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

FedLED: Label-Free Equipment Fault Diagnosis with Vertical Federated  Transfer Learning

The elicitation of an ordinal judgment on multiple alternatives is often
required in many psychological and behavioral experiments to investigate
preference/choice orientation of a specific population. The Plackett-Luce model
is one of the most popular and frequently applied parametric distributions to
analyze rankings of a finite set of items. The present work introduces a
Bayesian finite mixture of Plackett-Luce models to account for unobserved
sample heterogeneity of partially ranked data. We describe an efficient way to
incorporate the latent group structure in the data augmentation approach and
the derivation of existing maximum likelihood procedures as special instances
of the proposed Bayesian method. Inference can be conducted with the
combination of the Expectation-Maximization algorithm for maximum \textit{a
posteriori} estimation and the Gibbs sampling iterative procedure. We
additionally investigate several Bayesian criteria for selecting the optimal
mixture configuration and describe diagnostic tools for assessing the fitness
of ranking distributions conditionally and unconditionally on the number of
ranked items. The utility of the novel Bayesian parametric Plackett-Luce
mixture for characterizing sample heterogeneity is illustrated with several
applications to simulated and real preference ranked data. We compare our
method with the frequentist approach and a Bayesian nonparametric mixture model
both assuming the Plackett-Luce model as a mixture component. Our analysis on
real datasets reveals the importance of an accurate diagnostic check for an
appropriate in-depth understanding of the heterogenous nature of the partial
ranking data.

本文提出了一种基于贝叶斯的 Plackett-Luce 混合模型用于分析部分排名数据中未被观测到的样本异质性。