轴向柱塞泵健康评估的模型驱动和数据驱动混合方法

IF 5.3 Q1 ENGINEERING, MECHANICAL International Journal of Hydromechatronics Pub Date : 2023-01-01 DOI:10.1504/ijhm.2023.129123
Qun Chao, Zi Xu, Yuechen Shao, Jianfeng Tao, Chengliang Liu, Shuo Ding
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引用次数: 7

摘要

轴向柱塞泵是液压系统的关键部件,其性能直接影响液压系统的工作效率和可靠性。许多数据驱动方法已经应用于轴向柱塞泵的故障诊断。然而,轴向柱塞泵的性能退化评估在轴向柱塞泵的预测性维护中起着重要的作用,相关研究很少。提出了一种模型驱动和数据驱动相结合的轴向柱塞泵健康状态评估方法。建立了物理流动损失模型,求解了轴向柱塞泵在不同工况下的流动损失系数。流动损失系数作为特征向量来训练支持向量数据描述(SVDD)模型。提出了一种基于SVDD的泵健康状态定量评价指标。在不同泵健康状况下的实验结果证实了该方法的有效性。
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Hybrid model-driven and data-driven approach for the health assessment of axial piston pumps
Axial piston pumps are key components in hydraulic systems and their performance significantly affects the efficiency and reliability of hydraulic systems. Many data-driven approaches have been applied to the fault diagnosis of axial piston pumps. However, few studies focus on the performance degradation assessment that plays an important role in the predictive maintenance for axial piston pumps. This paper proposes a hybrid model-driven and data-driven approach to assess the health status of axial piston pumps. A physical flow loss model is established to solve for the flow loss coefficients of the axial piston pump under different operating conditions. The flow loss coefficients act as feature vectors to train a support vector data description (SVDD) model. A health indicator based on SVDD is put forward to quantitatively assess the pump health status. Experimental results under different pump health conditions confirm the effectiveness of the proposed method.
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来源期刊
CiteScore
7.60
自引率
0.00%
发文量
32
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