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Health evaluation of axial piston pumps based on density weighted support vector data description

Author

Listed:
  • Chao, Qun
  • Shao, Yuechen
  • Liu, Chengliang
  • Yang, Xiaoxue

Abstract

Axial piston pump is the power source of hydraulic systems and its health evaluation is crucial for the condition monitoring of hydraulic systems. Previous studies focused on the fault diagnosis of axial piston pumps but paid little attention to their health evaluation. In addition, labeled faulty samples are often insufficient for training supervised fault diagnosis models in practical applications. Therefore, this work aims to develop a health evaluation model for axial piston pumps, which only requires normal samples for model training. The proposed method uses density weighted support vector data description (SVDD) to determine the normal baseline level of an axial piston pump and then constructs a dimensionless health index to score the pump's health condition. A test bench was built to collect pressure and vibration signals of an actual axial piston pump at different health levels. Results show that the proposed method can effectively evaluate the pump's health condition through the quantifiable health index.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:reensy:v:237:y:2023:i:c:s0951832023002685
    DOI: 10.1016/j.ress.2023.109354
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    References listed on IDEAS

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    1. Zhang, Yong & Xin, Yuqi & Liu, Zhi-wei & Chi, Ming & Ma, Guijun, 2022. "Health status assessment and remaining useful life prediction of aero-engine based on BiGRU and MMoE," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    2. 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).
    3. Ma, Zhonghai & Liao, Haitao & Gao, Jianhang & Nie, Songlin & Geng, Yugang, 2023. "Physics-Informed Machine Learning for Degradation Modeling of an Electro-Hydrostatic Actuator System," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
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    5. Chen, Bin & Yu, Songhao & Yu, Yang & Zhou, Yilin, 2020. "Acoustical damage detection of wind turbine blade using the improved incremental support vector data description," Renewable Energy, Elsevier, vol. 156(C), pages 548-557.
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