{"title":"轴向柱塞泵健康评估的模型驱动和数据驱动混合方法","authors":"Qun Chao, Zi Xu, Yuechen Shao, Jianfeng Tao, Chengliang Liu, Shuo Ding","doi":"10.1504/ijhm.2023.129123","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":29937,"journal":{"name":"International Journal of Hydromechatronics","volume":null,"pages":null},"PeriodicalIF":5.3000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Hybrid model-driven and data-driven approach for the health assessment of axial piston pumps\",\"authors\":\"Qun Chao, Zi Xu, Yuechen Shao, Jianfeng Tao, Chengliang Liu, Shuo Ding\",\"doi\":\"10.1504/ijhm.2023.129123\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":29937,\"journal\":{\"name\":\"International Journal of Hydromechatronics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":5.3000,\"publicationDate\":\"2023-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Hydromechatronics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1504/ijhm.2023.129123\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, MECHANICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Hydromechatronics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1504/ijhm.2023.129123","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, MECHANICAL","Score":null,"Total":0}
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.