Prognosis and Detection of Experimental Failures in Open Field Diesel Engines Applying Wienerˈs Artificial Immunological Systems

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dc.contributor.authorOuta, Roberto
dc.contributor.authorChavarette, Fábio Roberto [UNESP]
dc.contributor.authorToro, Paulo Fernando
dc.contributor.authorGonçalves, Aparecido Carlos [UNESP]
dc.contributor.authorSantos, Lucas Henrique dos [UNESP]
dc.contributor.institutionFaculty of Technology of Araçatuba
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.contributor.institutionFaculty of Technology of Catanduva
dc.date.accessioned2021-06-25T10:57:16Z
dc.date.available2021-06-25T10:57:16Z
dc.date.issued2021-12-01
dc.description.abstractThe high costs of open-field diesel engines arise from the lack of maintenance of these systems. Thus, the maintenance of this equipment has been treated as a great challenge, as some methods of data monitoring are not possible to be implemented, given the inadequate sensing conditions, plant location, local climate, facilities, even the methods and maintenance routines. In a second step, the labor is not qualified and of sufficient quantity to meet the demand, resulting in a slow and inefficient system. One of the challenges of predictive systems is to inform damage and failures in real time of the operating conditions of these machines and equipment. This work demonstrates the possibility of analyzing and detecting failures in open field predictive systems, using the concepts of vibration and acoustics in artificial intelligence. One of the results of this work demonstrates the robustness of the negative selection artificial immune system algorithm, whose application of the Wiener filter was of fundamental need. The other result demonstrates the versatility of conditioned use both or just one of the concepts between vibration and acoustics, in prognosis and fault detection. Considering the versatility of using these two techniques, it is possible to affirm that, the predictive systems of real time analysis have an effective solution directed to the area and, if implemented, it is of low cost and high efficiency.en
dc.description.affiliationFaculty of Technology of Araçatuba Department of Biofuels, Av. Prestes Maia, 1764 - Ipanema
dc.description.affiliationUNESP - Instituto de Química Department of Engenharia Física e Matemática, Rua Prof. Francisco Degni, 55 – Quitandinha
dc.description.affiliationFaculty of Technology of Catanduva Department of Automação Indusrial, Av. Rua Maranhão, 898 - Centro
dc.description.affiliationUNESP - Univ. Estadual Paulista Faculty of Engineering of Ilha Solteira Department of Mechanical Engineering
dc.description.affiliationUnespUNESP - Instituto de Química Department of Engenharia Física e Matemática, Rua Prof. Francisco Degni, 55 – Quitandinha
dc.description.affiliationUnespUNESP - Univ. Estadual Paulista Faculty of Engineering of Ilha Solteira Department of Mechanical Engineering
dc.format.extent1-12
dc.identifierhttp://dx.doi.org/10.22055/jacm.2020.34972.2525
dc.identifier.citationJournal of Applied and Computational Mechanics, v. 7, n. 1, p. 1-12, 2021.
dc.identifier.doi10.22055/jacm.2020.34972.2525
dc.identifier.issn2383-4536
dc.identifier.scopus2-s2.0-85103644246
dc.identifier.urihttp://hdl.handle.net/11449/207558
dc.language.isoeng
dc.relation.ispartofJournal of Applied and Computational Mechanics
dc.sourceScopus
dc.subjectAcoustics
dc.subjectAIS-negative selection
dc.subjectPredictive system
dc.subjectVibration
dc.subjectWiener filter
dc.titlePrognosis and Detection of Experimental Failures in Open Field Diesel Engines Applying Wienerˈs Artificial Immunological Systemsen
dc.typeArtigo
unesp.author.orcid0000-0002-8649-1722[1]
unesp.author.orcid0000-0002-1203-7586[2]
unesp.author.orcid0000-0002-6127-0026[3]
unesp.author.orcid0000-0001-5376-3392[4]
unesp.author.orcid0000-0002-1348-9728[5]

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