Health status assessment of pump station units based on spatio-temporal fusion and uncertainty information

An effective health status assessment (HSA) for pump station units (PSUs) is crucial for accurately determining their real status and providing technical support for safe operational decisions. Due to the limitations of existing data-driven HSA methods, which primarily focus on the temporal dependencies of monitoring signals and fail to explore the complex interconnections among signals comprehensively. Moreover, when constructing performance degradation indices based on linear differences, these methods do not effectively integrate heterogeneous signals, resulting in an incomplete and inaccurate assessment of the overall system degradation. This paper proposes a real-time comprehensive HSA method for PSUs based on multi-source heterogeneous uncertainty information. Initially, a health benchmark model (HBM) is built using CrossGNN, which possesses cross-scale and cross-variable interaction capabilities, to precisely capture the temporal dependencies and dynamic relationships among variables in monitoring signals. Subsequently, key measurement points that reflect the operational status of the PSUs are identified through correlation analysis to establish multi-source evaluation indices. Then, considering the uncertainty in signal changes, a novel health degradation index (HDI) is developed using Mahalanobis distance (MD) and the Gaussian Cloud Model (GCM) to analyze changes in unit status. Furthermore, a weighting calculation method based on the non-dominated sorting genetic algorithm (NSGA-II) is proposed to establish a real-time comprehensive health index (RCHDI) for a thorough assessment of PSUs status. Finally, the effectiveness of the proposed method is validated through a case study using data from a pump station in the South-to-North Water Diversion Project in China. The results show that, compared to other studies, the proposed method significantly improves the stability and smoothness of the state assessment curve, with increases of 21.5% and 47.1% respectively, providing a new perspective for comprehensively assessing the health status of PSUs.V.