A Time-Frequency Dual-Domain Multi-Scale Convolutional Neural Network for Bearing Fault Diagnosis under Strong Noise
2026-08-10 • Machine Learning
Machine Learning
AI summaryⓘ
The authors developed a new method to better detect bearing faults even when there is a lot of noise. They use two types of information: one looks at the raw signal over time using different sized filters, and the other looks at the signal in the frequency domain after applying a transform. Combining these features helps their small model accurately identify faults, even with very noisy data. Tests show their method works better than others and stays accurate as noise increases. They also confirmed each part of their method helps improve performance.
bearing fault diagnosismulti-scale convolutional neural networktime domainfrequency domainFast Fourier Transformsignal-to-noise ratiofeature fusionCWRU datasetablation study
Authors
Yanxi Ding, Tingyue Jia
Abstract
To address the degradation of bearing fault diagnosis accuracy under strong noise, this paper proposes a time-frequency dual-domain multi-scale convolutional neural network. The time-domain branch employs three parallel convolutional kernels to capture multi-scale impulse features, while the frequency-domain branch applies the Fast Fourier Transform to extract noise-robust spectral structure information. Features from both branches are fused for fault classification, yielding a compact model of 110,122 parameters. 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 with monotonically increasing gains under stronger noise. Ablation experiments validate the independent performance contributions of the time-domain multi-scale branch and the frequency-domain branch. Comparative experiments against WDCNN, DRSN-CW, MCNN, and 1D-LeNet confirm the superiority of the proposed method under strong noise conditions.