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A Cloud-Based Decision Support System for Self-Healing in Distributed Automation Systems Using Fault Tree Analysis
Shanghai Jiao Tong University, Department of Automation.
Stanford University, Department of Computer Science.
Shenyang Institute of Automation, China Academy of Science.
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science. Aalto University, Espoo.ORCID iD: 0000-0002-9315-9920
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2018 (English)In: IEEE Transactions on Industrial Informatics, ISSN 1551-3203, E-ISSN 1941-0050, Vol. 14, no 3, p. 989-1000Article in journal (Refereed) Published
Abstract [en]

Downtime is a key performance index for industrial automation systems. An industrial automation system achieves maximum productivity when its downtime is reduced to the minimum. One approach to minimize downtime is to predict system faults and recover from them automatically. A cloud-based decision support system is proposed for rapid problem identifications and to assist the self-management processes. By running multiple parallel simulations of control software with real-time inputs ahead of system time, faults could be detected and corrected automatically using autonomous industrial software agents. Fault trees, as well as control algorithms, are modeled using IEC 61499 function blocks that can be directly executed on both physical controllers and cloud services. A case study of water heating process is used to demonstrate the self-healing process supported by the cloud-based decision support system.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2018. Vol. 14, no 3, p. 989-1000
National Category
Computer Sciences
Research subject
Dependable Communication and Computation Systems
Identifiers
URN: urn:nbn:se:ltu:diva-68137DOI: 10.1109/TII.2018.279150ISI: 000426700600017OAI: oai:DiVA.org:ltu-68137DiVA, id: diva2:1194541
Note

Validerad;2018;Nivå 2;2018-04-03 (andbra)

Available from: 2018-04-03 Created: 2018-04-03 Last updated: 2018-04-03Bibliographically approved

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