Affiliations: School of Electronic Information and Artificial Intelligence, Shaanxi University of Science and Technology, Xi’an, Shaanxi, China
Correspondence:
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Corresponding author: Qinjun Li, School of Electronic Information and Artificial Intelligence, Shaanxi University of Science and Technology, Xi’an, Shaanxi, China. E-mail: liqinjun@sust.edu.cn.
Abstract: The volume and complexity of lighting system are increasing, and the traditional fault diagnosis method can not meet the requirements. It is proposed to use the strong knowledge association and analysis ability of knowledge graph on big data to assist fault analysts in the lighting system fault diagnosis. Firstly, the schema layer of the knowledge graph in the top-down style was designed, which defined the overall architecture of the lighting system fault diagnosis knowledge graph. Then, the BERT-BiLSTM-CRF model was constructed and trained for knowledge extraction by using self-built data set, and the data layer of the knowledge graph in the bottom-up style was built. And then, the fault diagnosis rule module was constructed and optimized by combining the knowledge graph with the deduction lattice algorithm. Finally, the knowledge graph was visualized by using the Neo4j graph database and its application process in fault diagnosis was analyzed. The experimental results show that the BERT-BiLSTM-CRF model has a 17.58% improvement in precision over the BiLSTM-CRF model for the lighting data knowledge extraction task, and has better accuracy and effectiveness. This method effectively improves the reliability and intelligent level of fault diagnosis of lighting system.
Keywords: Lighting system fault diagnosis, knowledge graph, knowledge extraction, deep learning, deduction lattice algorithm
