Intelligent fault diagnosis of steel production line based on knowledge graph recommendation

The production of steel requires a number of processes such as raw material transportation, smelting, hot rolling and cold rolling, and the completion of these processes also requires the support of various raw materials and resources such as electricity and hot air. The complicated process flow, many types of equipment and complex equipment-fault relationship make it much more difficult to perform fault diagnosis. In order to achieve accurate and efficient fault diagnosis, a fault diagnosis model based on knowledge graph (KG) recommendation is proposed based on the above-mentioned characteristics of steel production line fault diagnosis, which uses a water wave propagation-like approach to obtain a multi-order representation of the equipment to be diagnosed in the KG and aggregates its depth representation for fault diagnosis. The construction process of the water wave set is optimized according to the characteristics of steel production line fault diagnosis to improve the final diagnosis effect. A KG embedding via paired relation vectors (PairRE) model is also introduced for joint training to learn the complex relationships in the KG. Finally, the scientific validity and effectiveness of the proposed method is verified by comparing the actual production data of a large steel company’s hot rolling line with several representative models in experiments and case studies. © 2024 South China University of Technology. All rights reserved.