Fault Diagnosis of Hydraulic Hoist Based on Digital Twin and Bayesian Network
Hydraulic hoist plays a key role in ensuring the safe operation of water conservancy facilities, flood control and drainage, and water resource scheduling. Once failure occurs, it will lead to out-of-control water levels, equipment damage, and other accidents, affecting the normal operation of water...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | zho |
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Editorial Office of Pearl River
2024-10-01
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| Series: | Renmin Zhujiang |
| Subjects: | |
| Online Access: | http://www.renminzhujiang.cn/thesisDetails#10.3969/j.issn.1001-9235.2024.10.013 |
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| _version_ | 1841530549120794624 |
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| author | WANG Bo SUN Ruiyang QIAN Lu GU Hao NIE Xiangtian |
| author_facet | WANG Bo SUN Ruiyang QIAN Lu GU Hao NIE Xiangtian |
| author_sort | WANG Bo |
| collection | DOAJ |
| description | Hydraulic hoist plays a key role in ensuring the safe operation of water conservancy facilities, flood control and drainage, and water resource scheduling. Once failure occurs, it will lead to out-of-control water levels, equipment damage, and other accidents, affecting the normal operation of water conservancy projects. In order to grasp the operation and maintenance of hydraulic hoists and quickly identify abnormal conditions, a fault diagnosis model of hydraulic hoists was constructed based on digital twin technology and Bayesian theory. Firstly, this paper analyzed the daily operation state of the hydraulic hoist and the data stored in the data tank and constructed the digital twin system of the hydraulic hoist. Secondly, a fault diagnosis model of a digital hydraulic hoist based on the Bayesian network was established according to expert experience and historical fault data, and sensitivity analysis was carried out through examples. Fault events were sorted. The results show that the model can accurately diagnose the fault event of the hydraulic hoist according to the input probability. The main fault factors are the abnormal spool position of the relief valve, extremely low oil level of the fuel tank, and non-reversing of the reversing valve, which are consistent with the actual operation and maintenance condition. Finally, the rationality and validity of the fault model were verified by axioms. |
| format | Article |
| id | doaj-art-d4148dcd455042ff9d698d1009fa21f7 |
| institution | Kabale University |
| issn | 1001-9235 |
| language | zho |
| publishDate | 2024-10-01 |
| publisher | Editorial Office of Pearl River |
| record_format | Article |
| series | Renmin Zhujiang |
| spelling | doaj-art-d4148dcd455042ff9d698d1009fa21f72025-01-15T03:02:09ZzhoEditorial Office of Pearl RiverRenmin Zhujiang1001-92352024-10-014512413756749148Fault Diagnosis of Hydraulic Hoist Based on Digital Twin and Bayesian NetworkWANG BoSUN RuiyangQIAN LuGU HaoNIE XiangtianHydraulic hoist plays a key role in ensuring the safe operation of water conservancy facilities, flood control and drainage, and water resource scheduling. Once failure occurs, it will lead to out-of-control water levels, equipment damage, and other accidents, affecting the normal operation of water conservancy projects. In order to grasp the operation and maintenance of hydraulic hoists and quickly identify abnormal conditions, a fault diagnosis model of hydraulic hoists was constructed based on digital twin technology and Bayesian theory. Firstly, this paper analyzed the daily operation state of the hydraulic hoist and the data stored in the data tank and constructed the digital twin system of the hydraulic hoist. Secondly, a fault diagnosis model of a digital hydraulic hoist based on the Bayesian network was established according to expert experience and historical fault data, and sensitivity analysis was carried out through examples. Fault events were sorted. The results show that the model can accurately diagnose the fault event of the hydraulic hoist according to the input probability. The main fault factors are the abnormal spool position of the relief valve, extremely low oil level of the fuel tank, and non-reversing of the reversing valve, which are consistent with the actual operation and maintenance condition. Finally, the rationality and validity of the fault model were verified by axioms.http://www.renminzhujiang.cn/thesisDetails#10.3969/j.issn.1001-9235.2024.10.013digital twindigital hydraulic hoistfault diagnosisBayesian networksensitivity analysis |
| spellingShingle | WANG Bo SUN Ruiyang QIAN Lu GU Hao NIE Xiangtian Fault Diagnosis of Hydraulic Hoist Based on Digital Twin and Bayesian Network Renmin Zhujiang digital twin digital hydraulic hoist fault diagnosis Bayesian network sensitivity analysis |
| title | Fault Diagnosis of Hydraulic Hoist Based on Digital Twin and Bayesian Network |
| title_full | Fault Diagnosis of Hydraulic Hoist Based on Digital Twin and Bayesian Network |
| title_fullStr | Fault Diagnosis of Hydraulic Hoist Based on Digital Twin and Bayesian Network |
| title_full_unstemmed | Fault Diagnosis of Hydraulic Hoist Based on Digital Twin and Bayesian Network |
| title_short | Fault Diagnosis of Hydraulic Hoist Based on Digital Twin and Bayesian Network |
| title_sort | fault diagnosis of hydraulic hoist based on digital twin and bayesian network |
| topic | digital twin digital hydraulic hoist fault diagnosis Bayesian network sensitivity analysis |
| url | http://www.renminzhujiang.cn/thesisDetails#10.3969/j.issn.1001-9235.2024.10.013 |
| work_keys_str_mv | AT wangbo faultdiagnosisofhydraulichoistbasedondigitaltwinandbayesiannetwork AT sunruiyang faultdiagnosisofhydraulichoistbasedondigitaltwinandbayesiannetwork AT qianlu faultdiagnosisofhydraulichoistbasedondigitaltwinandbayesiannetwork AT guhao faultdiagnosisofhydraulichoistbasedondigitaltwinandbayesiannetwork AT niexiangtian faultdiagnosisofhydraulichoistbasedondigitaltwinandbayesiannetwork |