Research on Fault Prediction Technology Based on Improved LSTM Neural Network

This paper presents a fault prediction method based on improved long short-term memory neural networks, including network structure design, network training, and prediction process implementation algorithms, etc. Further aiming at minimizing prediction error, a parameter optimization algorithm for SSA-BiLSTM prediction model is particularly designed, which is validated to have strong applicability and higher accuracy in fault time series analysis through experimental comparison with various typical time series prediction models.