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Open AccessArticle
Fault Diagnosis Method for Tractor Transmission System Based on Improved Convolutional Neural Network–Bidirectional Long Short-Term Memory
by
Liyou Xu
Liyou Xu 1,2,
Guoxiang Zhao
Guoxiang Zhao 1,
Sixia Zhao
Sixia Zhao 1,2,*
,
Yiwei Wu
Yiwei Wu 1,2 and
Xiaoliang Chen
Xiaoliang Chen 3
1
College of Vehicle and Traffic Engineering, Henan University of Science and Technology, Luoyang 471003, China
2
State Key Laboratory of Intelligent Agricultural Power Equipment, Luoyang 471003, China
3
Henan Institute of Technology, Xinxiang 453000, China
*
Author to whom correspondence should be addressed.
Machines 2024, 12(7), 492; https://doi.org/10.3390/machines12070492 (registering DOI)
Submission received: 20 June 2024
/
Revised: 18 July 2024
/
Accepted: 19 July 2024
/
Published: 21 July 2024
Abstract
In response to the problems of limited algorithms and low diagnostic accuracy for fault diagnosis in large tractor transmission systems, as well as the high noise levels in tractor working environments, a defect detection approach for tractor transmission systems is proposed using an enhanced convolutional neural network (CNN) and a bidirectional long short-term memory neural network (BILSTM). This approach uses a one-dimensional convolutional neural network (1DCNN) to create three feature extractors of varying scales, directly extracting feature information from different levels of the raw vibration signals. Simultaneously, in order to enhance the model’s predicted accuracy and learn the data features more effectively, it presents the multi-head attention mechanism (MHA). To overcome the issue of high noise levels in tractor working environments and enhance the model’s robustness, an adaptive soft threshold is introduced. Finally, to recognize and classify faults, the fused feature data are fed into a classifier made up of bidirectional long short-term memory (BILSTM) and fully linked layers. The analytical findings demonstrate that the fault recognition accuracy of the method described in this article is over 98%, and it also has better performance in noisy environments.
Share and Cite
MDPI and ACS Style
Xu, L.; Zhao, G.; Zhao, S.; Wu, Y.; Chen, X.
Fault Diagnosis Method for Tractor Transmission System Based on Improved Convolutional Neural Network–Bidirectional Long Short-Term Memory. Machines 2024, 12, 492.
https://doi.org/10.3390/machines12070492
AMA Style
Xu L, Zhao G, Zhao S, Wu Y, Chen X.
Fault Diagnosis Method for Tractor Transmission System Based on Improved Convolutional Neural Network–Bidirectional Long Short-Term Memory. Machines. 2024; 12(7):492.
https://doi.org/10.3390/machines12070492
Chicago/Turabian Style
Xu, Liyou, Guoxiang Zhao, Sixia Zhao, Yiwei Wu, and Xiaoliang Chen.
2024. "Fault Diagnosis Method for Tractor Transmission System Based on Improved Convolutional Neural Network–Bidirectional Long Short-Term Memory" Machines 12, no. 7: 492.
https://doi.org/10.3390/machines12070492
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