation method and stochastic uncertainty maximum likelihood estimation (SUMLE) are integrated for change point identification and parameter estimation. Meanwhile, similarity-based weighted SUMLE is projected to update the model. Then, taking into account the heterogeneity between individuals and the randomness of the degradation state at the change point, the analytical expression of RUL is derived under the concept of first hitting time. Finally, the effectiveness of the proposed method is verified through numerical simulation and practical cases.