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Open access
Jul 2026
Noise-Robust Multi-Domain Feature Integration for Bearing Fault Diagnosis
A hybrid deep learning architecture is proposed for robust vibration-based fault diagnosis in industrial machinery by jointly modeling time-domain, frequency-domain, and temporal dynamics, enabling coherent cross-domain interaction and robust fault characterization.
Canan Taştimur
· Information Technology and C... · 0 citations
Open access
Aug 2026
DWAN: a dictionary-wavelet attention network for noise-robust bearing fault diagnosis
Experiments demonstrate that the proposed denoising diagnostic framework, termed the dictionary-wavelet attention network, provides stronger noise robustness than mainstream deep-learning diagnostic models.
Yu-Xing-Chen Chen, Jie Hu, Jie Ren
· Measurement science and tech... · 0 citations
Open access
Aug 2026
Adaptive Noise-Aware Bearing Fault Diagnosis via FFT Windowing and Wavelet-Based SNR-Guided LSTM Model Selection with Real-Time FPGA Implementation
This paper presents an adaptive noise-aware bearing fault diagnosis framework that integrates Fast Fourier Transform (FFT)-windowed feature extraction, wavelet-based Signal-to-Noise Ratio (SNR) estimation, and Long Short-Term Memory (LSTM) classification to maintain high diagnostic accuracy under both clean and severel...
Salim Hamouda, Yassine Amirat, Samir Hamdani et al.
· Applied Sciences · 0 citations
Open access
Aug 2026
Bearing fault diagnosis based on a spectral-guided adaptive multi-scale convolutional network
The results show that SAMACNN outperforms both classical and advanced methods on the two datasets, demonstrating strong robustness and generalization capability in complex variable-condition measurement environments.
DaXin Li, Hong Wang, Hai Xue et al.
· Engineering Research Express · 0 citations
Jul 2026
An improved CNN–Transformer–ELM framework with residual shrinkage networks for bearing fault diagnosis
A robust and noise-resilient bearing fault diagnosis framework that integrates advanced signal processing with hybrid deep learning techniques is presented, demonstrating strong robustness and generalization capability.
Sujit Kumar, Manish Kumar, Bam Bahadur Sinha
· International Journal of Dyn... · 0 citations
Preprint
Aug 2026
A Time-Frequency Dual-Domain Multi-Scale Convolutional Neural Network for Bearing Fault Diagnosis under Strong Noise
Experiments on the CWRU bearing dataset across seven signal-to-noise ratio levels demonstrate that the proposed method achieves 99.75% accuracy under clean conditions and maintains 92.50% at -4 dB SNR, representing a 7.25 percentage-point improvement over the single-domain baseline.
Yanxi Ding, Tingyue Jia
· 0 citations