Application of Probabilistic Risk Analysis in the Overhaul of Aero-engines using a Combination of Bayesian Networks and Fuzzy Set Theory – A Case Study

Identifying and responding to risks in aircraft maintenance activities is fundamental since an engine failure during a flight may cause a forced landing and, tragically, cause deaths. This reality makes monitoring, identifying, and prioritizing risk treatment during aero-engine maintenance essential. In the last European Congress for Reliability and Safety, held in 2022 in Dublin, the author presented a method for Probabilistic Risk Analysis in the Overhaul of Aero-engines using a combination of Bayesian Networks and Fuzzy Set Theory aiming at meeting Civil Aviation Agency Regulations and the requirements AS9100. This study complements the one presented in Esrel 2022 and demonstrates the method application. As a result, combining the Bayesian network modeling method with the Fuzzy Set Theory, referred to as Fuzzy Bayesian Network (FBN), proved to be a more effective and precise method to combine risks generated from different sources. The contribution is significant since the proposed method allows process optimization and risk reduction in the repair station and permits decision-makers to assign funds for critical activities. It can affect the company's risk management processes and help understand performance and safety during engine overhaul. Although conducted in a repair station, it can be generalized to other industries and fields of work whose safety is affected by risks resulting in waste, rework, and unnecessary energy consumption.

Keywords: Bayesian Belief Networks (BBN), Fuzzy Set Theory (FST), Fuzzy Bayesian Network (FBN), Risk management and operational safety.

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