FAULT DIAGNOSIS OF ROLLING BEARINGS USING UNSUPERVISED DYNAMIC TIME WARPING-AIDED ARTIFICIAL IMMUNE SYSTEM

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorNational Institute of Technology-
Autor(es): dc.contributorCentral University of Karnataka-
Autor(es): dc.contributorIndira Gandhi National Tribal University-
Autor(es): dc.creatorFerreira, Lucas Veronez Goulart-
Autor(es): dc.creatorRathour, Laxmi-
Autor(es): dc.creatorDabke, Devika-
Autor(es): dc.creatorChavarette, Fábio Roberto-
Autor(es): dc.creatorMishra, Vishnu Narayan-
Data de aceite: dc.date.accessioned2025-08-21T18:54:19Z-
Data de disponibilização: dc.date.available2025-08-21T18:54:19Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2022-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.14317/jami.2023.1257-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/303937-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/303937-
Descrição: dc.descriptionRotating machines heavily rely on an intricate network of interconnected sub-components, with bearing failures accounting for a substantial proportion (40% to 90%) of all such failures. To address this issue, intelligent algorithms have been developed to evaluate vibrational signals and accurately detect faults, thereby reducing the reliance on expert knowledge and lowering maintenance costs. Within the field of machine learning, Artificial Immune Systems (AIS) have exhibited notable potential, with applications ranging from malware detection in computer systems to fault detection in bearings, which is the primary focus of this study. In pursuit of this objective, we propose a novel procedure for detecting novel instances of anomalies in varying operating conditions, utilizing only the signals derived from the healthy state of the analyzed machine. Our approach incorporates AIS augmented by Dynamic Time Warping (DTW). The experimental out-comes demonstrate that the AIS-DTW method yields a considerable improvement in anomaly detection rates (up to 53.83%) compared to the conventional AIS. In summary, our findings indicate that our method represents a significant advancement in enhancing the resilience of AIS-based novelty detection, thereby bolstering the reliability of rotating machines and reducing the need for expertise in bearing fault detection.-
Descrição: dc.descriptionUNESP-Univ. Estadual Paulista Faculty of Engineering of Ilha Solteira Department of Mechanical Engineering-
Descrição: dc.descriptionDepartment of Mathematics National Institute of Technology, Chaltlang-
Descrição: dc.descriptionDepartment of Mathematics Central University of Karnataka, Block no. D-11, Alanda Road, Karnataka-
Descrição: dc.descriptionUNESP-Univ. Estadual Paulista Institute of Chemistry Department of Engineering Physics and Mathematics, Rua Prof. Francisco Degni, 55, Quitandinha-
Descrição: dc.descriptionDepartment of Mathematics Indira Gandhi National Tribal University, Amarkan-tak, Madhya Pradesh-
Descrição: dc.descriptionUNESP-Univ. Estadual Paulista Faculty of Engineering of Ilha Solteira Department of Mechanical Engineering-
Descrição: dc.descriptionUNESP-Univ. Estadual Paulista Institute of Chemistry Department of Engineering Physics and Mathematics, Rua Prof. Francisco Degni, 55, Quitandinha-
Formato: dc.format1257-1274-
Idioma: dc.languageen-
Relação: dc.relationJournal of Applied Mathematics and Informatics-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectAIS-
Palavras-chave: dc.subjectbearing fault-
Palavras-chave: dc.subjectDTW-
Palavras-chave: dc.subjectNovelty detection-
Palavras-chave: dc.subjectvibrations-
Título: dc.titleFAULT DIAGNOSIS OF ROLLING BEARINGS USING UNSUPERVISED DYNAMIC TIME WARPING-AIDED ARTIFICIAL IMMUNE SYSTEM-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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