Most prognostic methods require a decent amount of data for model training. In reality, however, the amount of historical data owned by a single organization might be small or not large enough to train a reliable prognostic model. To address this challenge, this article proposes a federated prognostic model that allows multiple users to jointly construct a failure time prediction model using their multi-stream, high-dimensional, and incomplete data while keeping each user's data local and confidential. The prognostic model first employs multivariate functional principal component analysis to fuse the multi-stream degradation signals. Then, the fused features coupled with the times-to-failure are utilized to build a (log)-location-scale regression model for failure prediction. To estimate parameters using distributed datasets and keep the data privacy of all participants, we propose a new federated algorithm for feature extraction. Numerical studies indicate that the performance of the proposed model is the same as that of classic non-federated prognostic models and is better than that of the models constructed by each user itself.

翻译：大多数预后方法需要大量数据用于模型训练。然而在实际中，单一机构拥有的历史数据量可能较小或不足以训练可靠的预后模型。为解决这一挑战，本文提出一种联邦预后模型，允许多个用户利用其多流、高维、不完备数据共同构建失效时间预测模型，同时保持每个用户数据的本地性和机密性。该预后模型首先采用多变量函数主成分分析融合多流退化信号，随后将融合特征与失效时间相结合，构建（对数）位置-尺度回归模型进行失效预测。为利用分布式数据集估计参数并保护所有参与者的数据隐私，我们提出了一种新的联邦特征提取算法。数值研究表明，所提模型的性能与经典非联邦预后模型相当，且优于每个用户自行构建的模型。