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DCSIAN: A novel deep cross-scale interactive attention network for fault diagnosis of aviation hydraulic pumps and generalizable applications

Author

Listed:
  • Fu, Song
  • Zou, Limin
  • Wang, Yue
  • Lin, Lin
  • Lu, Yifan
  • Zhao, Minghang
  • Guo, Feng
  • Zhong, Shisheng

Abstract

Channel attention (CA) has been wildly applied to enhance the diagnosis performance of multiscale convolution (MSC)-based diagnosis methods. Nevertheless, most of the existing CA modules only consider the internal local correlation among different channels within each scale feature, but ignore the global correlation among different scales, restricting further improvement. To address this issue, a novel deep cross-scale interactive attention network (DCSIAN) is developed to achieve accurate fault diagnosis for aviation hydraulic pumps under high-noise environments. Specifically, a novel cross-scale interactive attention module (CSIAM) is developed and introduced into MSC to learn complementary and rich multiscale features from original vibration signals. CSIAM adopts two cascaded submodules to focus on local channel correlation and global scale correlation simultaneously. Local channel correlation is used to adaptively measure the importance of different channel feature within each scale, while global scale correlation is used to dynamically determine the contribution of each scale feature to the final diagnostic result. In this way, the fault-related information at different scales can be fully captured and utilized. Finally, the effectiveness of DCSIAN is validated by a series of experimental comparisons on an aviation hydraulic pump dataset and a bearing dataset with various types noise.

Suggested Citation

  • Fu, Song & Zou, Limin & Wang, Yue & Lin, Lin & Lu, Yifan & Zhao, Minghang & Guo, Feng & Zhong, Shisheng, 2024. "DCSIAN: A novel deep cross-scale interactive attention network for fault diagnosis of aviation hydraulic pumps and generalizable applications," Reliability Engineering and System Safety, Elsevier, vol. 249(C).
  • Handle: RePEc:eee:reensy:v:249:y:2024:i:c:s0951832024003193
    DOI: 10.1016/j.ress.2024.110246
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    References listed on IDEAS

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    1. Dong, Yutong & Jiang, Hongkai & Yao, Renhe & Mu, Mingzhe & Yang, Qiao, 2024. "Rolling bearing intelligent fault diagnosis towards variable speed and imbalanced samples using multiscale dynamic supervised contrast learning," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    2. Xu, Yadong & Yan, Xiaoan & Sun, Beibei & Liu, Zheng, 2022. "Dually attentive multiscale networks for health state recognition of rotating machinery," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    3. Xu, Yadong & Yan, Xiaoan & Feng, Ke & Zhang, Yongchao & Zhao, Xiaoli & Sun, Beibei & Liu, Zheng, 2023. "Global contextual multiscale fusion networks for machine health state identification under noisy and imbalanced conditions," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    4. Li, Sheng & Ji, J.C. & Xu, Yadong & Sun, Xiuquan & Feng, Ke & Sun, Beibei & Wang, Yulin & Gu, Fengshou & Zhang, Ke & Ni, Qing, 2023. "IFD-MDCN: Multibranch denoising convolutional networks with improved flow direction strategy for intelligent fault diagnosis of rolling bearings under noisy conditions," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
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