Journal of Zhejiang University SCIENCE  A

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A guided differential dilated convolutional network for fault diagnosis of hydraulic manipulators


Author(s):  Daocheng FU1, Xu YANG1, Haitao LIU2, Yugang REN3, Xianpeng SHI3, Limin ZHU4

Affiliation(s):  1Institute of Marine Science and Technology, Shandong University, Qingdao 266237, China 2School of Mechanical Engineering, Tianjin University, Tianjin 300354, China 3National Deep Sea Center, Qingdao 266237, China 4State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai 200240, China

Corresponding email(s):  Xu YANG, xu_y@sdu.edu.cn

Key Words:  Hydraulic manipulator; Fault diagnosis; Dual guidance; Gradient feature; Dilated convolutional


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Daocheng FU1, Xu YANG1, Haitao LIU2, Yugang REN3, Xianpeng SHI3, Limin ZHU4. A guided differential dilated convolutional network for fault diagnosis of hydraulic manipulators[J]. Journal of Zhejiang University Science A,in press.Frontiers of Information Technology & Electronic Engineering,in press.https://doi.org/10.1631/jzus.A2600200

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Abstract: Fault diagnosis for hydraulic manipulators plays a crucial role in ensuring operational safety but still faces challenges in fault localization. To address this issue, a guided differential dilated convolutional network (GDDCN) is proposed in this paper. First, a novel interference masking mechanism is designed to provide dual guidance for feature extraction and classification. Then, a learnable differential kernel with the center parameter fixed at zero and side parameters opposite in sign is designed to adaptively extract gradient features. Afterward, a multiscale gated dilated convolution module is developed to capture global temporal features across multiple scales and achieve gated feature fusion. Finally, the fused features are fed into a fully connected classification module for fault classification. The results show that the GDDCN achieves diagnosis of the faulty joint with an average accuracy of 99.39% on a hydraulic manipulator platform. The superiority and effectiveness of the GDDCN is further confirmed through comparative and ablation studies.

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On-line Access: 2026-07-20

Received: 2026-04-02

Revision Accepted: 2026-06-17

Crosschecked: 0000-00-00

Cited: 0

Clicked: 16

Citations:  Bibtex RefMan EndNote GB/T7714

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