【摘要】 纸机断纸是制约造纸企业提高产品质量和生产效益的关键原因。造纸生产过程具有高维、非线性和多变量耦合的特点，对断纸故障的预防和诊断提出了挑战。数据驱动的方法基于断纸故障历史数据建模，对断纸故障的预防起到了一定的效果。但该方法忽略了造纸工业中隐藏的机理和经验知识，无法提供对断纸原因的追根溯源。知识图谱作为一种揭示实体间关系的语义网络，可以实现断纸故障数据与知识的集成。基于本体技术的断纸知识图谱为断纸故障诊断提供了全面、可扩展的关联知识库。在此基础上，结合贝叶斯网络开发了断纸故障诊断模型，通过对某生活用纸企业断纸故障的案例分析，验证了该模型在断纸故障推理方面的有效性，断纸预测的正确率达到了85%。更多还原

【Abstract】 Paper break is the key reason that restricts papermaking mill from improving product quality and production efficiency.The papermaking process is characterized by high-dimensional, nonlinear and multi-variable coupling, which poses challenges to the prevention and diagnosis of paper break. The data-driven method is based on historical data of paper break and has a certain effect on the prediction of paper break. However, this method ignores the hidden mechanisms and empirical knowledge in the papermaking process and cannot provide traceability of paper break. As a semantic network that reveals the relationships between entities, the knowledge graph can integrate paper break data and knowledge. Based on the ontology technology, the paper break knowledge graph provides a comprehensive and scalable correlation knowledge base for paper break fault diagnosis. On this basis, a paper break fault diagnosis model is developed combined with Bayesian network. Through a case study of paper break in a tissue papermaking enterprise,the effectiveness of the model in the inference of the paper break fault is verified, the accuracy of paper break prediction reaches 85%.更多还原