A Gradient Alignment Federated Domain Generalization Framework for Rotating Machinery Fault Diagnosis

Empowered by the huge amounts of sensor data in Industrial Internet of Things (IIOT), deep learning models have made remarkable achievements in the field of rotating machinery fault diagnosis. To improve the diagnosis performance under unknown working conditions, domain generalization technologies have been extensively studied. However, the existing methods predominantly gather the sensor data from multiple source domains together for model training, which poses a threat to data privacy in the IIOT. To address this problem, this paper proposes a novel gradient alignment federated domain generalization (GAFedDG) framework for rotating machinery fault diagnosis. In the proposed GAFedDG, an intra-domain gradient aligning mechanism is designed to minimize the gradient discrepancy between the current classifier on raw signals and augmented signals, effectively preventing the local model from overfitting the domain-specific fault knowledge. In addition, to bridge the domain shifts across multiple scattered source domains, an inter-domain gradient aligning mechanism is implemented to minimize the gradient discrepancy between the current classifier and other domain classifiers. By combining the two mechanisms above, a domain-agnostic model that can generalize well on unseen working conditions is established. Extensive experimental results on two self-built test rigs show that the GAFedDG possesses superior generalization capability in privacy-preserving scenarios.