The fault prediction method for pump station equipment based on fault graph analysis

Pumping stations are key facilities in urban water supply systems and consume a significant amount of electricity during operation. Predictive maintenance can help prevent equipment inefficiencies caused by failures and reduce unnecessary carbon emissions. This paper presents a novel fault prediction method for pump station equipment, integrating fault graph analysis with a Multi-Task Temporal Fault Prediction Network. The approach combines a multilevel fault graph for system modeling, a Graph Attention Network for fault propagation, and a time series module to capture temporal dependencies. By jointly optimizing fault classification and remaining useful life prediction, the method improves prediction accuracy. Experimental results demonstrate significant improvements in fault prediction accuracy, early warning times, and false alarm rates.