Enabling the usage of UML in the verification of railway systems: The DAM-rail approachShow others and affiliations
2013 (English)In: Reliability Engineering & System Safety, ISSN 0951-8320, E-ISSN 1879-0836, Vol. 120, p. 112-126Article in journal (Refereed) Published
Abstract [en]
The need for integration of model-based verification into industrial processes has produced several attempts to define Model-Driven solutions implementing a unifying approach to system development. A recent trend is to implement tool chains supporting the developer both in the design phase and V&V activities. In this Model-Driven context, specific domains require proper modelling approaches, especially for what concerns RAM (Reliability, Availability, Maintainability) analysis and fulfillment of international standards. This paper specifically addresses the definition of a Model-Driven approach for the evaluation of RAM attributes in railway applications to automatically generate formal models. For this aim we extend the MARTE-DAM UML profile with concepts related to maintenance aspects and service degradation, and show that the MARTE-DAM framework can be successfully specialized for the railway domain. Model transformations are then defined to generate Repairable Fault Tree and Bayesian Network models from MARTE-DAM specifications. The whole process is applied to the railway domain in two different availability studies. © 2013 Elsevier Ltd.
Place, publisher, year, edition, pages
Elsevier , 2013. Vol. 120, p. 112-126
Keywords [en]
Availability analysis, Formal models, Model-Driven engineering, Railway systems, RAM requirements, UML profiles, Bayesian networks, Dams, Markup languages, Railroads, Rails, Reliability analysis, Unified Modeling Language, Formal model, Railway system, Railroad transportation
National Category
Embedded Systems
Research subject
Computer Science, Software Technology
Identifiers
URN: urn:nbn:se:mdh:diva-47757DOI: 10.1016/j.ress.2013.06.032ISI: 000324974000015Scopus ID: 2-s2.0-84885573893OAI: oai:DiVA.org:mdh-47757DiVA, id: diva2:1427450
2018-06-042020-04-292021-12-21Bibliographically approved