Research Article
Fault Diagnosis with BERT Bi-LSTM-assisted Knowledge Graph Aided by Attention Mechanism for Hydro-Power Plants
@INPROCEEDINGS{10.1007/978-3-031-51399-2_5, author={Bilei Guo and Yining Wang and Weifeng Pan and Yanlin Sun and Yuwen Qian}, title={Fault Diagnosis with BERT Bi-LSTM-assisted Knowledge Graph Aided by Attention Mechanism for Hydro-Power Plants}, proceedings={Tools for Design, Implementation and Verification of Emerging Information Technologies. 18th EAI International Conference, TRIDENTCOM 2023, Nanjing, China, November 11-13, 2023, Proceedings}, proceedings_a={TRIDENTCOM}, year={2024}, month={1}, keywords={Hydro-power Plant Fault Diagnose BERT Knowledge Graph Bi-LSTM}, doi={10.1007/978-3-031-51399-2_5} }- Bilei Guo
Yining Wang
Weifeng Pan
Yanlin Sun
Yuwen Qian
Year: 2024
Fault Diagnosis with BERT Bi-LSTM-assisted Knowledge Graph Aided by Attention Mechanism for Hydro-Power Plants
TRIDENTCOM
Springer
DOI: 10.1007/978-3-031-51399-2_5
Abstract
To minimize the risk of Hydro-Power Plant failure, it’s crucial to detect and precisely repair the damaged components. In this paper, we propose a knowledge graph-based fault diagnosis method for Hydro-Power Plants. Then, the improved BiLSTM-CRF algorithm is developed to recognize entities for fault diagnosis, and the BERT relationship extraction algorithm is designed to construct a fault diagnosis knowledge graph for the Hydro-Power Plant. The real experimental test results validate the proposed methodology.
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