Due to the complex transmission structure, fault diagnosis of helicopter transmission systems poses a significant challenge. Data privacy has aroused widespread attention for the interest conflict, especially for some special occasions such as helicopters. Furthermore, the existing graph construction methods overlook the chain structure of helicopter transmission systems, leading to limited model generalization ability, and unknown weight distribution between the source and target models during federated generalization. To address these issues, we propose multisensor graph adaptive federated generalization (MGAFG) for helicopter transmission system fault diagnosis. First, the graph is constructed by multisensor data. Next, the domain shift of different clients is measured by auxiliary domain-based adversarial learning. Specifically, self-supervised learning is introduced to learn the latent distribution features of target client data. Finally, the different fault types are classified by the generalized model. Two different datasets are used to verify the effectiveness of MGAFG. The experiment results show that MGAFG has the highest diagnosis accuracy than other methods.