Analysis of a major-accident dataset by Association Rule Mining to minimise unsafe interfaces

Abstract

Major accidents may cause severe damage to humans and the environment, and can potentially lead to significant losses in a business and societal level. Thus, the understanding of these complex multi-attribute events through the analysis of past accidents might assist the search for strategies to improve engineering system’s safety and design robustness. Therefore, we aim to explore potential relationships among contributing factors by means of assessing approximately 200 major industrial accidents from the Multi-attribute Technological Accidents Dataset (MATA-D) created by Moura et al. Understanding this complex and high dimensional data on incidents, is the main purpose of this work. We apply association rule mining techniques and perform point-failure analysis in order to produce further insight into the dataset. Subsequently, key similarities among accidents’ contributing factors will be analysed, in order to disclose relevant associations and identify to which extent a limited number of driving forces might be generating undesirable events. Results will be regarded as additional indicators to reduce risky interfaces among contributing factors, and to indicate further managerial actions to minimise accidents. Conclusions to enable additional means to visualise and communicate risks to specific stakeholders are then discussed.