An Update-Strategy-Based Gaussian Process Regression Method for Aeroengines Fault Prediction

Health state prediction and fault time prediction are two key tasks in the fault prediction field. However, existing fault prediction techniques perform these tasks hierarchically and separately without considering the time-varying dynamics of the system operation process, which reduces the prediction efficiency and accuracy. Therefore, a Gaussian process regression prediction method based on the update strategy is proposed for the dual tasks of aeroengines. In this method, for new samples collected continuously, the predictive distributions are deduced and the model parameters are updated. Specifically, through variable selection and multivariable fusion technology, the most beneficial variables corresponding to the health state are used to construct a shared health index for health state and fault time prediction. The proposed health index can better characterize the health state. Then, by the update strategies including single-point and multipoint update strategies, a unified Gaussian process regression framework with newly collected samples information is obtained. Thereby, the health index and fault time prediction are realized synchronously. Experimental results on the commercial modular aero-propulsion system simulation dataset demonstrate that the proposed method outperforms state-of-the-art ones.