Enhancing vehicle fault diagnosis through multi-view sound analysis: integrating scalograms and spectrograms in a deep learning framework

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dc.contributor.authorAkbalik, Ferit
dc.contributor.authorYildiz, Abdulnasir
dc.contributor.authorErtugrul, Omer Faruk
dc.contributor.authorZan, Hasan
dc.date.accessioned2025-02-22T14:09:05Z
dc.date.available2025-02-22T14:09:05Z
dc.date.issued2025
dc.departmentDicle Üniversitesien_US
dc.description.abstractThis study presents a comprehensive framework for vehicle fault diagnosis using engine sound signals, leveraging deep learning models and a multi-view approach. Traditional methods for vehicle fault diagnosis often rely on the expertise of mechanics or diagnostic tools, which can be costly, time-consuming, and may not always provide accurate results. To address these limitations, we propose CarFaultNet, a multi-view model that processes both scalograms and spectrograms simultaneously to capture complementary information from these time-frequency representations. Our approach incorporates transfer learning with pretrained convolutional neural networks, including AlexNet, GoogLeNet, ShuffleNet, SqueezeNet, and MobileNet v2, as well as CarFaultNet, which combines two MobileNet networks. The results demonstrate that CarFaultNet outperforms traditional machine learning methods and single-view deep learning models, achieving a precision of 95.32%, recall of 94.83%, F1-score of 94.99%, and accuracy of 95.00%. Class activation mapping visualizations provide valuable insights into the model's decision-making process, highlighting the regions of the input images that are most influential for the classification of different vehicle faults. By leveraging a large, diverse dataset encompassing various vehicle models and real-world operating conditions, our approach addresses the drawbacks of previous studies and demonstrates the potential of deep learning for practical and effective vehicle fault diagnosis.en_US
dc.identifier.doi10.1007/s11760-024-03746-5
dc.identifier.issn1863-1703
dc.identifier.issn1863-1711
dc.identifier.issue1en_US
dc.identifier.scopus2-s2.0-85214084419en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.1007/s11760-024-03746-5
dc.identifier.urihttps://hdl.handle.net/11468/29785
dc.identifier.volume19en_US
dc.identifier.wosWOS:001389241600005
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoenen_US
dc.publisherSpringer London Ltden_US
dc.relation.ispartofSignal Image and Video Processingen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmzKA_WOS_20250222
dc.subjectVehicle fault detectionen_US
dc.subjectPretrained modelsen_US
dc.subjectMobileNeten_US
dc.subjectEngine sounden_US
dc.subjectScalogramen_US
dc.subjectSpectrogram, Class activation mappingen_US
dc.titleEnhancing vehicle fault diagnosis through multi-view sound analysis: integrating scalograms and spectrograms in a deep learning frameworken_US
dc.typeArticleen_US

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