Research on intelligent water valve fault classification method based on machine learning

Water valves are crucial components for controlling fluid flow in smart city pipeline networks. Identifying the types of faults in water valves, which are widely distributed and numerous, and timely judging and warning their operational status, will greatly improve the efficiency of water systems and significantly reduce the risk of accidents. Based on various datasets of water valve parameters, we simulated the normal operation data of the valve, as well as valve clogging, external leakage, and positioner supply pressure drop using DAMADICS. We then compared classification algorithms such as KNN, BP, SVM, etc. The KNN classification algorithm, which is suitable for online monitoring of valves, has been selected.