Physical Spatial-Temporal Weighted Graph Construction for FTU's Health Condition Assessment

To ensure the dependable operation of hydropower generation, substantial research efforts have been dedicated to the health condition assessment (HCA) of Francis turbine units (FTUs). Typical HCA strategies encompass the creation of health benchmark model (HBM) and the calculation of performance degradation indicators (PDIs). However, Previous investigations have primarily concentrated on either the temporal or spatial relationships within pulsation signals and working condition parameters, ignoring the complex interactions between these relationships. In addition, a simplistic averaging of temporal and spatial relationships can lead to the loss of critical information. This paper proposes a physical spatial-temporal weighted graph construction for the health condition assessment of FTUs. Initially, a simulation model of the FTUs is developed, utilizing computational fluid dynamics theories to produce simulated pulsation signals. Then, actual pulsation signals are used to refine the simulation model, achieving a high-fidelity representation. Subsequently, the pulsation signals are modeled using graph data to capture both physical spatial relationships and essential features. On this basis, time-related statistical parameters are employed to allocate weights to each identified relationship. Furthermore, the integration of the feature relationships with physical spatial relationships leads to the creation of a physical spatial-temporal weighted graph, enhancing its signal representation capability. Ultimately, this graph is fed into the graph convolutional model to obtain HBM. To assess the performance, the PDI is calculated by extracting the Euclidean distance between the comprehensive features derived from the HBM-predicted labels and the actual signals observed in the degraded state. Validation experiment is conducted to verify the effectiveness of proposed method through the utilization of real-time monitoring data and working condition parameters from the FTU deployed within the hydropower station.

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Published
2024-10-11
DOI
https://doi.org/10.1109/phm-beijing63284.2024.10874623
Citations
1
Primary Topic
Context-Aware Activity Recognition Systems
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article
Field-Weighted Citation Impact
0.25

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Physical Spatial-Temporal Weighted Graph Construction for FTU's Health Condition Assessment

Y Fei, Fengyuan Zhang, Yuxin Li, Jin Zhang et al.
1 citations
Context-Aware Activity Recognition Systems
0.25
article

Physical Spatial-Temporal Weighted Graph Construction for FTU's Health Condition Assessment

Y Fei, Fengyuan Zhang, Yuxin Li, Jin Zhang, Jie Liu, Yujie Liu
article en
1 citations

Abstract

To ensure the dependable operation of hydropower generation, substantial research efforts have been dedicated to the health condition assessment (HCA) of Francis turbine units (FTUs). Typical HCA strategies encompass the creation of health benchmark model (HBM) and the calculation of performance degradation indicators (PDIs). However, Previous investigations have primarily concentrated on either the temporal or spatial relationships within pulsation signals and working condition parameters, ignoring the complex interactions between these relationships. In addition, a simplistic averaging of temporal and spatial relationships can lead to the loss of critical information. This paper proposes a physical spatial-temporal weighted graph construction for the health condition assessment of FTUs. Initially, a simulation model of the FTUs is developed, utilizing computational fluid dynamics theories to produce simulated pulsation signals. Then, actual pulsation signals are used to refine the simulation model, achieving a high-fidelity representation. Subsequently, the pulsation signals are modeled using graph data to capture both physical spatial relationships and essential features. On this basis, time-related statistical parameters are employed to allocate weights to each identified relationship. Furthermore, the integration of the feature relationships with physical spatial relationships leads to the creation of a physical spatial-temporal weighted graph, enhancing its signal representation capability. Ultimately, this graph is fed into the graph convolutional model to obtain HBM. To assess the performance, the PDI is calculated by extracting the Euclidean distance between the comprehensive features derived from the HBM-predicted labels and the actual signals observed in the degraded state. Validation experiment is conducted to verify the effectiveness of proposed method through the utilization of real-time monitoring data and working condition parameters from the FTU deployed within the hydropower station.

Tongji University (CN), Huazhong University of Science and Technology (CN)
National Natural Science Foundation of China, Natural Science Foundation of Hubei Province, Shenzhen Knowledge Innovation Program
Openalex Percentile: Top 42%
Context-Aware Activity Recognition Systems
0.25
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