A Novel Remaining Useful Life Probability Prediction Approach for Aero-Engine with Improved Bayesian Uncertainty Estimation Based on Degradation Data

As the heart of aircraft, the aero-engine is not only the main power source for aircraft flight but also an essential guarantee for the safe flight of aircraft. Therefore, it is of great significance to find effective methods for remaining useful life (RUL) prediction for aero-engines in order to avoid accidents and reduce maintenance costs. With the development of deep learning, data-driven approaches show great potential in dealing with the above problem. Although many attempts have been made, few works consider the error of the point prediction result caused by uncertainties. In this paper, we propose a novel RUL probability prediction approach for aero-engines with prediction uncertainties fully considered. Before forecasting, a principal component analysis (PCA) is first utilized to cut down the dimension of sensor data and extract the correlation between multivariate data to reduce the network computation. Then, a multi-layer bidirectional gate recurrent unit (BiGRU) is constructed to predict the RUL of the aero-engine, while prediction uncertainties are quantized by the improved variational Bayesian inference (IVBI) with a Gaussian mixture distribution. The proposed method can give not only the point prediction of RUL but also the confidence interval of the prediction result, which is very helpful for real-world applications. Finally, the experimental study illustrates that the proposed method is feasible and superior to several other comparative models.