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 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.