Machines 2026, 14(8), 952; https://doi.org/10.3390/machines14080952 - 20 Aug 2026
Abstract
In practical industrial applications, rolling bearing fault samples are often scarce and costly to annotate, making small-sample fault diagnosis challenging. To address this issue, this paper proposes a frequency-aware time–frequency representation learning framework, named Multi-branch Frequency Enhancement Temporal Attention Network (MFETA-Net). In the
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In practical industrial applications, rolling bearing fault samples are often scarce and costly to annotate, making small-sample fault diagnosis challenging. To address this issue, this paper proposes a frequency-aware time–frequency representation learning framework, named Multi-branch Frequency Enhancement Temporal Attention Network (MFETA-Net). In the proposed framework, dual-channel vibration signals are first transformed into time–frequency representations using the Short-Time Fourier Transform (STFT). Then, a multi-branch frequency enhancement encoder is used to extract local frequency-band patterns, cross-band correlations, and frequency variation features. A temporal-frequency dependency modeling mechanism preserves the correspondence between temporal positions and frequency distributions during sequential modeling, while a temporal attention aggregation module emphasizes diagnostically important regions. Extensive experiments on the CWRU and HUST bearing datasets show that MFETA-Net achieves accuracies of 77.26% and 79.60% under the smallest training setting, respectively, indicating its capability to learn discriminative fault representations from limited labeled samples. Ablation studies further verify the effectiveness of each proposed module, while noise experiments confirm the robustness of the proposed framework under controlled noisy conditions.
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(This article belongs to the Section Machines Testing and Maintenance)
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