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%.
关键词
断纸/故障诊断/知识图谱/贝叶斯网络
Key words
paper break/fault diagnosis/knowledge graph/bayesian network
维普
万方数据