In industrial equipment monitoring, fault diagnosis is critical for ensuring system reliability and enabling predictive maintenance. However, the scarcity of fault data, due to the rarity of fault events and the high cost of data annotation, significantly hinders data-driven approaches. Existing time-series generation models, optimized for abundant normal data, struggle to capture fault distributions in few-shot scenarios, producing samples that lack authenticity and diversity due to the large domain gap and high intra-class variability of faults. To address this, we propose a novel few-shot fault time-series generation framework based on diffusion models. Our approach employs a positive-negative difference adapter, leveraging pre-trained normal data distributions to model the discrepancies between normal and fault domains for accurate fault synthesis. Additionally, a diversity loss is introduced to prevent mode collapse, encouraging the generation of diverse fault samples through inter-sample difference regularization. Experimental results demonstrate that our model significantly outperforms traditional methods in authenticity and diversity, achieving state-of-the-art performance on key benchmarks.

翻译：在工业设备监测中，故障诊断对于确保系统可靠性和实现预测性维护至关重要。然而，由于故障事件罕见且数据标注成本高昂，故障数据的稀缺性严重阻碍了数据驱动方法的发展。现有的时间序列生成模型主要针对丰富的正常数据进行优化，在少样本场景下难以捕捉故障分布，由于故障存在较大的领域差异和较高的类内变异性，生成的样本往往缺乏真实性和多样性。为解决这一问题，我们提出了一种基于扩散模型的新型少样本故障时间序列生成框架。该方法采用正负差异适配器，利用预训练的正常数据分布建模正常与故障领域间的差异，以实现精确的故障合成。此外，我们引入了多样性损失函数以防止模式坍塌，通过样本间差异正则化促进多样化故障样本的生成。实验结果表明，我们的模型在真实性和多样性方面显著优于传统方法，在关键基准测试中取得了最先进的性能。