A Multiscale Dilated Convolutional Network With Multihead Attention for Gearbox Intelligent Fault Diagnosis.
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| Title: | A Multiscale Dilated Convolutional Network With Multihead Attention for Gearbox Intelligent Fault Diagnosis. |
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| Authors: | Zhong, Shangkun1 (AUTHOR), Li, Guoqiang1 (AUTHOR) lgq@jmu.edu.cn, Zheng, Chengjie1 (AUTHOR), Liu, Wenwei2 (AUTHOR) liuwenwei_dzws@yeah.net, Cheng, Yiwei3 (AUTHOR), Wu, Defeng1 (AUTHOR), Wang, Huiqi (AUTHOR) wanghuiqi@cqu.edu.cn |
| Source: | Shock & Vibration. 6/30/2026, Vol. 2026, p1-12. 12p. |
| Subjects: | Fault diagnosis, Feature extraction, Convolutional neural networks, Condition-based maintenance, Receptive fields (Neurology) |
| Abstract: | The manual maintenance mode of gearboxes suffers from low efficiency and high subjectivity, making it difficult to meet the requirements of intelligent production. Therefore, achieving accurate condition identification and diagnosis of gearboxes is essential for further innovating intelligent maintenance of gearboxes. However, existing intelligent fault diagnosis methods for gearboxes suffer from insufficient mining of deep state features, resulting in poor effectiveness and generalization in practical applications. To solve the above challenges, this paper designed a new feature learning module that is named multiscale dilated multihead attention network (MDCN‐MHA). It can effectively expand the receptive field and enhance the ability to capture key fault features from gearbox condition data. Experimental verification and ablation studies show that the model with three multihead attention outperforms other comparison model variants in terms of accuracy and stability. Even under various noise levels, it still maintains high classification accuracy. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | The manual maintenance mode of gearboxes suffers from low efficiency and high subjectivity, making it difficult to meet the requirements of intelligent production. Therefore, achieving accurate condition identification and diagnosis of gearboxes is essential for further innovating intelligent maintenance of gearboxes. However, existing intelligent fault diagnosis methods for gearboxes suffer from insufficient mining of deep state features, resulting in poor effectiveness and generalization in practical applications. To solve the above challenges, this paper designed a new feature learning module that is named multiscale dilated multihead attention network (MDCN‐MHA). It can effectively expand the receptive field and enhance the ability to capture key fault features from gearbox condition data. Experimental verification and ablation studies show that the model with three multihead attention outperforms other comparison model variants in terms of accuracy and stability. Even under various noise levels, it still maintains high classification accuracy. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 10709622 |
| DOI: | 10.1155/vib/6073130 |
