Remaining useful life prediction based on federated learning and cloud-edge collaboration

Predicting the remaining useful life (RUL) of industrial equipment is critical for maintaining safe operations and minimizing maintenance costs. However, RUL prediction for edge devices faces several challenges. First, edge devices often lack the computational power and storage capacity required for complex RUL prediction algorithms, making such predictions difficult. Many RUL prediction algorithms require substantial resources, which are scarce on edge devices. Second, the limited data transmission rate between the cloud and edge devices causes high latency when transmitting large data sets to the cloud, affecting real-time predictions and increasing network bandwidth usage. Additionally, data sharing among all edge devices is often impractical owing to privacy, security issues, and potential conflicts of interest, limiting models to local data and reducing their accuracy.

To address these challenges, this paper proposes a cloud-edge collaboration framework for RUL prediction based on federated learning. The framework comprises two main processes. In the first process, each training device trains a variational autoencoder (VAE) using its local data set. The trained encoders are then uploaded to the cloud and aggregated using a weighted average method (FedAVG), with the number of training samples as weights. The aggregated global encoder is then downloaded to all edge devices. In the second process, the aggregated encoder extracts hidden features from the local data sets on each edge device. These features are uploaded sequentially to the cloud to train the RUL predictor. Once trained, the predictor is sent back to the edge devices, completing one training cycle. This iterative process continues until a well-trained RUL prediction model, consisting of the global encoder and predictor, is achieved. During the testing stage, the global encoder is used to extract hidden features, while the RUL predictor performs deeper feature extraction and RUL prediction. In this framework, only local encoders and hidden features are uploaded to the server, significantly reducing communication overhead. Most of the training occurs on the server, with clients only performing the basic training of the shallow VAE, thereby effectively utilizing the server's powerful computational capabilities. Data privacy is maintained since the server receives hidden features and encoders, not the original data, preventing data reconstruction.

To validate the proposed method's efficiency and practicality, different network structures were tested for RUL prediction on the commercial modular aero-propulsion system simulation (C-MAPSS). Although there was a slight decline in prediction performance compared to the baseline, the difference was within acceptable limits. This minor trade-off in accuracy enabled RUL prediction under resource constraints. The proposed algorithm significantly reduced data transmission time after feature extraction across various data scales consistently. In industrial scenarios with large data volumes, this reduction was even more pronounced. Further validation using nuclear power unit fault data sets showed a slight decrease in root mean square error (RMSE) on the test set without a significant drop in prediction accuracy. These results demonstrate that the proposed cloud-edge collaboration framework is promising for fault diagnosis in nuclear power units, effectively addressing edge resource limitations.

The proposed cloud-edge collaboration framework leverages federated learning to achieve RUL prediction on resource-constrained edge devices, thereby alleviating issues related to resource constraints and data privacy. By employing VAE-based feature extraction and federated learning for model training, the framework achieves efficient model training while significantly reducing communication overhead with minimal impact on accuracy. Experimental validation on industrial simulation data sets and nuclear power unit fault data sets demonstrates the framework's practicality and effectiveness. This framework represents a useful approach to addressing challenges in fault diagnosis and URL prediction within resource-constrained settings.