Multi-SoftMax Convolutional Neural Network and Its Application in the Diagnosis of Planetary Gearbox Complicated Faults

Abstract

There are some shortcomings in the existing methods of fault diagnosis of planetary gearbox: First, the traditional methods are complex and cannot effectively diagnose the planetary gearbox faults. Second, the methods based on convolutional neural network mostly diagnose gearbox faults and rarely are used to diagnose planetary gearbox. In order to effectively diagnose complex faults and variable working conditions, fault tree structure, working condition parallel structure and multi-SoftMax convolution neural network are proposed for the first time. Fault tree structure can handle a variety of complex faults and see the diagnosis effect of each node. The parallel structure can handle variable conditions and predict speed and load. A series of experiments are carried out using the vibration data of ours laboratory planetary gearbox, which indicated that the method can accurately diagnose the complex faults and variable working conditions of the planetary gearbox, and the accuracy is 97%. It is verified that the multi-SoftMax convolutional neural network has strong generalization ability and the advantages of the fault tree structure.