Remaining useful life prediction for train bearing based on an ILSTM network with adaptive hyperparameter optimization

Abstract Remaining useful life (RUL) prediction for bearing is a significant part of the maintenance of urban rail transit trains. Bearing RUL is closely linked to the reliability and safety of train running, but the current prediction accuracy makes it difficult to meet the requirements of high reliability operation. Aiming at the problem, a prediction model based on an improved long short-term memory (ILSTM) network is proposed. Firstly, the variational mode decomposition is used to process the signal, the intrinsic mode function with stronger representation ability is determined according to energy entropy and the degradation feature data is constructed combined with the time domain characteristics. Then, to improve learning ability, a rectified linear unit (ReLU) is applied to activate a fully connected layer lying after the long short-term memory (LSTM) network, and the hidden state outputs of the layer are weighted by attention mechanism. The Harris Hawks optimization algorithm is introduced to adaptively set the hyperparameters to improve the performance of the LSTM. Finally, the ILSTM is applied to predict bearing RUL. Through experimental cases, the better performance in bearing RUL prediction and the effectiveness of each improving measures of the model are validated, and its superiority of hyperparameters setting is demonstrated.