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| Content Provider | Springer Nature Link |
|---|---|
| Author | Kempf, Michael |
| Copyright Year | 2007 |
| Abstract | Die momentan bekannten Methoden zur Abschätzung des technischen Risikos während eines Entwicklungsprojektes in der industriellen Praxis wie z.B. die Fehlermöglichkeits- und -einflussanalyse, die Fehlerbaumanalyse oder ähnliche Ansätze tragen den Anforderungen an eine effiziente Risikoanalyse nur eingeschränkt Rechnung. Insbesondere die Abhängigkeiten zwischen Ereignissen werden nicht adäquat modelliert, was zu Fehleinschätzungen bei der Risikobewertung führen kann. In diesem Beitrag wird ein Modell zur Risikobewertung auf der Basis der Theorie der Bayes’schen Netze vorgestellt. Dieses Risikomodell ist relativ einfach generierbar. Zunächst müssen die relevanten, potenziellen Fehlerereignisse und ihre Interdependenzen ermittelt werden. Anschließend müssen die Auftrittshäufigkeiten der jeweiligen Fehler sowie die Stärke der Fehlerrelationen, d.h. wie sicher zieht die Fehlerursache eine bestimmte Fehlerwirkung nach sich, spezifiziert werden. Durch das so generierte, probabilistisch bewertete Fehlernetz ist die Grundlage geschaffen, Risiken realitätsnah abzuschätzen, zu simulieren und zu bewerten. Sogar bei unsicherem Wissen in den frühen Entwicklungsphasen können Fehleranalysen durchgeführt werden, um die potenziellen, schwer zu erkennbaren Risikobereiche rechtzeitig aufzudecken. The currently known methods estimating the technical risk during a design project in industrial application such as e.g. failure mode and effects analysis, fault tree analysis or similar approaches don’t fully meet the requirements of an efficient risk analysis. Especially dependencies between events are not modeled adequately which can result in misinterpretations with risk assessment. In this article a model for risk assessment using the theory of Bayesian Networks is presented. The risk model is relatively easy to generate. First of all, the relevant potential failure events and their interactions have to be determined. Afterwards the frequencies of occurrence of the respective failures as well as the strength of the failure relations, i.e. how certain does a failure cause result in a specific failure effect, have to be specified. With the resulting probabilistic failure net a sound basis is established to realistically evaluate, simulate and assess risks. Even with uncertain knowledge at early stages of product design, failure analyses can be carried out to reveal potential and hard to detect risks. |
| Starting Page | 85 |
| Ending Page | 94 |
| Page Count | 10 |
| File Format | |
| ISSN | 01783564 |
| Journal | Informatik - Forschung und Entwicklung |
| Volume Number | 22 |
| Issue Number | 2 |
| e-ISSN | 09492925 |
| Language | German |
| Publisher | Springer-Verlag |
| Publisher Date | 2007-11-01 |
| Publisher Place | Berlin, Heidelberg |
| Access Restriction | Subscribed |
| Subject Keyword | Risk Analysis Bayesian Networks Stochastic Simulation Theory of Computation Data Structures, Cryptology and Information Theory Software Engineering/Programming and Operating Systems Computer Systems Organization and Communication Networks Computer Hardware Computer Science |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Science |
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