Hyperdimensional computing in industrial systems: the use-case of distributed fault isolation in a power plant

Denis Kleyko, Evgeny Osipov, Nikolaos Papakonstantinou, Valeriy Vyatkin

Research output: Contribution to journalArticleScientificpeer-review

8 Citations (Scopus)
111 Downloads (Pure)

Abstract

This article 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 same scenario the proposed approach shows comparable performance while being distributed and requiring simple binary operations, which allow for a fast and efficient implementation in a hardware.

Original languageEnglish
Pages (from-to)30766-30777
JournalIEEE Access
Volume6
DOIs
Publication statusPublished - 28 May 2018
MoE publication typeA1 Journal article-refereed

Keywords

  • Automation
  • complex systems
  • Computational modeling
  • distributed fault isolation
  • distributed representation
  • Feature extraction
  • Holographic Graph Neuron
  • hyperdimensional computing
  • Machine learning
  • machine learning
  • Neurons
  • Sensors
  • Training
  • Vector Symbolic Architectures

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