Chen Yanjun, Zhou Min, Zhang Meizhou, Zha Meng
Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, China.
Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430081, China.
Sensors (Basel). 2025 Jun 23;25(13):3912. doi: 10.3390/s25133912.
In order to enhance the management and application of fault knowledge within intelligent production lines, thereby increasing the efficiency of fault diagnosis and ensuring the stable and reliable operation of these systems, we propose a fault diagnosis methodology that leverages knowledge graphs. First, we designed an ontology model for fault knowledge by integrating textual features from various components of the production line with expert insights. Second, we employed the ALBERT-BiLSTM-Attention-CRF model to achieve named entity and relationship recognition for faults in intelligent production lines. The introduction of the ALBERT model resulted in a 7.3% improvement in the score compared to the BiLSTM-CRF model. Additionally, incorporating the attention mechanism in relationship extraction led to a 7.37% increase in the score. Finally, we utilized the Neo4j graph database to facilitate the storage and visualization of fault knowledge, validating the effectiveness of our proposed method through a case study on fault diagnosis in CNC machining centers. The research findings indicate that this method excels in recognizing textual entities and relationships related to faults in intelligent production lines, effectively leveraging prior knowledge of faults across various components and elucidating their causes. This approach provides maintenance personnel with an intuitive tool for fault diagnosis and decision support, thereby enhancing diagnostic accuracy and efficiency.
为了加强智能生产线中故障知识的管理与应用，从而提高故障诊断效率并确保这些系统稳定可靠运行，我们提出一种利用知识图谱的故障诊断方法。首先，通过将生产线各组件的文本特征与专家见解相结合，设计了一个故障知识本体模型。其次，采用ALBERT-BiLSTM-Attention-CRF模型实现智能生产线中故障的命名实体和关系识别。与BiLSTM-CRF模型相比，引入ALBERT模型使得分提高了7.3%。此外，在关系提取中加入注意力机制使得分提高了7.37%。最后，利用Neo4j图数据库促进故障知识的存储和可视化，通过对数控加工中心故障诊断的案例研究验证了所提方法的有效性。研究结果表明，该方法在识别智能生产线中与故障相关的文本实体和关系方面表现出色，有效利用了各组件的故障先验知识并阐明其原因。这种方法为维修人员提供了直观的故障诊断和决策支持工具，从而提高了诊断准确性和效率。
