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

Peng Luo,Guoji Shen,Zhe Cheng,Lun Zhang,Niaoqing Hu

doi:10.1109/access.2020.3007027

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.

Highlights

It is of great importance to monitor the health state of mechanical equipment
Aiming at the four parts aforementioned, many relevant methods, such as EMD, EEMD, VMD and SVM, BPNN et al, have been proposed [5]–[7]. These traditional fault diagnosis methods have high requirements for the acquired vibration signals and the signal processing methods, and they rely on a lot of expert knowledge as well [8], [9]
By transforming the dimension or the domain, the above-mentioned methods aim to improve the generalization performance of the training model

Summary

INTRODUCTION

It is of great importance to monitor the health state of mechanical equipment. Generally, fault diagnosis methods include data acquisition, signal processing, feature extraction and pattern recognition [1]–[4]. Zhao et al [24] transformed the original time domain signal into the frequency domain signal, and fault diagnosis was carried out based on the spectrum data characteristic diagram Some other scholars, such as Hu et al [25] decomposed the signal using EMD method, screened samples in terms of the kurtosis of each decomposition component, and input the stacked components into the DCNN model for fault diagnosis. A planetary gearbox fault experiment has been designed and the test results show that the DCNN model under the training guidance strategy achieves a significant improvement as compared with the original methods and other Deep Learning methods. The computation for the number of all the parameters of a fully connected layer is described as follows

THE OUTPUT LAYER

BASIS FOR RANDOM DISCRETIZATION OF SAMPLES

Findings

CONCLUSION