Publication:
Bearing Fault Diagnosis Using Lightweight and Robust One-Dimensional Convolution Neural Network in the Frequency Domain

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dc.citedby2
dc.contributor.authorHakim M.en_US
dc.contributor.authorOmran A.A.B.en_US
dc.contributor.authorInayat-Hussain J.I.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorAbdellatef H.en_US
dc.contributor.authorAbdellatif A.en_US
dc.contributor.authorGheni H.M.en_US
dc.contributor.authorid57853404500en_US
dc.contributor.authorid55212152300en_US
dc.contributor.authorid6602271377en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid57358838000en_US
dc.contributor.authorid57304215000en_US
dc.contributor.authorid57210428348en_US
dc.date.accessioned2023-05-29T09:36:50Z
dc.date.available2023-05-29T09:36:50Z
dc.date.issued2022
dc.descriptionConvolution; Convolutional neural networks; Deep learning; Fast Fourier transforms; Fault detection; Frequency domain analysis; Gaussian noise (electronic); Roller bearings; Signal processing; Signal to noise ratio; Time domain analysis; Bearing; Bearing fault diagnosis; Convolution neural network; Convolutional neural network; Deep learning; Environmental noise; Faults diagnosis; Frequency domains; One-dimensional; One-dimensional convolutional neural network; Failure analysis; algorithm; Fourier analysis; signal noise ratio; signal processing; Algorithms; Fourier Analysis; Neural Networks, Computer; Signal Processing, Computer-Assisted; Signal-To-Noise Ratioen_US
dc.description.abstractThe massive environmental noise interference and insufficient effective sample degradation data of the intelligent fault diagnosis performance methods pose an extremely concerning issue. Realising the challenge of developing a facile and straightforward model that resolves these problems, this study proposed the One-Dimensional Convolutional Neural Network (1D-CNN) based on frequency-domain signal processing. The Fast Fourier Transform (FFT) analysis is initially utilised to transform the signals from the time domain to the frequency domain; the data was represented using a phasor notation, which separates magnitude and phase and then fed to the 1D-CNN. Subsequently, the model is trained with White Gaussian Noise (WGN) to improve its robustness and resilience to noise. Based on the findings, the proposed model successfully achieved 100% classification accuracy from clean signals and simultaneously achieved considerable robustness to noise and exceptional domain adaptation ability. The diagnosis accuracy retained up to 97.37%, which was higher than the accuracy of the CNN without training under noisy conditions at only 43.75%. Furthermore, the model achieved an accuracy of up to 98.1% under different working conditions, which was superior to other reported models. In addition, the proposed model outperformed the state-of-art methods as the Signal-to-Noise Ratio (SNR) was lowered to ?10 dB achieving 97.37% accuracy. In short, the proposed 1D-CNN model is a promising effective rolling bearing fault diagnosis. � 2022 by the authors.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo5793
dc.identifier.doi10.3390/s22155793
dc.identifier.issue15
dc.identifier.scopus2-s2.0-85136342570
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85136342570&doi=10.3390%2fs22155793&partnerID=40&md5=0bf503403cf706a19871ec2cf7b72984
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26804
dc.identifier.volume22
dc.publisherMDPIen_US
dc.relation.ispartofAll Open Access, Gold, Green
dc.sourceScopus
dc.sourcetitleSensors
dc.titleBearing Fault Diagnosis Using Lightweight and Robust One-Dimensional Convolution Neural Network in the Frequency Domainen_US
dc.typeArticleen_US
dspace.entity.typePublication
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