Intelligent equipment fault diagnosis based on Federated Transfer Learning (FTL) attracts considerable attention from both academia and industry. It allows real-world industrial agents with limited samples to construct a fault diagnosis model without jeopardizing their raw data privacy. Existing approaches, however, can neither address the intense sample heterogeneity caused by different working conditions of practical agents, nor the extreme fault label scarcity, even zero, of newly deployed equipment. To address these issues, we present FedLED, the first unsupervised vertical FTL equipment fault diagnosis method, where knowledge of the unlabeled target domain is further exploited for effective unsupervised model transfer. Results of extensive experiments using data of real equipment monitoring demonstrate that FedLED obviously outperforms SOTA approaches in terms of both diagnosis accuracy (up to 4.13 times) and generality. We expect our work to inspire further study on label-free equipment fault diagnosis systematically enhanced by target domain knowledge.

基于联邦迁移学习的智能设备故障诊断引起了学术界和工业界的广泛关注，该方法允许有限样本的实际工业智能体在不危害原始数据隐私的情况下构建故障诊断模型。然而，现有方法既不能处理由实际智能体的不同工况引起的强烈样本异质性，也不能处理新部署设备的故障标签稀缺，甚至为零的问题。为了解决这些问题，我们提出了FedLED，这是一种第一个无监督的垂直联邦迁移学习设备故障诊断方法，其中进一步利用了未标记目标领域的知识进行有效的无监督模型迁移。实验结果表明，使用真实设备监测数据进行的大量实验显示，FedLED在诊断准确性（高达4.13倍）和泛化性方面明显优于目前最先进的方法。我们期望我们的工作能够激发进一步研究，通过目标领域知识系统地提升无标签设备故障诊断。

FedLED：使用垂直联邦迁移学习进行无标签设备故障诊断