基 于 联 邦 图 网 络 的 转 子 系 统 故 障 诊 断 方 法

Considerable harsh operating environments of rotor systems，combined with the difficulty in fusing monitoring data from multiple sources and a tendency to form data islands，present substantial challenges to the health monitoring of rotor systems. This paper proposes a rotor system fault diagnosis method utilizing Federated Graph Convolutional neural Networks（FGCN）based on a genetic evolutionary composition. First，a federated migration learning framework，employing federated learning and graph neural networks，is established. The global model is derived by training local models on individual clients and aggregating them via a federated weighted average algorithm. This method facilitates data localization while securing the privacy and integrity of model parameters. Furthermore，to address the challenge of inadequate adaptive integration of multi-source sensor data，a genetic evolution composition method is introduced. This method dynamically adjusts the connectivity and weights among graph nodes during training，emulating the mechanisms of natural selection and genetic variation found in biological evolution. This approach significantly enhances the adaptability and flexibility of the multi-source sensor composition，thereby improving the accuracy of fault diagnosis. In conclusion，experimental validation on the rotor failure testbed dataset demonstrates that the proposed method effectively utilizes limited target domain data，achieving over 95% accuracy in fault diagnosis scenarios where the clients contain different number of faults classes.