Recently, federated fault diagnosis has garnered growing attention due to its promising capabilities in information fusion with privacy preservation. However, most of the existing approaches are based on the assumptions of no domain shift between multiple factories and no unseen domains for online applications. In actual industry, these assumptions are generally unsatisfied due to prominent environmental noises, mechanical wear, and changes in working conditions. Federated models that ignore domain shifts would face the negative aggregation problem and are not robust to unseen domains. To solve the domain shift problem, a federated domain generalization method is proposed for privacy-preserving fault diagnosis in this article. The key idea is to construct a sharable reference domain in cloud, which can convert the privacy-risky centralized alignment problem into a privacy-preserving pairwise alignment problem. Based on the recognition that any fault category in a discriminative feature space can be characterized by a particular position and volatility, we design a shareable domain generator to provide a reference for pairwise alignment. Then, the non-deterministic sampling and non-parametric alignment criterion are introduced to realize local domain alignment, which facilitates the domain-invariant feature extraction. Finally, by the alternation of local domain alignment and global reference synchronization, the alignment of multi-source domains is achieved implicitly. We give convergence guarantees for the proposed method and derive the generalization error bound of federated DG, which illustrates the positive effect of the proposed method on improving generalization. Experiments on two cases demonstrate the consistent superior generalization performance of our method without the risk of data leakage.

中文翻译：

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融合共识知识：域转移下基于隐私保护参考的联邦学习故障诊断方法

近年来，联合故障诊断因其在信息融合和隐私保护方面的良好能力而受到越来越多的关注。然而，大多数现有方法都是基于多个工厂之间没有域转移以及在线应用程序没有看不见的域的假设。在实际工业中，由于突出的环境噪声、机械磨损和工作条件的变化，这些假设通常不能满足。忽略域转移的联合模型将面临负面聚合问题，并且对于看不见的域不具有鲁棒性。为了解决域转移问题，本文提出了一种用于隐私保护故障诊断的联邦域泛化方法。其关键思想是在云中构建一个可共享的参考域，可以将存在隐私风险的集中对齐问题转化为保护隐私的成对对齐问题。基于判别性特征空间中的任何故障类别都可以通过特定位置和波动性来表征的认识，我们设计了一个可共享域生成器，为成对对齐提供参考。然后，引入非确定性采样和非参数对齐准则来实现局部域对齐，从而有利于域不变特征提取。最后，通过局部域对齐和全局参考同步的交替，隐式地实现了多源域的对齐。我们为所提出的方法提供了收敛保证，并推导了联邦DG的泛化误差界，这说明了所提出的方法对提高泛化能力的积极作用。两种情况的实验证明了我们的方法具有一致的优越泛化性能，并且没有数据泄漏的风险。