ArticleSensors (Basel, Switzerland)2022
Bearing Fault Diagnosis Using Lightweight and Robust One-Dimensional Convolution Neural Network in the Frequency Domain.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed.
- Cross-Condition Fault Diagnosis of Crane Slewing Bearings Based on a Lightweight Domain-Adaptive Graph Convolutional Network.Sensors (Basel, Switzerland) · 2026Article
- Towards a Resilience-Oriented Framework for Fault Diagnosis Under Varying Operating Conditions.Sensors (Basel, Switzerland) · 2026Article
- Noise Robustness Evaluation of Time-Frequency Networks (TFNs) for Intelligent Mechanical Fault Diagnosis.Sensors (Basel, Switzerland) · 2026Article
- IoT-Driven Robust Bearing Fault Diagnosis for Induction Motors Under Operating-Condition Shift.Sensors (Basel, Switzerland) · 2026Article
- A temporal-spectral dual-stream anti-noise bearing fault diagnosis model based on adaptive mode decomposition.Scientific reports · 2026Article
- Comparative analysis of deep learning algorithms for rolling element bearing fault classification under variable loads and speeds.Scientific reports · 2026Article
- Disentangle-and-aggregate feature learning (DAFNet) for motor bearing fault diagnosis.Scientific reports · 2026Article
- AI-Driven Resilient Fault Diagnosis of Bearings in Rotating Machinery.Sensors (Basel, Switzerland) · 2025Article
- Article
- A Deployment Method for Motor Fault Diagnosis Application Based on Edge Intelligence.Sensors (Basel, Switzerland) · 2024Article
- Lightweight Ghost Enhanced Feature Attention Network: An Efficient Intelligent Fault Diagnosis Method under Various Working Conditions.Sensors (Basel, Switzerland) · 2024Article
- An Intelligent Ball Bearing Fault Diagnosis System Using Enhanced Rotational Characteristics on Spectrogram.Sensors (Basel, Switzerland) · 2024Article
- A Sound and Vibration Fusion Method for Fault Diagnosis of Rolling Bearings under Speed-Varying Conditions.Sensors (Basel, Switzerland) · 2023Article
- Fault Diagnosis Method for Imbalanced Data Based on Multi-Signal Fusion and Improved Deep Convolution Generative Adversarial Network.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
7 authors.
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Abstract
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.
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