A method for constructing fault knowledge graphs based on an improved hidden Markov Model: A case study for papermaking industry

Abstract

As a technology and knowledge-intensive industry, the process industry, central to sustainable manufacturing goals, faces challenges with large volumes of dispersed data, high integration of production units, and complex workflows. Existing methods struggle to analyze unstructured mechanism and experience knowledge, leading to information silos. To support cleaner production through enhanced fault diagnosis and prevention, this study leverages knowledge graph theory. An improved Hidden Markov Model for industrial text segmentation is proposed, demonstrating a 3.2 % accuracy increase over general tools. By utilizing this method to effectively process unstructured data and extract valuable knowledge, a dedicated fault knowledge graph framework and ontology model for process industries is constructed. This knowledge graph is then integrated with machine learning algorithms to build an industrial status diagnosis model; crucially, it enables intelligent feature selection, bypassing complex dimensionality reduction tasks common in previous approaches. Through a case study on tissue paper break faults, the framework is demonstrated by establishing a paper break fault knowledge graph and diagnosis model. This approach provides causal reasoning for proactive interventions that reduce scrap rates and optimize resource utilization, key drivers for improving eco-efficiency and advancing green, sustainable operations within the process industries.