Abstract:  In order to solve the problem of life index extraction and prediction of rolling bearings in the case of small samples, a bearing health index extraction and prediction method based on variational mode decomposition (VMD) and gate recurrent unit (GRU) network was proposed. First, in view of the scarcity of bearing fault life data of field equipment, the sample data was expanded, and the one-dimensional data was decomposed into multi-dimensional data by VMD decomposition. Then, the multi-dimensional features were extracted and normalized in time domain. Finally, the normalized features were used as the input and the bearing life percentage was used as the output to train the GRU network, which was used to extract the bearing health index and predict the remaining useful life, and the data were verified and analyzed. The results show that the monotonicity is improved by 0.26, 0.19 and 0.08respectively comparing with GRU without VMD(Raw-GUR), recurrent neural network health index (RNN-HI) and convolutional neural network-LSTM (CNN-LSTM) based on test-bed data; the monotonicity is improved by 0.48, 0.3 and 0.07respectively based on escalator motor bearing fault data. Therefore, after VMD feature space expansion and feature extraction, the bearing health index constructed by VMD-GRU method has better monotonicity, and the local feature space of signals can be obtained in more detail, which overcomes the disadvantage that the traditional algorithm is difficult to capture local features only considering the global feature of the samples, and makes the performance of health index constructed by GRU network better.

Key words:  escalator;rolling bearing;health index(HI);remaining useful life (RUL) prediction;variational mode decomposition(VMD);gate recurrent unit (GRU)

GUAN Peng, ZHANG Yi. Health index extraction and remaining useful life prediction method of escalator bearing［J］.Journal of Mechanical & Electrical Engineering, 2022,39(2):202-209.