Contribution of dynamic experience feedback to the quantitative estimation of risks for preventing accidents: A proposed methodology for machinery safety

This paper proposes a methodological approach for designing a dynamic risk identification and estimation support tool for machinery safety. Based on a comprehensive literature review and by updating the risks through dynamic experience feedback integrated into quantitative risk estimation, the methodology makes it possible to better equip machinery safety practitioners to intervene effectively. The methodology combines dynamic risk identification and Logical Analysis of Data (LAD) as two potential methods applied in machinery safety. LAD is an artificial intelligence technique introduced to extract information from accident reports in order to analyze machinery-related accidents in the workplace, which has not been covered in previous studies of machinery safety. The practical relevance and feasibility of the proposed methodology are explained using an example involving two accidents that occurred on the same machine in the same sawmill.