{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,7]],"date-time":"2026-06-07T12:17:02Z","timestamp":1780834622780,"version":"3.54.1"},"reference-count":57,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2022,8,3]],"date-time":"2022-08-03T00:00:00Z","timestamp":1659484800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The 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 \u221210 dB achieving 97.37% accuracy. In short, the proposed 1D-CNN model is a promising effective rolling bearing fault diagnosis.<\/jats:p>","DOI":"10.3390\/s22155793","type":"journal-article","created":{"date-parts":[[2022,8,3]],"date-time":"2022-08-03T23:33:01Z","timestamp":1659569581000},"page":"5793","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":42,"title":["Bearing Fault Diagnosis Using Lightweight and Robust One-Dimensional Convolution Neural Network in the Frequency Domain"],"prefix":"10.3390","volume":"22","author":[{"given":"Mohammed","family":"Hakim","sequence":"first","affiliation":[{"name":"Department of Mechanical Engineering, College of Engineering, Universiti Tenaga Nasional, Jalan IKRAM-UNITEN, Kajang 43000, Selangor, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6689-2265","authenticated-orcid":false,"given":"Abdoulhadi A. Borhana","family":"Omran","sequence":"additional","affiliation":[{"name":"Department of Mechanical and Mechatronic Engineering, Faculty of Engineering, Sohar University, Sohar P.C 311, Oman"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jawaid I.","family":"Inayat-Hussain","sequence":"additional","affiliation":[{"name":"College of Graduate Studies, Universiti Tenaga Nasional, Jalan IKRAM-UNITEN, Kajang 43000, Selangor, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5618-6663","authenticated-orcid":false,"given":"Ali Najah","family":"Ahmed","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, College of Engineering, Universiti Tenaga Nasional, Jalan IKRAM-UNITEN, Kajang 43000, Selangor, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2303-8330","authenticated-orcid":false,"given":"Hamdan","family":"Abdellatef","sequence":"additional","affiliation":[{"name":"School of Engineering-Electrical & Computer Engineering Department, Lebanese American University, Byblos 1102, Lebanon"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdallah","family":"Abdellatif","sequence":"additional","affiliation":[{"name":"Expert System and Optimization Laboratory, Department of Electrical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Selangor, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0971-0829","authenticated-orcid":false,"given":"Hassan Muwafaq","family":"Gheni","sequence":"additional","affiliation":[{"name":"Computer Techniques Engineering Department, Al-Mustaqbal University College, Hillah 51001, Iraq"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"494","DOI":"10.1109\/TIE.2014.2327555","article-title":"Vibration spectrum imaging: A novel bearing fault classification approach","volume":"62","author":"Amar","year":"2015","journal-title":"IEEE Trans. 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