DCNN With Explicable Training Guide and its Application to Fault Diagnosis of the Planetary Gearboxes

Abstract

The diagnosis performance of Deep Convolutional Neural Network (DCNN) method is closely related to the generalization ability of the training model. An empirical training strategy is to randomly disperse the training samples and train the model with mini-batch training samples. But there are still two problems in the empirical method that need to be solved urgently. Firstly, what is the theoretical basis for random discretization of samples? Secondly, how to scientifically quantify batch division? Aiming at these two problems, the theoretical basis of sample random discretization has been deduced and proved, furthermore, a scientific quantitative batch division method is proposed based on the proved thesis. The fault diagnosis results of the planetary gearbox show that: (1) The model obtained by the training guide proposed in this paper has stronger generalization ability; (2) The DCNN with the training guide can accurately and effectively diagnose the faults of planetary gearbox and obtain ideal diagnosis results.

Keywords

• DCNN
• generalization ability
• explicable training method
• fault diagnosis