With the development of information technology in manufacturing enterprises, a large amount of equipment maintenance data and knowledge are recorded. These rich knowledge resources contain a vast amount of semantic and physical associations that have not yet been developed, resulting in a significant gap between equipment maintenance procedures and experiential knowledge. Therefore, this paper proposes a multi-source maintenance data management method called Industrial Dataspace (IDS), and on this basis, proposes a method for constructing an equipment maintenance knowledge graph (IDS-KG) that considers the causal relationships between faults in the equipment maintenance corpus. The method fixes procedural data on the ontology model at the upper layer of the knowledge graph and automatically mines maintenance information from empirical data, and ultimately achieves the fusion management of equipment maintenance procedure knowledge and empirical knowledge. The method is validated in the practical application of nuclear power equipment maintenance, and the experiments show that the method proposed in this paper is able to effectively fuse the procedural data and empirical data and structured as triplets, and at the same time, it is able to identify the hidden causal relationship between failures in the empirical data.

随着制造企业信息技术的发展，记录了大量的设备维护数据和知识。这些丰富的知识资源包含大量尚未开发的语义和物理关联，导致设备维护程序和经验知识之间存在巨大差距。因此，本文提出一种称为工业数据空间（IDS）的多源维护数据管理方法，并在此基础上提出一种考虑设备故障之间因果关系的设备维护知识图谱（IDS-KG）构建方法维护语料库。该方法将程序数据固定在知识图谱上层本体模型上，自动从经验数据中挖掘维修信息，最终实现设备维修程序知识与经验知识的融合管理。该方法在核电设备维修的实际应用中得到了验证，实验表明，本文提出的方法能够有效融合过程数据和经验数据，并以三元组的形式构建，同时能够识别经验数据中故障之间隐藏的因果关系。