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The loose slipper fault diagnosis of variable-displacement pumps under time-varying operating conditions

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Listed:
  • Xu, Xinlei
  • Zhang, Junhui
  • Huang, Weidi
  • Yu, Bin
  • Lyu, Fei
  • Zhang, Xiaolong
  • Xu, Bing

Abstract

Variable-displacement pumps (VDPs) are widely used as core power components of high-pressure hydraulic systems due to their superior power density. The loose slipper fault is a typical failure type of the VDP, which can cause premature damage or even unexpected shutdown. The on-site VDPs usually work under time-varying operating conditions (TVOCs), and their pressure varies with the changing speed. These unique characteristics pose a new challenge to the fault diagnosis (FD) of the VDPs. This study introduces an innovative FD method named the multi-scale attention mechanism residual network (MSARN). The MSARN is equipped with three well-chosen components: multi-scale convolution module (MSCM), attention mechanism (AM), and residual connection. The integration of these three components facilitates the efficient extraction and fusion of meaningful multi-scale fault features, thereby preventing the overfitting problem. The experimental results obtained on the VDP validate the effectiveness and accuracy of the MSARN. The suggested method trains the model using data collected under partial constant and representative operating conditions, enabling the achievement of FD under TVOCs.

Suggested Citation

  • Xu, Xinlei & Zhang, Junhui & Huang, Weidi & Yu, Bin & Lyu, Fei & Zhang, Xiaolong & Xu, Bing, 2024. "The loose slipper fault diagnosis of variable-displacement pumps under time-varying operating conditions," Reliability Engineering and System Safety, Elsevier, vol. 252(C).
    • Handle: RePEc:eee:reensy:v:252:y:2024:i:c:s0951832024005209
    DOI: 10.1016/j.ress.2024.110448
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    1. Chen, Jiayu. & Lin, Cuiyin & Yao, Boqing & Yang, Lechang & Ge, Hongjuan, 2023. "Intelligent fault diagnosis of rolling bearings with low-quality data: A feature significance and diversity learning method," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    2. Christian Janiesch & Patrick Zschech & Kai Heinrich, 2021. "Machine learning and deep learning," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(3), pages 685-695, September.
    3. Gao, Pengjie & Wang, Junliang & Shi, Ziqi & Ming, Weiwei & Chen, Ming, 2024. "Long-term temporal attention neural network with adaptive stage division for remaining useful life prediction of rolling bearings," Reliability Engineering and System Safety, Elsevier, vol. 251(C).
    4. Li, Naipeng & Gebraeel, Nagi & Lei, Yaguo & Bian, Linkan & Si, Xiaosheng, 2019. "Remaining useful life prediction of machinery under time-varying operating conditions based on a two-factor state-space model," Reliability Engineering and System Safety, Elsevier, vol. 186(C), pages 88-100.
    5. 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).
    6. Tang, Shengnan & Zhu, Yong & Yuan, Shouqi, 2022. "Intelligent fault identification of hydraulic pump using deep adaptive normalized CNN and synchrosqueezed wavelet transform," Reliability Engineering and System Safety, Elsevier, vol. 224(C).
    7. Chen, Dingliang & Cai, Wei & Yu, Hangjun & Wu, Fei & Qin, Yi, 2023. "A novel transfer gear life prediction method by the cross-condition health indicator and nested hierarchical binary-valued network," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    8. 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).
    9. Zhou, Haoxuan & Wang, Bingsen & Zio, Enrico & Wen, Guangrui & Liu, Zimin & Su, Yu & Chen, Xuefeng, 2023. "Hybrid system response model for condition monitoring of bearings under time-varying operating conditions," Reliability Engineering and System Safety, Elsevier, vol. 239(C).
    10. Xu, Yadong & Yan, Xiaoan & Sun, Beibei & Liu, Zheng, 2022. "Global contextual residual convolutional neural networks for motor fault diagnosis under variable-speed conditions," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    11. Long, Junqi & Chen, Chuanhai & Liu, Zhifeng & Guo, Jinyan & Chen, Weizheng, 2022. "Stochastic hybrid system approach to task-orientated remaining useful life prediction under time-varying operating conditions," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    12. Wang, Jinrui & Zhang, Zongzhen & Liu, Zhiliang & Han, Baokun & Bao, Huaiqian & Ji, Shanshan, 2023. "Digital twin aided adversarial transfer learning method for domain adaptation fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    13. Bai, Ruxue & Meng, Zong & Xu, Quansheng & Fan, Fengjie, 2023. "Fractional Fourier and time domain recurrence plot fusion combining convolutional neural network for bearing fault diagnosis under variable working conditions," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    14. Chao, Qun & Shao, Yuechen & Liu, Chengliang & Yang, Xiaoxue, 2023. "Health evaluation of axial piston pumps based on density weighted support vector data description," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
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