Probabilistic Analysis for Remaining Useful Life Prediction and Reliability Assessment

Although the importance of remaining useful life (RUL) prediction is widely recognized in industries, its implementation in real scenarios is highly restricted by the complexity of the degradation mechanism, uncertainty of machinery, and insufficiency of prior knowledge. To address such a challenge, this article proposes a model-based framework, which has the capability to integrate multiple predictive models via a probabilistic mechanism. When a new observation is fed into each predictive model, the posterior distribution of each model will be updated via Bayesian inference. Then, a grid-sampling strategy is applied to their posterior distributions for identifying the "peak" and "profile," which are used for RUL prediction and reliability assessment, respectively. The effectiveness of this framework is validated with the experiments on a set of steel tension specimens. Theoretical interpretations and comparative studies demonstrate the superiority of the proposed framework. Besides, the proposed framework can not only reduce human workload on trivial parameter setting but also be effective with insufficient prior knowledge, making the intelligent RUL prediction easier.