For remaining useful life prediction method based on single sensor data, there is a problem of low data utilization and low prediction accuracy. This paper proposes a method for predicting the remaining useful life of an engine that combines multi-sensors data. Firstly, multiple sensors data are fused into a composite health index to characterize the degraded performance of the engine. The linear Wiener process is used to model the composite health index. The model parameters are determined by the maximum likelihood estimation method, and then the predicted life of the engine is obtained. In order to determine the fusion coefficient, a method for minimizing the predicted mean square error between the actual life and the predicted life is proposed. After the fusion coefficient is determined, based on the historical life data of the training engine, the offline estimation value of the model parameters is determined. Then, using the Bayesian formula, combined with the real-time monitoring data of the engine and the prior distribution of the parameters, the model parameters are updated in real time, and the probability distribution of the remaining useful life is derived based on the first hitting time, thereby realizing the online prediction of the remaining useful life of the engine. Finally, the commercial modular aero-propulsion system simulation data set is selected for numerical simulation experiments. The results show that compared with the single sensor-based method, the proposed method can improve the accuracy of the remaining useful life prediction, and the relative mean square error of remaining useful life prediction is reduced by about 2%.
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