Construction and Application of Wind Turbine Diagnosis System Based on Knowledge Graph

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Construction and Application of Wind Turbine Diagnosis System Based on Knowledge Graph

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[1]CHEN Hong,CHEN Xincai,GONG Xiaobin,et al.Construction and Application of Wind Turbine Diagnosis System Based on Knowledge Graph[J].Journal of Zhengzhou University (Engineering Science),2023,44(06):54-60.[doi:10.13705/j.issn.1671-6833.2023.06.007]

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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 44卷 Number of periods: 2023 06 Page number: 54-60 Column: Public date: 2023-12-25

Title:: Construction and Application of Wind Turbine Diagnosis System Based on Knowledge Graph

Author(s):: CHEN Hong¹; 2; CHEN Xincai¹; GONG Xiaobin³; HAN Dongyang¹; LIU Huajie¹; 1. School of Mechanical and Power Engineering, Zhengzhou University, Zhengzhou 450001, China; 2. Department of Mechanical and Electrical, Hami Vocational and Technical College, Hami 839099, China; 3. School of Mechanical and Electrical Engineering, Zhengzhou University of Light Industry, Zhengzhou 450000, China

Keywords:: knowledge graph; knowledge extraction; wind turbine; fault diagnosis; ontology

CLC:: TP391；TK8

DOI:: 10.13705/j.issn.1671-6833.2023.06.007

Abstract:: To address the precision problems in wind turbine fault diagnosis and maintenance processes, the lack of management of fault domain knowledge, and the large amount of historical fault data records left behind, a wind turbine fault diagnosis system was proposed to be constructed in the form of a knowledge graph. Firstly, knowledge extraction of fault texts was carried out by an improved named entity recognition model BERT-BiLSTM-CRF. The data set used text data of wind turbine fault cases and accident analysis in the past 10 years. And it was proved through experiments that the improved entity recognition method was 2. 54% more effective compared to the traditional model in the wind turbine fault domain. The extracted knowledge entities were then structurally analysed. As the traditional fault tree lacked purpose in actual fault reasoning, and each bottom event had different levels of importance to the top event, it was proposed that the characteristic attributes of the fault was introduced, as branching conditions, into the fault tree reasoning, to complete the fault tree qualitative and quantitative analysis, and the fault mode impact and hazard analysis ( FMECA) were combined to refine the fault domain knowledge model. Then Protg development tools were use to complete the ontology modelling of the fault tree structure based on the six-tuple concept, so that the constructed ontology knowledge base could meet the prerequisites for inference. Finally, the visualization of knowledge model was realized by Neo4j, and the ability of reading and writing knowledge data was improved.

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Last Update: 2023-10-22