Remaining Useful Life Prediction for Degradation Processes With Memory Effects

• Published in: IEEE transactions on reliability 2017-09, Vol.66 (3), p.751-760
• Main Authors: Xi, Xiaopeng, Chen, Maoyin, Zhou, Donghua
• Format: Article
• Language: English
• Subjects: Blast furnaces; Degradation; Engines; Fractional Brownian motion (FBM); maximum likelihood (ML); Maximum likelihood estimation; memory effects; Monitoring; Numerical models; remaining useful life (RUL); Temperature sensors
• ISSN: 0018-9529; 1558-1721
• DOI: 10.1109/TR.2017.2717488

Some practical systems such as blast furnaces and turbofan engines have degradation processes with memory effects. The term of memory effects implies that the future states of the degradation processes depend on both the current state and the past states because of the interaction with environments. However, most works generally used a memoryless Markovian process to model the degradation processes. To characterize the memory effects in practical systems, we develop a new type of degradation model, in which the diffusion is represented as a fractional Brownian motion (FBM). FBM is actually a special non-Markovian process with long-term dependencies. Based on the monitored data, a Monte Carlo method is used to predict the remaining useful life (RUL). The unknown parameters in the proposed model can be estimated by the maximum likelihood algorithm, and then the distribution of the RUL is predicted. The effectiveness of the proposed model is fully verified by a numerical example and a practical case study.