RUL Prediction Based on Improved LSTM Network Structure
Po Hu, Zhongqi Li, Di Tian, Jing Zhang · 2022 34th Chinese Control and Decision Conference (CCDC) · 2022
Using the sparse idea of Highway network to design a sparse denoising LSTM network that suppresses redundant neurons to achieve more accurate residual life (RUL) prediction. Different from the idea that the traditional Highway network is sparse in time direction, this paper transforms the traditional LSTM network by designing sparse gates, and suppresses those neurons that have contributed little to the next layer in the previous layer, and highlights those nerves that contribute more. The role of the element, thereby achieving the goal of sparseness and " denoising " at the same time. When the time series is long, the prediction accuracy of RUL prediction using the sparse denoising LSTM network (Sparse Denoising LSTM, SD-LSTM) is high, and the sparse gate structure can also reduce the computational complexity to a certain extent.