A Proposition for Combining Rough Sets, Fuzzy Logic and FRAM to Address Methodological Challenges in Safety Management: A Discussion Paper

Abstract

In recent years, the focus in safety management has shifted from failure-based analysis towards a more systemic perspective, redefining a successful or failed performance as a complex and emergent event rather than as a conclusion of singular errors or root causes. This paradigm shift has also necessitated the introduction of innovative tools capable of capturing the complex and dynamic nature of modern sociotechnical systems. In our research, we argued at previous stages for adopting a more systemic and human-centric perspective to evaluate the context of aircraft de-icing operations. The Functional Resonance Analysis Method (FRAM) was applied in the first stage for this purpose. Consequently, fuzzy logic was combined with FRAM in the second stage to provide a quantified representation of performance variability. Fuzzy logic was used as a quantification tool suitable for computing with natural language. Several limitations were found in the data collection and rule generation process for the first prototype. In the third phase, the model was further improved by integrating rough sets as a data-mining tool to generate and reduce the size of the rule base and classify outcomes. In this paper, we reflect on the three stages of the project and discuss in a qualitative manner the challenges and limitations faced in the development and application of the models. A summary of the advantages and disadvantages of the three models as experienced in our case are presented at the end. The objective is to present an outlook for future studies to address methodological limitations in the study of complex sociotechnical systems.

Keywords

• FRAM
• rough sets
• fuzzy logic
• aircraft de-icing
• safety and risk management
• performance variability