Fault Detection in the Hyperspace: Towards Intelligent Automation Systems

被引：0
|
作者：
Kleyko, Denis [1 ]
Osipov, Evgeny [1 ]
Papakonstantinou, Nikolaos [2 ]
Vyatkin, Valeriy [1 ,3 ]
Mousavi, Arash [1 ]
机构：
[1] Lulea Univ Technol, Dept Comp Sci Elect & Space Engn, S-97753 Lulea, Sweden
[2] VTT Tech Res Ctr Finland, Espoo, Finland
[3] Aalto Univ, Dept Elect Engn & Automat, FI-00076 Espoo, Finland
来源：
PROCEEDINGS 2015 IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL INFORMATICS (INDIN) | 2015年
关键词：
Hyperdimensional Computing; Vector Symbolic Architecture; Holographic Graph Neuron; fault detection; nuclear power plant; QUANTITATIVE MODEL; DIAGNOSIS;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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.
引用
收藏
页码：1219 / 1224
页数：6
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