The time series self-supervised contrastive learning framework has succeeded significantly in industrial fault detection scenarios. It typically consists of pretraining on abundant unlabeled data and fine-tuning on limited annotated data. However, the two-phase framework faces three challenges: Sampling bias, task-agnostic representation issue, and angular-centricity issue. These challenges hinder further development in industrial applications. This article introduces a debiased contrastive learning with supervision guidance (DCLSG) framework and applies it to industrial fault detection tasks. First, DCLSG employs channel augmentation to integrate temporal and frequency domain information. Pseudolabels based on momentum clustering operation are assigned to extracted representations, thereby mitigating the sampling bias raised by the selection of positive pairs. Second, the generated supervisory signal guides the pretraining phase, tackling the task-agnostic representation issue. Third, the angular-centricity issue is addressed using the proposed Gaussian distance metric measuring the radial distribution of representations. The experiments conducted on three industrial datasets (ISDB, CWRU, and practical datasets) validate the superior performance of the DCLSG compared to other fault detection methods.

中文翻译：

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用于工业故障检测的带监督指导的去偏对比学习


时间序列自监督对比学习框架在工业故障检测场景中取得了巨大成功。它通常包括对大量未标记数据的预训练和对有限注释数据的微调。然而，两阶段框架面临三个挑战：采样偏差、任务无关的表示问题和角度中心问题。这些挑战阻碍了工业应用的进一步发展。本文介绍了一种带监督指导的去偏对比学习（DCLSG）框架，并将其应用于工业故障检测任务。首先，DCLSG 采用通道增强来集成时域和频域信息。基于动量聚类操作的伪标签被分配给提取的表示，从而减轻由于选择正对而引起的采样偏差。其次，生成的监督信号指导预训练阶段，解决与任务无关的表示问题。第三，使用所提出的测量表示的径向分布的高斯距离度量来解决角中心问题。在三个工业数据集（ISDB、CWRU 和实际数据集）上进行的实验验证了 DCLSG 与其他故障检测方法相比的优越性能。