Fault identification of reclaimer bearing in thermal power plant based on deep learning

The reclaimer, an auxiliary equipment, is important in thermal power plants, it affects the safety and economy of thermal power plants. Therefore, the bearing fault detection of the reclaimer is studied in this paper. For the acquisition of 1D bearing vibration data, empirical mode decomposition (EMD) is employed for signal decomposition. And the residual network is used to extract local features from the data. Further, Bi-directional long short-term memory (Bi-LSTM) is used to extract global features of the data. In the experiments, the numbers of EMD decomposition layers and iterations are analyzed to improve the accuracy. And, compared to convolutional neural network (CNN), the proposed model has high accuracy.