LAUSR

Abstract The paper presents a novel data-driven method for fault detection and isolation of control moment gyroscopes onboard satellites. The proposed method uses the Chebyshev Neural Network and genetic algorithm in conjunction with satellite attitude rate data only. A data-driven model is first developed that fuses the symmetric property of the data and the system orientation property of actuators that reduces the need for historical data by 93.75%. Next, the data is trained using Chebyshev Neural Network. An adaptive threshold-based fault detection algorithm is applied to detect the faults in the spin and gimbal motors of the control moment gyroscopes. A fault isolation scheme is developed wherein an objective function is optimized using a genetic algorithm for different cases of system parameters. The proposed scheme has a success rate of 93.5% in isolating faults of 8 motors (4 gimbal and 4 spin) that can fail in 254 different ways. Overall, the proposed methodology can be regarded as a promising fault diagnostic tool for satellites using limited historical data and measurements.