Risk assessment and key factors analysis of confined space operation using Knowledge Graph, Association Rules Mining and Bayesian Network

Confined space operation involves working in (semi-)enclosed spaces. While confined space is an important workspace in chemical industry and urban development, there is also an increased risk of injury or even death due to hazardous factors, such as limited entry and exit area or a lack of adequate ventilation. In this paper, an intelligent method was proposed combining knowledge graph (KG), association rules mining (ARM) and Bayesian network (BN) to assess the risk and determine the key factors for confined space operation. First, a causative indicator system was established using 601 previous accidents, for which KG was also constructed to allow automatic extraction of accident causes. Based on the association rules determined by ARM, a risk assessment method was developed using BN. The key factors were analyzed and countermeasures were proposed. The results show that the association between failure to conduct ventilation detection on the site and failure to wear safety protective equipment demonstrates significant correlation strength, while association rule between inadequate safety education and training and failure to wear safety protective equipment also high. By the analysis in BN, it can be seen that the probability of confined space operation accidents is significantly higher (59%) with the baseline probability of nodes in BN. The other important factors include failure to wear safety protective equipment, blindly rescue, insufficient provision of protective equipment and operation without a license. This study can evaluate the risk and determine key factors in a data-driven manner to reduce the subjectivity, which provides a reference for the targeted safety management of confined space operation.