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Yanjun Chen1,2,3, Min Zhou1,2,3, Meizhou Zhang1,2,3
1Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, China.
This study introduces a knowledge graph methodology for intelligent production line fault diagnosis. It enhances fault recognition and diagnosis efficiency by integrating an ALBERT-BiLSTM-Attention-CRF model and Neo4j graph database.
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