Fault diagnosis system for hydropower equipment based on expert knowledge base

Dechang Hu; Yongxin Sun; Jun Li; Yanchun Dong; Hengshuang Tian; Hao Yun

doi:10.1117/12.3055338

12 December 2024 Fault diagnosis system for hydropower equipment based on expert knowledge base

Dechang Hu, Yongxin Sun, Jun Li, Yanchun Dong, Hengshuang Tian, Hao Yun

Author Affiliations +

Proceedings Volume 13439, Fourth International Conference on Testing Technology and Automation Engineering (TTAE 2024); 1343912 (2024) https://doi.org/10.1117/12.3055338
Event: Fourth International Conference on Testing Technology and Automation Engineering (TTAE 2024), 2024, Xiamen, China

Abstract

In order to supply the demands of modern hydropower stations, a fault diagnosis system based on SOA Architecture was built. This system adopts Depth First Search Strategy with Pruning Processing in the matching process between fault and feature, which can improve the efficiency of inference engine. The inference is achieved by completely matching or partially matching with question. By storing the data of knowledge base with Neo4j, this system can manage expert knowledge base effectively. Based on "Confidence level", this system implement dynamic learning with expert assisting. Through this system, hydropower stations can prevent accidents and improve the maintenance mode, reduce the cost of operation, and improve the efficiency of machine.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Dechang Hu, Yongxin Sun, Jun Li, Yanchun Dong, Hengshuang Tian, and Hao Yun "Fault diagnosis system for hydropower equipment based on expert knowledge base", Proc. SPIE 13439, Fourth International Conference on Testing Technology and Automation Engineering (TTAE 2024), 1343912 (12 December 2024); https://doi.org/10.1117/12.3055338

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