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A Model-Based Approach to Qualified Process Automation for Anomaly Detection and Treatment
KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.), Embedded Control Systems.ORCID iD: 0000-0001-7048-0108
DFKI - German Research Center for Artificial Intelligence.
KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.), Mechatronics.
DFKI - German Research Center for Artificial Intelligence.
2016 (English)In: 21st IEEE International Conference on Emerging Technology & Factory Automation, ETFA 2016, 2016Conference paper (Refereed)
Abstract [en]

Modern machineries are becoming complex cyberphysicalsystems with increasingly intelligent support for processautomation. For the dependability and performance, acombination of measures for fault avoidance, robust architecture,and runtime anomaly handling is necessary. These in turn callfor a formalization of knowledge across different system lifecyclestages and a provision of novel methods and tools for qualifiedsystem synthesis and effective risk management. This paperpresents a model-based approach to qualified process automationfor the operation and maintenance of production systems. Thecontribution is centered on the formalizations of a wide range ofsystem concerns, and thereby a consolidation of the rationalebehind the design of run-time process logic in BPMN2.0. Inparticular, the approach allows an integration of formal systemdescriptions, FTA and FEMA based anomaly analysis, andexecutable process models for effective anomaly detection andtreatment. The approach adopts mature modeling methods andtools through EAST-ADL. In this paper, a prototype tool-chainwith MetaEdit+ Domain-Specific Modeling (DSM) Workbench,HiP-HOPS Analysis Tool and Camunda BPM Platform is alsopresented.

Place, publisher, year, edition, pages
2016.
Keyword [en]
Evolvable Production Systems (EPS), Cyber- Physical Systems (CPS), Model-Based Development (MBD), Domain-Specific Modeling (DSM), Process Automation (PA), Business Process Model and Notation (BPMN2.0), Industry 4.0
National Category
Embedded Systems Production Engineering, Human Work Science and Ergonomics
Identifiers
URN: urn:nbn:se:kth:diva-191215OAI: oai:DiVA.org:kth-191215DiVA: diva2:955502
Conference
21st IEEE International Conference on Emerging Technology & Factory Automation, ETFA 2016
Projects
EIT Digital – Cyber-Physical Systems for Smart Factories
Available from: 2016-08-25 Created: 2016-08-25 Last updated: 2016-08-29

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