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

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.

Automated summary

This paper proposes a method using heterogeneous signal fusion and graph-driven approach to assess and predict the health condition of Francis turbine units in hydroelectric power generation. By establishing connections between similar signals, the multivariate data of health status are transformed into spatial-temporal graphs. A hybrid neural network-based model is designed to excavate the spatial-temporal dependence relationships in these graphs. The comprehensive health assessment index is obtained by calculating the distance between the predicted heterogeneous signals and measured signals. Verification experiments show that the proposed method has a higher sensitivity in assessing the deterioration degree of turbine units.