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Benefits of Bayesian Network Models by Christophe Simon, Philippe Weber

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Conclusion

Despite the maturity of BN’s formalism and their application in industrial problems, it is necessary to formalize the procedures or the construction methodologies of the model. If the data are not available for automatic learning of the model, a major effort must be made to focus on the structuring of knowledge and construction of the model because the robustness of the model is based on the quality and rightness of the modeling methodology.

Modeling the functional consequences of failures from structured knowledge

Several modeling methods are proposed in this book to formalize the BN to compute the reliability and impact of degradation of components in the functional architecture of the system. Components and failure modes are then naturally described by multi-state variables, e.g. derived from FMEA or HAZOP analysis, which is difficult to model by other methods.

This book proposes defining the model with different levels of abstraction according to knowledge from functional analysis tools such as structured analysis and design technique, also known as IDEF0. The impact (probabilistically) of failure propagated on the system functions appears in terms of realization of the function or occurrence of failure modes.

The PRM modeling approach is very interesting because it allows the development of models from elementary structures. Thanks to this flexibility, the construction of a model with different levels of abstraction is facilitated. PRM capabilities capitalize ...

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