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Open AccessArticle
A Novel Transformer Network Based on Cross-Spatial Learning and Deformable Attention for Composite Fault Diagnosis of Agricultural Machinery Bearings
by
Xuemei Li
Xuemei Li 1,*
,
Min Li
Min Li 2
,
Bin Liu
Bin Liu 2
,
Shangsong Lv
Shangsong Lv 2 and
Chengjie Liu
Chengjie Liu 2
1
College of Mechanical and Control Engineering, Baicheng Normal University, Baicheng137000, China
2
College of Information and Control Engineering, Jilin Institute of Chemical Technology, Jilin 132022, China
*
Author to whom correspondence should be addressed.
Agriculture 2024, 14(8), 1397; https://doi.org/10.3390/agriculture14081397 (registering DOI)
Submission received: 8 July 2024
/
Revised: 10 August 2024
/
Accepted: 16 August 2024
/
Published: 18 August 2024
Abstract
Diagnosing agricultural machinery faults is critical to agricultural automation, and identifying vibration signals from faulty bearings is important for agricultural machinery fault diagnosis and predictive maintenance. In recent years, data−driven methods based on deep learning have received much attention. Considering the roughness of the attention receptive fields in Vision Transformer and Swin Transformer, this paper proposes a Shift−Deformable Transformer (S−DT) network model with multi−attention fusion to achieve accurate diagnosis of composite faults. In this method, the vibration signal is first transformed into a time−frequency graph representation through continuous wavelet transform (CWT); secondly, dilated convolutional residual blocks and efficient attention for cross−spatial learning are used for low−level local feature enhancement. Then, the shift window and deformable attention are fused into S−D Attention, which has a more focused receptive field to learn global features accurately. Finally, the diagnosis result is obtained through the classifier. Experiments were conducted on self−collected datasets and public datasets. The results show that the proposed S−DT network performs excellently in all cases. With a slight decrease in the number of parameters, the validation accuracy improves by more than 2%, and the training network has a fast convergence period. This provides an effective solution for monitoring the efficient and stable operation of agricultural automation machinery and equipment.
Share and Cite
MDPI and ACS Style
Li, X.; Li, M.; Liu, B.; Lv, S.; Liu, C.
A Novel Transformer Network Based on Cross-Spatial Learning and Deformable Attention for Composite Fault Diagnosis of Agricultural Machinery Bearings. Agriculture 2024, 14, 1397.
https://doi.org/10.3390/agriculture14081397
AMA Style
Li X, Li M, Liu B, Lv S, Liu C.
A Novel Transformer Network Based on Cross-Spatial Learning and Deformable Attention for Composite Fault Diagnosis of Agricultural Machinery Bearings. Agriculture. 2024; 14(8):1397.
https://doi.org/10.3390/agriculture14081397
Chicago/Turabian Style
Li, Xuemei, Min Li, Bin Liu, Shangsong Lv, and Chengjie Liu.
2024. "A Novel Transformer Network Based on Cross-Spatial Learning and Deformable Attention for Composite Fault Diagnosis of Agricultural Machinery Bearings" Agriculture 14, no. 8: 1397.
https://doi.org/10.3390/agriculture14081397
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