Engine life prediction based on degradation data

The motor hour (working time) of an armored vehicle’s engine reflects its technical state to a certain extent. However, even the same type of engine with the same motor hour shows very different technical states in different working environments. At the same time, it is difficult to obtain the full life data or physical failure mechanism required by the traditional life prediction method. In view of the above problems, a model of engine life prediction based on degradation data and neural networks is built in this paper. Firstly, the degradation parameters are selected according to certain principles, and the sample data are standardized. Then, the principal component analysis method is used to simplify multiple parameters to a comprehensive parameter, and the interpolation method is applied to get the parameter’s time series data as the train data of the neural network. Finally, the life prediction model of the engine based on the neural network is established. The validation results indicate that the model runs accurately. It is also practical and worthy of being used abroad. © 2018 Totem Publisher, Inc. All rights reserved.