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

1 Citation (Scopus)

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
Number of pages12
JournalIEEE Access
Volume6
DOIs
Publication statusPublished - 2018
MoE publication typeA1 Journal article-refereed

Fingerprint

Neurons
Power plants
Learning systems
Hardware
System of systems

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

Cite this

@article{7727e691d7a34b23a8887bb10b032efc,
title = "Hyperdimensional computing in industrial systems: the use-case of distributed fault isolation in a power plant",
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.",
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",
author = "Denis Kleyko and Evgeny Osipov and Nikolaos Papakonstantinou and Valeriy Vyatkin",
year = "2018",
doi = "10.1109/ACCESS.2018.2840128",
language = "English",
volume = "6",
pages = "30766--30777",
journal = "IEEE Access",
issn = "2169-3536",
publisher = "Institute of Electrical and Electronic Engineers IEEE",

}

Hyperdimensional computing in industrial systems : the use-case of distributed fault isolation in a power plant. / Kleyko, Denis; Osipov, Evgeny; Papakonstantinou, Nikolaos; Vyatkin, Valeriy.

In: IEEE Access, Vol. 6, 2018, p. 30766-30777.

Research output: Contribution to journalArticleScientificpeer-review

TY - JOUR

T1 - Hyperdimensional computing in industrial systems

T2 - the use-case of distributed fault isolation in a power plant

AU - Kleyko, Denis

AU - Osipov, Evgeny

AU - Papakonstantinou, Nikolaos

AU - Vyatkin, Valeriy

PY - 2018

Y1 - 2018

N2 - 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.

AB - 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.

KW - Automation

KW - complex systems

KW - Computational modeling

KW - distributed fault isolation

KW - distributed representation

KW - Feature extraction

KW - Holographic Graph Neuron

KW - hyperdimensional computing

KW - Machine learning

KW - machine learning

KW - Neurons

KW - Sensors

KW - Training

KW - Vector Symbolic Architectures

UR - http://www.scopus.com/inward/record.url?scp=85047613488&partnerID=8YFLogxK

U2 - 10.1109/ACCESS.2018.2840128

DO - 10.1109/ACCESS.2018.2840128

M3 - Article

AN - SCOPUS:85047613488

VL - 6

SP - 30766

EP - 30777

JO - IEEE Access

JF - IEEE Access

SN - 2169-3536

ER -