An efficient approach based on a novel 1D-LBP for the detection of bearing failures with a hybrid deep learning method

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dc.authoridKaplan, Kaplan/0000-0001-8036-1145
dc.contributor.authorKaya, Yilmaz
dc.contributor.authorKuncan, Melih
dc.contributor.authorAkcan, Eyyup
dc.contributor.authorKaplan, Kaplan
dc.date.accessioned2024-12-24T19:25:21Z
dc.date.available2024-12-24T19:25:21Z
dc.date.issued2024
dc.departmentSiirt Üniversitesi
dc.description.abstractBearings serve as fundamental components in the transmission of motion for rotating machinery. The occurrence of mechanical wear and subsequent bearing failures within these rotating systems can lead to diminished operational efficiency and, if left unaddressed, may result in the complete cessation of the system's function. Hence, there exists a critical need for effective monitoring methodologies aimed at accurately detecting faults in such systems, preferably in their nascent stages. This study presents a novel approach to fault diagnosis leveraging vibration data obtained from bearings. Initially, a feature extraction technique is devised, which incorporates localized signal variations. Subsequently, these features, extracted via MM-1D-LBP, are utilized in conjunction with a hybrid deep learning network based on Long Short-Term Memory (LSTM) and onedimensional Convolutional Neural Network (1D-CNN) architectures for diagnostic purposes. To assess the efficacy of the proposed methodology, experiments were conducted on two distinct datasets acquired from realworld bearing assemblies. In the first dataset, the aim was to predict various failure types (Inner Ring, Outer Ring, Ball). In the second dataset, the objective was to estimate defect sizes using bearing vibration signals corresponding to defects of different dimensions (0.15 cm, 0.5 cm, 0.9 cm) under consistent operating conditions. Remarkably high success rates of 99.31 % and 99.65 % were achieved for the two datasets, respectively, thus underscoring the efficacy of the proposed MM-1D-LBP+1D-CNN-LSTM approach. These findings not only demonstrate the feasibility of the proposed method for fault diagnosis in bearing systems but also suggest its potential applicability across diverse signal categories. Ultimately, this research contributes to advancing the state-of-the-art in fault diagnosis methodologies for rotating machinery, offering enhanced accuracy and early detection capabilities.
dc.identifier.doi10.1016/j.asoc.2024.111438
dc.identifier.issn1568-4946
dc.identifier.issn1872-9681
dc.identifier.scopus2-s2.0-85187703480
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.asoc.2024.111438
dc.identifier.urihttps://hdl.handle.net/20.500.12604/6369
dc.identifier.volume155
dc.identifier.wosWOS:001217175800001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier
dc.relation.ispartofApplied Soft Computing
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241222
dc.subjectBearing Failure
dc.subject1D-LBP
dc.subjectLSTM
dc.subject1D-CNN
dc.subjectFeature extraction
dc.titleAn efficient approach based on a novel 1D-LBP for the detection of bearing failures with a hybrid deep learning method
dc.typeArticle

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