Bearing fault diagnosis using lightweight and robust one-dimensional convolution neural network in the frequency domain

Hakim, Mohammed and Omran, Abdoulhadi A. Borhana and Inayat-Hussain, Jawaid I. and Ahmed, Ali Najah and Abdellatef, Hamdan and Abdellatif, Abdallah and Gheni, Hassan Muwafaq (2022) Bearing fault diagnosis using lightweight and robust one-dimensional convolution neural network in the frequency domain. Sensors, 22 (15). ISSN 1424-8220, DOI

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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 -10 dB achieving 97.37% accuracy. In short, the proposed 1D-CNN model is a promising effective rolling bearing fault diagnosis.

Item Type: Article
Funders: None
Uncontrolled Keywords: Deep learning; One-dimensional convolutional neural network; Signal-to-noise ratio; Fault diagnosis; Fast Fourier transform; Bearing
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Faculty of Engineering > Department of Electrical Engineering
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 27 Nov 2023 02:52
Last Modified: 27 Nov 2023 02:52

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