CEC-FedISDG: A Cloud-Edge Collaboration Federated Invariance and Specificity Domain Generalization Method for Machine Remaining Useful Life Prediction

Advances in sensor technology and the Industrial Internet of Things (IIoT) have enabled the collection of large-scale monitoring data, facilitating intelligent remaining useful life (RUL) prediction for industrial equipment. However, accurate RUL prediction in distributed environments faces two major challenges. First, the scarcity of data and limited computational resources at edge clients hinder the development of robust RUL models, while privacy constraints prohibit centralized data sharing. Second, distribution shifts across client machines severely limit the model’s ability to generalize to unknown operating conditions (OCs) and equipment. To address these challenges, this article proposes a cloud-edge collaboration (CEC) federated invariance and specificity domain generalization (DG) (CEC-FedISDG) method. CEC-FedISDG integrates both domain-invariant and domain-specific predictive knowledge within a unified cloud-edge federated learning (FL) framework. This design enables the model to exploit the broad generalizability of invariant features while retaining domain-specific predictive capabilities. Specifically, a progressive invariance refinement (PIR) module is developed to gradually strengthen domain-invariant features while preserving privacy through a two-stage learning process. Subsequently, a dynamic specificity selection (DSS) module is designed. It dynamically integrates the outputs of private-domain regressors that retain domain specificity utilizing a domain classifier, adapting weights to test samples, thereby improving RUL prediction accuracy. Experimental evaluations on two bearing datasets and a real-world industrial wind turbine gearbox (WTG) dataset demonstrate that the CEC-FedISDG achieves superior generalization performance while adhering to strict privacy preservation requirements.