Remaining Useful Lifetime Estimation for Aero-Engines Based on Wiener-Process with Integrated Lifetime Data and Degradation Data

In this paper, we address a wiener-process degradation model to estimate the remaining useful lifetime of aero-engines. The measurement error and unit-to-unit variability are taken into account in the proposed degradation model and the RUL estimation is derived by the concept of first hitting time. Typically, the unknown parameters of the degradation model were estimated based on historical degradation data. This paper presents a Bayesian framework to estimate the unknown parameters by using the historical lifetime data. Once the newly monitored degradation data of aero-engine are obtained, the parameters of the degradation model and the remaining useful lifetime estimation are updated by using the Kalman filter algorithm. Lastly, the proposed model is demonstrated on CFM56 series aero-engines, and the results show that our proposed model can reduce the uncertainty and improve the accuracy of remaining useful lifetime estimation.