Open AccessA CNN-ABiGRU method for Gearbox Fault Diagnosis
Author(s) -
Xiaoyang Zheng,
Zeyu Ye,
JianYing Wu
Publication year - 2022
Publication title -
international journal of circuits, systems and signal processing
Language(s) - English
Resource type - Journals
ISSN - 1998-4464
DOI - 10.46300/9106.2022.16.54
Subject(s) - convolutional neural network , computer science , generalization , fault (geology) , artificial intelligence , pattern recognition (psychology) , key (lock) , signal (programming language) , artificial neural network , mathematics , mathematical analysis , computer security , seismology , geology , programming language
As a key part of modern industrial machinery, there has been a lot of fault diagnosis methods for gearbox. However, traditional fault diagnosis methods suffer from dependence on prior knowledge. This paper proposed an end-to-end method based on convolutional neural network (CNN), Bidirectional gated recurrent unit (BiGRU), and Attention Mechanism. Among them, the application of BiGRU not only made perfect use of the time sequence of signal, but also saved computing resources more than the same type of networks because of the low amount of calculation. In order to verify the effectiveness and generalization performance of the proposed method, experiments are carried out on two datasets, and the accuracy is calculated by the ten-fold crossvalidation. Compared with the existing fault diagnosis methods, the experimental results show that the proposed model has higher accuracy.