Interval extended PCA-based fault diagnosis of spacecraft thrusters

This paper presents a comparison between the diagnosis results using tow new interval methods to detect and isolate thrusters faults of an autonomous spacecraft involved in the rendez-vous phase of the Mars Sample Return (MSR) mission. As an extension of the classical Principal Component Analysis (PCA) method to interval data, the first diagnosis approach we proposed is based on the Vertices Principal Component Analysis (VPCA) and the second one is based on an analytical approach. To develop a solid comparison between these two methods, a set of interval data provided by the MSR “high-fidelity” industrial simulator and representing the opening rates of the spacecraft thrusters has been considered. The results have proven the efficiency of both interval FDI approaches in the diagnosing process assuring the detection and the isolation of single type faults.

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