Abstract

The fault diagnosis of the gearbox is a complex and important work. In this paper, a multilayer gated recurrent unit (MGRU) method is proposed for spur gear fault diagnosis, that is, three-layer gated recurrent unit (GRU). The vibration signals are firstly monitored on the test bench, and then extracted in both time domain and time-frequency domain. Finally, MGRU is used to learn representation and classification. The MGRU can improve the representation of information and identify the features of fault types more precisely with the increasing number of layers. The proposed method was tested by two spur gears with 10 state modes. To evaluate the method's classification accuracy, four methods were utilized for comparison, i.e., the GRU, long short-term memory (LSTM), multilayer LSTM (MLSTM), and support vector machine (SVM), respectively. In addition, the separability and robustness analysis are also discussed for the proposed MGRU performance. All of the results exhibited that the proposed MGRU approach is effective for spur gear fault diagnosis.