Actuator and Sensor Fault Diagnosis for Satellite Attitude Control Systems

The reliability of actuators and sensors during satellite orbital operations directly impacts the stability of attitude control systems. Fault diagnosis techniques for these two critical components form the foundational safeguard for long-term spacecraft mission success.

In typical three-axis stabilized satellite control systems, actuators (such as reaction wheels and thrusters) may experience failures including jamming, gain anomalies, or complete malfunction. The diagnostic module typically employs model-reference comparison methods, comparing theoretical outputs with actual feedback through residual signal analysis. This is implemented using adaptive threshold algorithms to locate fault sources in code - often through functions like calculate_residual() and adaptive_threshold_detection(). For intermittent faults, time-frequency analysis techniques are incorporated to enhance detection sensitivity, implemented via wavelet transform algorithms in the diagnostic codebase.

Sensor faults (in star trackers, gyroscopes, etc.) manifest as data jumps, bias drift, or abnormal noise amplification. The diagnostic system employs multi-source information fusion strategies, utilizing cross-validation between sensors with different physical characteristics. For example, gyroscope angular rate integration results are consistency-checked against star tracker absolute attitude data. This is typically coded using Kalman filter innovation sequence analysis to identify abnormal readings, with functions like innovation_sequence_analysis() and sensor_cross_validation() handling the real-time data fusion.

Advanced diagnostic systems further integrate fault tree analysis and machine learning methods, capable of identifying single faults and handling complex scenarios like actuator-sensor coupled failures. Diagnostic results are fed back to the fault-tolerant control module in real-time, triggering control law reconfiguration or hardware redundancy switching. This ensures attitude control precision under fault conditions through coded redundancy management systems and control_reconfiguration() algorithms.

This technology has evolved into a complete closed-loop system covering fault detection, isolation, and system reconfiguration, becoming a core component of modern spacecraft autonomous health management systems. Future development directions include on-orbit machine learning algorithms and digital twin-based predictive maintenance technologies, potentially implemented through reinforcement_learning_modules() and digital_twin_simulation() in next-generation diagnostic software.