Abstract：It is difficult for the existing rolling bearing performance degradation trend prediction method to select the degradation index and its prediction accuracy is low. To address the problem, a prediction method of rolling bearing degradation trend based on self-encoder and GRU neural network is proposed. Firstly, the high-dimensional feature set of the mixed vibration domain of the bearing vibration signal is constructed, and the performance degradation index with high sensitivity and good trend is initially selected by using the comprehensive evaluation value of the index. Then, the self-encoder is used to fuse the high-dimensional feature set to eliminate the redundant information between the mixed domain features. On this basis, input the fused features into the GRU neural network model to complete the rolling bearing degradation trend prediction. The experimental results show that the proposed method can obtain more accurate prediction results of rolling bearing degradation trend.
王鹏，邓蕾，汤宝平，韩延. 基于自编码器和门限循环单元神经网络的滚动轴承退化趋势预测[J]. 振动与冲击, 2020, 39(17): 106-111.
WANG Peng， DENG Lei， TANG Baoping， HAN Yan. Degradation trend prediction of rolling bearing based on auto-encoder and GRU neural network. JOURNAL OF VIBRATION AND SHOCK, 2020, 39(17): 106-111.
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