RUL Prediction Method of a Rolling Bearing Based on Improved SAE and Bi-LSTM

Since the sigmoid activation function of sparse auto-encoder (SAE) is easy to cause the gradient to disappear, a new Tan function is used to replace the original sigmoid function. In SAE, for the limitations in regression prediction when Kullback-Leibler (KL) divergence is used for sparseness constraints, KL divergence is replaced with the dropout mechanism to achieve network sparsity. And the improved SAE is used to perform unsupervised adaptive deep feature extraction for the vibration signals of rolling bearings, without designing labels manually for supervised fine adjustment. Meanwhile, for the remaining useful life (RUL) prediction method of rolling bearing, generally only the past information is considered and the future information is ignored, the bi-directional long short-term memory (Bi-LSTM) is introduced to construct an RUL prediction model of the rolling bearing. Using two bearing data sets, experimental results both show that the proposed RUL prediction method of a rolling bearing based on improved sparse auto encoder and Bi-LSTM can improve the convergence speed of the model and has lower prediction error. © 2022 Science Press. All rights reserved.