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

Electronics ◽  
2021 ◽  
Vol 10 (13) ◽  
pp. 1537
Author(s):  
Hossein Varvani Farahani ◽  
Afshin Rahimi
Keyword(s):  
Fault Diagnosis ◽  
Attitude Control ◽  
Missing Values ◽  
Fault Isolation ◽  
Data Driven ◽  
Support Vector ◽  
Satellite Mission ◽  
Control Moment Gyroscope ◽  
Control Moment ◽  
Satellite Control

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 New Approach to Fault Diagnosis for Satellite Control Systems Based on Machine Learning

2012 ◽  
Vol 457-458 ◽  
pp. 1070-1076 ◽  
Author(s):  
Fei Yan ◽  
Ming Jian Li
Keyword(s):  
Machine Learning ◽  
Control System ◽  
Fault Diagnosis ◽  
Control Systems ◽  
Attitude Control ◽  
Support Vector ◽  
Learning Technology ◽  
Attitude Control System ◽  
Satellite Control ◽  
Intelligent Approach

Based on the traditional method of analytical redundancy fault diagnosis, the advanced machine learning technology is combined with the model-based fault diagnosis so as to form a new intelligent approach to the fault diagnosis for satellite control systems. The support vector regression technique in statistical learning theory is employed to model the control system with a little sampling data firstly. Then the feasibility of detecting and identifying faults for the satellite attitude control system with the Mahalanobis distance is analyzed in detail. Finally a set of fault-detection observers are designed and implemented based on the residual evaluation. The simulation result indicates that the diagnosing method proposed in this paper is characterized with light computation burden and good real-time performance.

scholarly journals Fault Detection, Isolation And Prognosis For Control Moment Gyros Onboard Satellites

2021 ◽  
Author(s):  
Venkatesh Muthusamy
Keyword(s):  
Fault Detection ◽  
Health Monitoring ◽  
Detection Algorithm ◽  
Fault Isolation ◽  
Remaining Useful Life ◽  
Path Model ◽  
Data Driven ◽  
Chebyshev Neural Network ◽  
Control Moment ◽  
Computational Budget

Developing a Diagnosis, Prognosis and Health Monitoring (DPHM) framework for a small satellite is a challenging task due to the limited availability of onboard health monitoring sensors and computational budget. This thesis deals with the problem of developing DPHM framework for a satellite attitude actuator system that uses a single gimballed Control Moment Gyro (CMG) in pyramid configuration as an actuator. This includes the development of computationally light data-driven model, fault detection, isolation and prognosis algorithms that works only using the attitude rate measurements from the satellite. A novel scheme is proposed for developing a data-driven model which fuses the symmetric property of the data and the system orientation property of actuators that reduces the need for historical data by 93.75%. The data is trained using Chebyshev Neural Network. A threshold based fault detection algorithm is used to detect the faults of spin motor and gimbal motor used in a CMG. A novel optimization based fault isolation formulation is developed and simulated for given uniformly distributed system parameters. The algorithm 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. For Fault Prognosis, an error based scheme is developed as a measure of degradation. General path model with Bayesian updating is used for predicting the remaining useful life of the spin motor. It performs with 96.25% accuracy when 30% of data is available. Overall, the proposed algorithms can be regarded as a promising DPHM tool for similar non-linear systems.

Fault Detection, Isolation And Prognosis For Control Moment Gyros Onboard Satellites

2021 ◽  
Author(s):  
Venkatesh Muthusamy
Keyword(s):  
Fault Detection ◽  
Health Monitoring ◽  
Detection Algorithm ◽  
Fault Isolation ◽  
Remaining Useful Life ◽  
Path Model ◽  
Data Driven ◽  
Chebyshev Neural Network ◽  
Control Moment ◽  
Computational Budget

Developing a Diagnosis, Prognosis and Health Monitoring (DPHM) framework for a small satellite is a challenging task due to the limited availability of onboard health monitoring sensors and computational budget. This thesis deals with the problem of developing DPHM framework for a satellite attitude actuator system that uses a single gimballed Control Moment Gyro (CMG) in pyramid configuration as an actuator. This includes the development of computationally light data-driven model, fault detection, isolation and prognosis algorithms that works only using the attitude rate measurements from the satellite. A novel scheme is proposed for developing a data-driven model which fuses the symmetric property of the data and the system orientation property of actuators that reduces the need for historical data by 93.75%. The data is trained using Chebyshev Neural Network. A threshold based fault detection algorithm is used to detect the faults of spin motor and gimbal motor used in a CMG. A novel optimization based fault isolation formulation is developed and simulated for given uniformly distributed system parameters. The algorithm 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. For Fault Prognosis, an error based scheme is developed as a measure of degradation. General path model with Bayesian updating is used for predicting the remaining useful life of the spin motor. It performs with 96.25% accuracy when 30% of data is available. Overall, the proposed algorithms can be regarded as a promising DPHM tool for similar non-linear systems.

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