Rolling bearing fault diagnosis is critical for ensuring the safe operation of rotating machinery, yet it faces significant challenges in noisy environments. This paper proposes BEAM-Net (Bearing-spectrum Enhanced by EMA and Weighted Spectral Convolution Network), a lightweight neural network designed specifically for rolling bearing fault diagnosis under strong noise conditions. Classifying bearing faults from vibration signals remains a challenging task when fault-related features are subtle and easily submerged in background noise—especially when the signal-to-noise ratio (SNR) is low. To address this challenge, BEAM-Net adopts a “decompose–enhance–extract” pipeline: first, an Exponential-Moving-Average Trend Decomposer (ETD) splits the frequency spectrum into a smooth trend component and a fault-sensitive residual component; second, a Spectral Residual Gate (SRG) reinjects detailed residual information through a learnable gating mechanism; finally, a Weighted Spectrum Convolution block (WSC) incorporates a symmetric center-emphasizing prior into the convolution kernel, ensuring that local spectral patterns receive greater attention. Experimental results on the Case Western Reserve University (CWRU) bearing dataset at SNR = −6 dB show that BEAM-Net achieves an F1 score of 99.15% with only 2,835 parameters. Compared to the single-convolution baseline, this represents a +0.78% improvement in F1 score and a 50% reduction in the false positive rate (from 0.18% to 0.09%). Cross-dataset validation on the Paderborn University (PU) and Machinery Failure Prevention Technology (MFPT) datasets further confirms the generalizability of the proposed approach, achieving F1 scores of 97.83% and 98.46%, respectively, under comparable noise conditions. These findings demonstrate that combining explicit spectral trend modeling with weighted convolution is not only effective but also parameter-efficient, making it well-suited for noise-robust rolling bearing fault diagnosis. It should be noted that the current method is primarily validated on spectral-analysis-based diagnostics of rolling bearings; its applicability to other vibroacoustic diagnostic modalities (e.g., tapping or nonlinear vibration excitation) and to quantitative defect severity grading remains to be investigated in future work.
Duan et al. (2026) studied this question.