GABoT: A Lightweight Real-Time Adaptable Approach for Intelligent Fault Diagnosis of Rotating Machinery

Basit Öğe Kaydı
dc.authoridBAGCI DAS, DUYGU/0000-0003-4519-3531
dc.contributor.authorDas, Duygu Bagci
dc.contributor.authorDas, Oguzhan
dc.date.accessioned2024-08-31T07:49:25Z
dc.date.available2024-08-31T07:49:25Z
dc.date.issued2024
dc.departmentEge Üniversitesien_US
dc.description.abstractPurpose As the concept of Industry 4.0 is introduced, the significance of Smart Fault Diagnosis in the industry is increased. Therefore, it is essential to develop accurate, robust, and lightweight intelligent fault diagnosis approach that can be executed in real-time even with embedded systems. Additionally, it is preferable to use a single method for multi-purposes such as the fault detection, identification, and severity assessment. This study proposed a new approach called GaBoT for fault diagnosis of rotating machinery to satisfy those requirements.Method The proposed approach adopted the concept of the ensemble of ensembles by boosting random forest. The statistical features of discrete wavelet transform were considered since they are easy and fast to obtain. Model optimization was conducted by employing genetic algorithm to alleviate the computational load without decreasing the model performance. The proposed approach has been validated by unseen data from an experimental dataset including shaft, rotor, and bearing faults.Results The experimental results indicate that the proposed approach can effectively find the fault type with 99.85% accuracy. Besides, it successfully determines the fault severity by accuracy values between 96.45 and 99.72%. GABoT can also determine the imbalance severity in the presence of three bearing faults.Conclusion Employing GA eliminated most of the redundant features and reduced the model execution time consumption. The results yielded that GABoT is a highly accurate model, and can be utilized in real-time fault diagnosis of rotating machinery.en_US
dc.description.sponsorshipNational Defense Universityen_US
dc.description.sponsorshipNo Statement Availableen_US
dc.identifier.doi10.1007/s42417-024-01440-x
dc.identifier.issn2523-3920
dc.identifier.issn2523-3939
dc.identifier.scopus2-s2.0-85195370831en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.1007/s42417-024-01440-x
dc.identifier.urihttps://hdl.handle.net/11454/104864
dc.identifier.wosWOS:001242200000001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer Heidelbergen_US
dc.relation.ispartofJournal of Vibration Engineering & Technologiesen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmz20240831_Uen_US
dc.subjectIntelligent Fault Analysisen_US
dc.subjectRotating Machineen_US
dc.subjectMachine Learningen_US
dc.subjectFault Severityen_US
dc.subjectGenetic Algorithmen_US
dc.subjectLightweight Aien_US
dc.titleGABoT: A Lightweight Real-Time Adaptable Approach for Intelligent Fault Diagnosis of Rotating Machineryen_US
dc.typeArticleen_US

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu