Flywheel Fault Prediction Algorithm Based on Improved Filter and LSTM Fusion

Abstract

To solve the problem that the poor accuracy of long-term prediction relying on model accuracy for the filter-based prediction method, a fusion fault prediction algorithm based on filter and long short-term memory (LSTM) network is proposed to achieve the prediction of spacecraft flywheel slowly growing faults. Firstly, a mini-batch normalization LSTM network and a trend recognition module are designed, which are connected in series to form a neural network predictor to improve the time series prediction accuracy. Then, the Kalman filter update process is improved by the recursive least square (RLS) parameter estimation principle to enhance the robustness for time series prediction error. On this basis, the predicted values output by the neural network predictor are fused with the improved filter. Prediction residual can be obtained for iterative updating and prediction, overcoming the dependence of the filter algorithm on the model and improving prediction accuracy. Finally, the time series predictive performance of three neural network predictors are analyzed by simulation experiments. And considering the degradation fault of flywheel bearing performance, the proposed fusion prediction algorithm is used to determine that the flywheel degrades to a threshold at 856 s, with a prediction time error of 36 s. The simulation results verify the effectiveness of the proposed algorithm for slowly growing fault.

Keywords

• |fault prediction|lstm|kalman filter|rls|flywheel