Federated prognostics enable clients (e.g., companies, factories, and production lines) to collaboratively develop a failure time prediction model while keeping each client's data local and confidential. However, traditional federated models often assume homogeneity in the degradation processes across clients, an assumption that may not hold in many industrial settings. To overcome this, this paper proposes a personalized federated prognostic model designed to accommodate clients with heterogeneous degradation processes, allowing them to build tailored prognostic models. The prognostic model iteratively facilitates the underlying pairwise collaborations between clients with similar degradation patterns, which enhances the performance of personalized federated learning. To estimate parameters jointly using decentralized datasets, we develop a federated parameter estimation algorithm based on proximal gradient descent. The proposed approach addresses the limitations of existing federated prognostic models by simultaneously achieving model personalization, preserving data privacy, and providing comprehensive failure time distributions. The superiority of the proposed model is validated through extensive simulation studies and a case study using the turbofan engine degradation dataset from the NASA repository.

翻译：联邦预测使各客户端（如公司、工厂及生产线）能够协作开发故障时间预测模型，同时保持各客户端数据的本地性与机密性。然而，传统联邦模型通常假设各客户端的退化过程具有同质性，这一假设在许多工业场景中并不成立。为克服此问题，本文提出一种专为具有异质性退化过程的客户端设计的个性化联邦预测模型，支持其构建定制化预测模型。该预测模型通过迭代促进退化模式相似的客户端进行底层配对协作，从而增强个性化联邦学习的性能。为利用分布式数据集联合估计参数，我们开发了一种基于近端梯度下降的联邦参数估计算法。所提方法通过同时实现模型个性化、保障数据隐私并提供完整的故障时间分布，解决了现有联邦预测模型的局限性。通过大量仿真研究以及基于NASA数据库涡轮风扇发动机退化数据集的案例研究，验证了该模型的优越性。