Study on combined stress failure envelope of CMG based on PSO-BP neural network

The investigation of the failure envelope of control moment gyroscopes (CMGs) under simulated dynamic and thermal vacuum conditions on the ground is crucial for ensuring the reliability of CMGs. In this study, a test rig is employed to simulate the combined stresses in a vacuum environment, including the temperature, CMG gimbal rotating speed, and satellite rotating speed. The objective is to obtain high-fidelity running status data of the CMG. The particle swarm optimization and BP neural network (PSO-BP) model is utilized to learn from these test data and subsequently predict the running status for other stress combinations, ultimately enabling the determination of the failure envelope surface of the CMG. The results demonstrate that this method significantly reduces the cost of testing to detect the CMG failure envelope while achieving high prediction accuracy and adaptability under combined stress situations. By employing the k-fold cross-validation method, the PSO-BP model demonstrates superior generalization performance to the BP model in predicting the running status of CMGs. The optimized hidden layer size and learning rate of the PSO-BP model are also discussed. Furthermore, the method presented in this paper can effectively incorporate the hidden experience data from the test. Finally, the PSO-BP method, in comparison to the BP method, yields a smooth and stable initial predicted value, effectively preventing the prediction result from falling into local optimization.