A health condition assessment and prediction method of Francis turbine units using heterogeneous signal fusion and graph-driven health benchmark model

Abstract

To ensure the safety and efficiency of hydroelectric power generation, the health condition assessment and prediction (HCAP) of Francis turbine units (FTUs) have been widely concerned. To this end, some data-driven methods based on health benchmark model (HBM) and performance deterioration index (PDI) have been proposed, but there are still some shortcomings: 1) Only one type of signal is used for FTU health monitoring and assessment, which cannot fully represent the deterioration of the system. 2) The establishment of HBM only focuses on the time sequence dependences of signals, while ignoring the inter-correlations between signals. 3) PDI based on linear difference measurement cannot integrate heterogeneous signal representations to fully assess FTU status. In this paper, a HCAP method of FTUs using heterogeneous signal fusion and graph-driven HBM is proposed. First, the multivariate data of health status are transformed into spatial-temporal graphs by establishing connections between similar signals. Furthermore, a hybrid neural network-based HBM is designed to excavate the spatial-temporal dependence relationships existing in these graphs, and learn the mapping relationship between working condition parameters and monitoring signals. Finally, Mahalanobis distance between the heterogeneous signals predicted by HBM and the measured signals in the degraded status is calculated, and the comprehensive PDI for HCAP tasks is obtained by the designed heterogeneous signal fusion function. Verification experiments show that the proposed HCAP method effectively assesses FTU deterioration degree earlier with a higher sensitivity.