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| Clustering Coarsening Graph Model for Fault Diagnosis of Coal Mill Group |
| DENG Zhongyi
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| East China Electric Power Test and Research Institute, China Datang Corporation Science and Technology Research Institute Co Ltd, Hefei 230011, China |
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Abstract The coal mill group is one of the important equipment in thermal power plants. To ensure the safety and stability of the production process, a fault diagnosis method named clustering coarsening graph convolution neural network (CC-GCN) is proposed in this paper. Firstly, the graph structure is established between the original fault samples by KNN algorithm and converted into graph samples. Then, spectral clustering is used to compress the graph samples into multi-level coarsening graphs, and convolution operations and feature fusion are performed for each level of coarsening graph respectively. Finally, fault diagnosis is performed on the fault samples based on the graph classification method. Simulation experiments are carried out on two sets of data sets with different operation conditions of the coal mill group, and the results show that this method can not only effectively improve the accuracy of fault diagnosis, but also significantly reduce the running time of the model.
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Received: 09 November 2022
Published: 26 April 2024
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