In spacecraft attitude control, maintaining an accurate estimate of attitude readings is crucial. Due to aging factors of sensors like gyroscopes, emerged drift or bias from the correct rate values lead to decreased pointing accuracy. This paper proposes unsupervised data-driven approach in order to diagnose in early stage abnormal drifts in spacecraft attitude sensors. Three types of faults are injected in the satellite attitude control simulator. The obtained results are compared with other similar data-driven methods. The comparison shows the superiority of our method in terms of missed alarm rate and incorrect detection rate. In addition, our approach does not require prior knowledge and labels about the attitude sensors faults.
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fault detection and identification, unsupervised learning, supervised learning, gyroscope drift
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