Deep Learning (DL) can diagnose faults and assess machine health from raw condition monitoring data without manually designed statistical features. However, practical manufacturing applications remain extremely difficult for existing DL methods. Machine data is often unlabeled and from very few health conditions (e.g., only normal operating data). Furthermore, models often encounter shifts in domain as process parameters change and new categories of faults emerge. Traditional supervised learning may struggle to learn compact, discriminative representations that generalize to these unseen target domains since it depends on having plentiful classes to partition the feature space with decision boundaries. Transfer Learning (TL) with domain adaptation attempts to adapt these models to unlabeled target domains but assumes similar underlying structure that may not be present if new faults emerge. This study proposes focusing on maximizing the feature generality on the source domain and applying TL via weight transfer to copy the model to the target domain. Specifically, Self-Supervised Learning (SSL) with Barlow Twins may produce more discriminative features for monitoring health condition than supervised learning by focusing on semantic properties of the data. Furthermore, Federated Learning (FL) for distributed training may also improve generalization by efficiently expanding the effective size and diversity of training data by sharing information across multiple client machines. Results show that Barlow Twins outperforms supervised learning in an unlabeled target domain with emerging motor faults when the source training data contains very few distinct categories. Incorporating FL may also provide a slight advantage by diffusing knowledge of health conditions between machines.

翻译：深度学习（DL）能够从原始状态监测数据中诊断故障并评估机器健康状态，无需人工设计统计特征。然而，现有深度学习方法在实际制造应用中仍面临极大困难。机器数据往往无标签，且仅来自极少数健康状态（例如仅包含正常运行数据）。此外，随着工艺参数变化和新型故障类别出现，模型常遭遇领域偏移。传统监督学习依赖充足类别以通过决策边界划分特征空间，因此可能难以学习到能泛化至未知目标域的紧凑判别性表征。基于领域适应的迁移学习（TL）试图使模型适应无标签目标域，但需假设底层结构相似——若新故障出现，该假设可能不成立。本研究提出聚焦于最大化源域特征泛化能力，并通过权重迁移将模型复制到目标域。具体而言，采用Barlow Twins的自监督学习（SSL）通过专注于数据语义属性，能产生比监督学习更适用于健康状态监测的判别性特征。此外，用于分布式训练的联邦学习（FL）可通过在多个客户端机器间共享信息，有效扩展训练数据的有效规模与多样性，从而提升泛化能力。结果表明：当源域训练数据包含极少量不同类别时，在存在新兴电机故障的无标签目标域中，Barlow Twins性能优于监督学习。而融合FL可通过在机器间传播健康状态知识，进一步提供微弱优势。