Abstract
The use of probabilistic risk analysis in the jet engines manufacturing process is essential to prevent failure. It has been observed in the literature about risk management that the standard risk assessment is normally inadequate to address the risks in this process. To remedy this problem, the methodology presented in this paper covers the construction of a probabilistic risk analysis model, based on Bayesian Belief Network coupled to a bow-tie diagram. It considers the effects of human, software and calibration reliability to identify critical risk factors in this process. The application of this methodology to a particular jet engine manufacturing process is presented to demonstrate the viability of the proposed approach.
Highlights
Background and ContextCausal modeling using a bow-tie chart is a powerful tool for getting insight into the interdependencies between the constituent parts of complex system such as the manufacturing of jet engines
Considering the context presented above, this paper aims to present a proposal for probabilistic risk analysis based on bow-tie methodology combined with Bayesian Belief Network to analyze critical activities that can affect the reliability of the safety system in the manufacturing of jet engines
This paper presents a model that combines Fault Tree analysis, Event Tree analysis and Bayesian Belief Networks in an integrated model that can be used by decision makers to identify critical risk factors in order to allocate resources to improve the safety of the system
Summary
Causal modeling using a bow-tie chart is a powerful tool for getting insight into the interdependencies between the constituent parts of complex system such as the manufacturing of jet engines. As far as safety is concerned, the propagation of fault situations in the engine manufacturing process can be modeled and followed. Weaknesses in protection against fault propagation can be systematically determined. The power of causal modeling can be greatly enhanced if probabilities and logical dependencies can be quantified (Nureg, 2001). Quantification has limitations mainly related to complexity of model and scarcity of data
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