An Adaptive Federated Domain Generalization Framework for Consumer Electronics Manufacturing Equipment Cross-Factory Fault Detection

Reliability and operational efficiency of equipment are crucial in the manufacturing of consumer electronics. Existing fault detection methods often face limitations such as dataset dependence, poor scenario generalization, and data privacy issues when addressing the complex and diverse operating conditions in product manufacturing. To address these issues, this paper proposes a cross-factory fault detection framework for consumer electronics production equipment based on adaptive federated domain generalization. This framework reconsiders the limitations of Sharpness-Aware Minimization(SAM) and, by jointly considering local personalization and global generalization objectives, designs an adaptive weighting scheme to balance the trade-off between loss minimization and sharpness during optimization, thereby improving the model’s robustness and accuracy under various working conditions. Then, A parameter momentum aggregation scheme is proposed on the server side to incorporate historical gradient information, reducing client drift impact and improving model convergence and stability. Finally, extensive scenario experiments were conducted on two public datasets. The results indicate that the proposed framework achieves an average improvement of 22.5% in fault detection accuracy over the baseline model across varying operating conditions and data distribution scenarios, demonstrating its effectiveness in addressing the challenges of complex condition variations and data privacy in consumer electronics manufacturing.