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Bearing Fault Diagnosis Using Lightweight and Robust One-Dimensional Convolution Neural Network in the Frequency Domain
| Content Provider | MDPI |
|---|---|
| Author | Hakim, Mohammed Omran, Abdoulhadi A. Borhana Inayat-Hussain, Jawaid I. Ahmed, Ali Najah Abdellatef, Hamdan Abdellatif, Abdallah Gheni, Hassan Muwafaq |
| Copyright Year | 2022 |
| Description | 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. |
| Starting Page | 5793 |
| e-ISSN | 14248220 |
| DOI | 10.3390/s22155793 |
| Journal | Sensors |
| Issue Number | 15 |
| Volume Number | 22 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2022-08-03 |
| Access Restriction | Open |
| Subject Keyword | Sensors Industrial Engineering Deep Learning One-dimensional Convolutional Neural Network Signal-to-noise Ratio Fault Diagnosis Fast Fourier Transform Bearing |
| Content Type | Text |
| Resource Type | Article |
