Fault detection in the hyperspace: towards intelligent automation systemsShow others and affiliations
2015 (English)In: 2015 IEEE 13th International Conference on Industrial Informatics (INDIN): Proceedings, IEEE, 2015, p. 1219-1224Conference paper, Published paper (Refereed)
Abstract [en]
This article presents a methodology for intelligent, biologically inspired fault detection system for generic complex systems of systems. The proposed methodology utilizes the concepts of associative memory and vector symbolic architectures, commonly used for modeling cognitive abilities of human brain. Compared to classical methods of artificial intelligence used in the context of fault detection the proposed methodology shows an unprecedented performance, while featuring zero configuration and simple operations.
Place, publisher, year, edition, pages
IEEE, 2015. p. 1219-1224
Series
IEEE Conference on Industrial Informatics, ISSN 1935-4576, E-ISSN 2378-363X
Keywords [en]
Hyperdimensional Computing, Vector Symbolic Architecture, Holographic Graph Neuron, fault detection, nuclear power plant
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:oru:diva-116480DOI: 10.1109/INDIN.2015.7281909ISI: 000380453900175Scopus ID: 2-s2.0-84949512219OAI: oai:DiVA.org:oru-116480DiVA, id: diva2:1903096
Conference
13th International Conference on Industrial Informatics, (INDIN 2015), Cambridge, United Kingdom, July 22-24, 2015
Funder
The Swedish Foundation for International Cooperation in Research and Higher Education (STINT), IG2011-2025Luleå University of Technology, 381940
Note
This work is partially supported by the Swedish Foundation for International Cooperation in Research and Higher Education (STINT), institutional grant IG2011-2025, the grant 381940 of Luleå University of Technology, and SAUNA project of the The Finnish Research Programme on Nuclear Power Plant Safety 2015 – 2018 (SAFIR2018) program.
2024-10-032024-10-032024-10-04Bibliographically approved