FedITA: A cloud–edge collaboration framework for domain generalization-based federated fault diagnosis of machine-level industrial motors

Adequate samples are necessary for establishing a high-performance supervised learning model for intelligent fault diagnosis. Startup companies may only have normal devices and therefore there exists extreme class imbalance of training samples. Lack of faulty devices makes it difficult to independently establish supervised learning. The ideal aggregated training using raw data from multiple client sources may lead to potential conflicts of interest, making it difficult to implement. In addition, individual difference caused by manufacturing inconsistencies and dynamic testing environments is a special interference for machine-level industrial motors, which is more significant in the information flow of multiple client sources. This article proposes a federated iterative learning algorithm (FedITA) as a cloud–edge collaboration framework for domain generalization-based federated fault diagnosis of machine-level industrial motors. The proposed FedITA utilizes progressive training and iterative weight updates to enhance secure interaction between different clients, effectively reducing the risk of overfitting caused by extreme class imbalance. A hybrid perception mechanism is implemented by developing complementary perception modules and integrated into a hybrid perception field network (HPFNet) as a recommended global federated model. The proposed method and model are performed on real production line signals and can achieve mean cross-machine F1-score of 96.50% in limited communication.