Probabilistic risk analysis in manufacturing situational operation: application of modelling techniques and causal structure to improve safety performance

The use of probabilistic risk analysis in jet engines manufacturing process is essential to prevent failure. The objective of this study is to present a probabilistic risk analysis model to analyze the safety of this process. The standard risk assessment normally conducted is inadequate to address t...

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Published inInternational journal of production management and engineering Vol. 3; no. 1; pp. 33 - 42
Main Authors Pereira, Jose Cristiano, Alves Lima, Gilson Brito
Format Journal Article
LanguageEnglish
Published Universitat Politècnica de València 01.01.2015
Subjects
Bayesian Belief Net
Bayesian Belief Net-work
Probabilistic Risk Analysis
Situation Model
work
Online AccessGet full text
ISSN2340-4876
2340-5317
2340-4876
DOI10.4995/ijpme.2015.3287

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Abstract The use of probabilistic risk analysis in jet engines manufacturing process is essential to prevent failure. The objective of this study is to present a probabilistic risk analysis model to analyze the safety of this process. The standard risk assessment normally conducted is inadequate to address the risks. To remedy this problem, the model presented in this paper considers the effects of human, software and calibration reliability in the process. Bayesian Belief Network coupled to a Bow Tie diagram is used to identify potential engine failure scenarios. In this context and to meet this objective, an in depth literature research was conducted to identify the most appropriate modeling techniques and an interview were conducted with experts. As a result of this study, this paper presents a model that combines fault tree analysis, event tree analysis and a Bayesian Belief Networks into a single 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. The model is delivered in the form of a computer assisted decision tool supported by subject expert estimates.
AbstractList The use of probabilistic risk analysis in jet engines manufacturing process is essential to prevent failure. The objective of this study is to present a probabilistic risk analysis model to analyze the safety of this process. The standard risk assessment normally conducted is inadequate to address the risks. To remedy this problem, the model presented in this paper considers the effects of human, software and calibration reliability in the process. Bayesian Belief Network coupled to a Bow Tie diagram is used to identify potential engine failure scenarios. In this context and to meet this objective, an in depth literature research was conducted to identify the most appropriate modeling techniques and an interview were conducted with experts. As a result of this study, this paper presents a model that combines fault tree analysis, event tree analysis and a Bayesian Belief Networks into a single 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. The model is delivered in the form of a computer assisted decision tool supported by subject expert estimates.
Author Pereira, Jose Cristiano
Alves Lima, Gilson Brito
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Probabilistic Risk Analysis
Situation Model
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