Time domain graph-based anomaly detection approach applied to a real industrial problem

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dc.creatorWagner J. de Alvarenga Júnior
dc.creatorFelipe V. Campos
dc.creatorAlexsander C. A. A. Costa
dc.creatorTuribio T. Salis
dc.creatorEduardo Magalhães
dc.creatorLuiz C. B. Torres
dc.creatorAntonio P. Braga
dc.date.accessioned2025-06-23T14:09:56Z
dc.date.accessioned2025-09-09T01:29:10Z
dc.date.available2025-06-23T14:09:56Z
dc.date.issued2022
dc.identifier.doi10.1016/j.compind.2022.103714
dc.identifier.issn01663615
dc.identifier.urihttps://hdl.handle.net/1843/83045
dc.languageeng
dc.publisherUniversidade Federal de Minas Gerais
dc.relation.ispartofComputers in Industry
dc.rightsAcesso Restrito
dc.subjectAprendizado de máquina
dc.subject.otherAnomaly detection, Gabriel graph, Dominating set, Deep autoenconders, Machine learning
dc.subject.otherA graph-based approach to anomaly detection is presented. This work shows that dominating set incorporates statistical properties of a data set. The concept of a space coverage to encapsulate a data set, using dominating sets, is presented. The new approach brings results similar to deep autoencoders, without the need to extensively set parameters.
dc.titleTime domain graph-based anomaly detection approach applied to a real industrial problem
dc.typeArtigo de periódico
local.citation.spage103714
local.citation.volume142
local.description.resumoDetecting anomalies in industrial processes is a critical task. Prior fault detection can reduce company costs, and most importantly, may prevent accidents and environmental damage. Anomaly detection can be treated as drift detection, as both aims at identifying changes in data that happen unexpectedly over time. In this paper, a graph-based approach to anomaly detection is presented. Based on graph theory and set coverage, the method brings results similar to deep autoencoders, without the need to extensively set parameters.
local.publisher.countryBrasil
local.publisher.departmentENG - DEPARTAMENTO DE ENGENHARIA ELETRÔNICA
local.publisher.initialsUFMG
local.url.externahttps://www.sciencedirect.com/science/article/pii/S0166361522001117

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