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The aim of this chapter is to provide readers with a basic knowledge of satellite monitoring and data mining for anomaly detection using a support vector machine (SVM) technique. The author describes the design and implementation of an SVM and an example of the use of the satellite hardware anomaly detection to discover instability in the attitude rate bias of a gyro sensor. This anomaly is caused by a change in the characteristic parameter of the gyro hardware: a statistical parameter related to noise specifications. The detection is demonstrated using telemetry data that have been sent by an actual science satellite. This chapter is divided into three sections: first, the author describes the target satellite and the basic mathematical modelling and formulation of attitude dynamics of the satellite. In the formulation, kinematics and model parameter estimation technique using Kalman filter method is described to provide readers the key parameter; the drift parameters of attitude rate gyro, which are to be dealt with in the following sections in detail. Estimation of unknown parameter of the formulation is also shown using actual telemetry data. This scheme called observers is most popular method for almost every satellite. Second, a brief introduction of the SVM technique is given and followed by a design and implementation of the SVM technique to the gyro bias instability detection. This analysis and calculation are performed using a set of real telemetry data are given. Finally, a software architecture is proposed that will make it easier to migrate SVM software into an onboard computer as a step toward realizing onboard autonomy. Although the formulation developed in this chapter is concerned with attitude rate sensors of a particular satellite, this approach can be applied to other types of remote systems; a remote system that is designed to be operated by human operators in a distant site by communicating using telemetry systems. This type of onboard autonomous system monitoring seems to be promising not only in all remote systems that are working at server circumstances such as space or deep underwater but also in some consumer products such as cars and trains.

Telemetry Data Mining with SVM for Satellite Monitoring