Data-Driven Fault Diagnosis for Satellite Control Moment Gyro Assembly with Multiple In-Phase Faults

Abstract

A satellite can only complete its mission successfully when all its subsystems, including the attitude control subsystem, are in healthy condition and work properly. Control moment gyroscope is a type of actuator used in the attitude control subsystems of satellites. Any fault in the control moment gyroscope can cause the satellite mission failure if it is not detected, isolated, and resolved in time. Fault diagnosis provides an opportunity to detect and isolate the occurring faults and, if accompanied by proactive remedial actions, it can avoid failure and improve the satellite reliability. In this paper, an enhanced data-driven fault diagnosis is introduced for fault isolation of multiple in-phase faults of satellite control moment gyroscopes that has not been addressed in the literature before with high accuracy. The proposed method is based on an optimized support vector machine, and the results yield fault predictions with up to 95.6% accuracy. In addition, a sensitivity analysis with regard to noise, missing values, and missing sensors is done. The results show that the proposed model is robust enough to be used in real applications.

A Method for Satellite Component Health Assessment Based on Multiparametric Data Distribution Characteristics

“…The results are shown in Figure 7. With a combined shell temperature weight of 0.4821 and a combined bearing temperature weight of 0.5179, the evaluation model is shown in Equation (23):…”

“…Suo et al [22] proposed a data-driven fault diagnosis strategy combining the fast iterative method and support vector machine, and verified its effectiveness using satellite power system data. Varvani Farahani et al [23] proposed an enhanced data-driven fault diagnosis method based on the support vector machine approach, which can achieve high-precision detection of the faults in satellite gyros. Chen et al [24] considered the performance degradation of actuators and used the transfer learning method for the fault detection of complex systems.…”

A New Dual-Mass MEMS Gyroscope Fault Diagnosis Platform

“…For the trained model, to verify the effectiveness of the training, twenty-eight groups of signals were used for verification in this paper, and the seven types of signals, namely, normal, bias, blocking, drift, multiplicative, periodic, and internal fault, are labeled as the corresponding numbers [ 1 , 2 , 3 , 4 , 5 , 6 , 7 ]. Finally, the prediction diagram of the fault diagnosis platform obtained is shown in Figure 10 , where the blue dots indicate the actual signal type, and the black lines indicate the recognized signal type.…”

“…With the development of modern technology, measurement and control systems are becoming more and more complex, resulting in difficulties regarding troubleshooting and increased possibility of failure. To improve the accuracy and reliability of the gyroscope, promptly providing navigation and positioning parameters for the carrier, detecting, identifying, and predicting navigation system faults, and guaranteeing the positioning accuracy and reliability of the navigation system are important tasks [ 2 ].…”

Advances in Machine Condition Monitoring and Fault Diagnosis

“…The results show promising performance for the proposed scheme with accuracy in predicting the health index parameter around 0.01-0.02 normalized root mean squared error, the accuracy in prediction of RUL of 1-2.5%, and robustness to various uncertainty factors. In Reference [8], a data-driven fault diagnosis method was developed for detecting faults occurring in the satellite control moment gyroscope assembly. The proposed method is based on an optimized SVM, and the results yield fault predictions with up to 95.6% accuracy.…”