Hyperdimensional Computing in Industrial Systems: The Use-Case of Distributed Fault Isolation in a Power Plant

Denis Kleyko, Evgeny Osipov, Nikolaos Papakonstantinou, Valeriy Vyatkin
2018 IEEE Access  
This paper presents an approach for distributed fault isolation in a generic system of systems. The proposed approach is based on the principles of hyperdimensional computing. In particular, the recently proposed method called Holographic Graph Neuron is used. We present a distributed version of Holographic Graph Neuron and evaluate its performance on the problem of fault isolation in a complex power plant model. Compared to conventional machine learning methods applied in the context of the
more » ... e scenario the proposed approach shows comparable performance while being distributed and requiring simple binary operations, which allow for a fast and efficient implementation in hardware. INDEX TERMS Vector symbolic architectures, Holographic Graph Neuron, distributed representation, complex systems, distributed fault isolation, hyperdimensional computing, machine learning. 30766 2169-3536 2018 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information. VOLUME 6, 2018 DENIS KLEYKO received the bachelor's degree (Hons.) in telecommunication systems and the master's degree (Hons.) in information systems from the Siberian State University of Telecommunications and Information Sciences, Novosibirsk, Russian, in 2011 and 2013, respectively. He is currently pursuing the Ph.D. degree with the
doi:10.1109/access.2018.2840128 fatcat:ew37xuv6pfcctitlqyoeo7oiey