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Multi-objective optimization of design and testing of safety instrumented systems with MooN voting architectures using a genetic algorithm

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Multi-objective optimization of design and testing of safety instrumented systems with MooN voting architectures using a genetic algorithm

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dc.contributor.author Torres Echeverria, Alejandro Carlos es_ES
dc.contributor.author Martorell Alsina, Sebastián Salvador es_ES
dc.contributor.author Thompson, Amber es_ES
dc.date.accessioned 2017-04-07T07:26:34Z
dc.date.available 2017-04-07T07:26:34Z
dc.date.issued 2012-10
dc.identifier.issn 0951-8320
dc.identifier.uri http://hdl.handle.net/10251/79553
dc.description.abstract This paper presents the optimization of design and test policies of safety instrumented systems using MooN voting redundancies by a multi-objective genetic algorithm. The objectives to optimize are the Average Probability of Dangerous Failure on Demand, which represents the system safety integrity, the Spurious Trip Rate and the Lifecycle Cost. In this way safety, reliability and cost are included. This is done by using novel models of time-dependent probability of failure on demand and spurious trip rate, recently published by the authors. These models are capable of delivering the level of modeling detail required by the standard IEC 61508. Modeling includes common cause failure and diagnostic coverage. The Probability of Failure on Demand model also permits to quantify results with changing testing strategies. The optimization is performed using the multi-objective Genetic Algorithm NSGA-II. This allows weighting of the trade-offs between the three objectives and, thus, implementation of safety systems that keep a good balance between safety, reliability and cost. The complete methodology is applied to two separate case studies, one for optimization of system design with redundancy allocation and component selection and another for optimization of testing policies. Both optimization cases are performed for both systems with MooN redundancies and systems with only parallel redundancies. Their results are compared, demonstrating how introducing MooN architectures presents a significant improvement for the optimization process. es_ES
dc.language Inglés es_ES
dc.publisher Elsevier es_ES
dc.relation.ispartof Reliability Engineering and System Safety es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Safety system es_ES
dc.subject Optimization es_ES
dc.subject Genetic algorithms es_ES
dc.subject K-out-of-n es_ES
dc.subject Probability of failure on demand es_ES
dc.subject Spurious trip rate es_ES
dc.subject.classification INGENIERIA NUCLEAR es_ES
dc.title Multi-objective optimization of design and testing of safety instrumented systems with MooN voting architectures using a genetic algorithm es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1016/j.ress.2012.03.010
dc.rights.accessRights Cerrado es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Ingeniería Química y Nuclear - Departament d'Enginyeria Química i Nuclear es_ES
dc.description.bibliographicCitation Torres Echeverria, AC.; Martorell Alsina, SS.; Thompson, A. (2012). Multi-objective optimization of design and testing of safety instrumented systems with MooN voting architectures using a genetic algorithm. Reliability Engineering and System Safety. 106:45-60. doi:10.1016/j.ress.2012.03.010 es_ES
dc.description.accrualMethod Senia es_ES
dc.relation.publisherversion http://dx.doi. org/10.1016/j.ress.2012.03.010 es_ES
dc.description.upvformatpinicio 45 es_ES
dc.description.upvformatpfin 60 es_ES
dc.type.version info:eu repo/semantics/publishedVersion es_ES
dc.description.volume 106 es_ES
dc.relation.senia 235569 es_ES


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