用频谱混叠设计自监督任务，仅用少量标签数据就能精准诊断机械故障。
Spectral Aliasing Pretext: A novel task for Self-Supervised fault diagnosis in rotating machinery
- 通过故意欠采样生成频谱折叠，让Transformer重建原始频谱。
- 在少量标签数据下，线性探测即达高精度，方差极低。
- 适合标签稀缺的工业场景，优于全量微调与监督训练。
深度学习为机械故障诊断提供了新路径，但依赖大量标注数据，而工业场景中该资源稀缺。本文提出频谱混叠预训练（Spectral Aliasing Pretext, SAP），一种基于未标注振动信号的自监督学习方法。通过故意对信号进行欠采样，制造频谱折叠，再训练Transformer模型重建原始未折叠频谱。这一预训练任务迫使模型学习机械故障特有的频域不变特征，避免了可能破坏信号结构的增强操作。在CWRU数据集上的实验表明，SAP能学习到稳定且高度可区分的表示。在线性探测设置下，仅需极少标签数据即可快速实现极高分类性能，且结果波动小。相比之下，全量微调（包括完全监督训练）并未带来更稳定或更优的结果。总体表明，结合线性探测的SAP在标签数据有限时，比全监督训练更具有效性和可靠性。
原文摘要 · Abstract (English)
Deep learning is a new way for machinery fault diagnosis but requires extensive labeled data, a scarce resource in industrial settings. We propose Spectral Aliasing Pretext (SAP), a self-supervised learning method that pretrains models on unlabeled vibration data by exploiting spectral aliasing. We deliberately undersample signals to create folded spectrum, then train a Transformer to reconstruct the original unfolded spectrum. This pretext task forces the model to learn frequency-domain invariants characteristic of mechanical faults, without potentially destructive augmentations. Experiments on the CWRU dataset show that SAP learns stable and highly discriminative representations. In a linear probing setting, SAP quickly achieves very high classification performance with only a small fraction of labeled data and low variance. In contrast, full fine-tuning, including fully supervised training, does not lead to more stable or better results. Overall, these findings suggest that SAP combined with linear probing can be more effective and reliable than fully supervised training for fault diagnosis with limited labeled data.
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