Fault detection and diagnosis for spacecraft using principal component analysis and support vector machines

Development of intelligent fault detection and diagnosis technologies for spacecraft is one of important issues in the space engineering. In this paper, we present a new fault detection and diagnosis approach for spacecraft based on Principal Component Analysis (PCA) and Support Vector Machines (SVM). Firstly, PCA is used to extract features from input data and reduce the input data to low dimensional feature vectors. Then the method use a binary SVM to detect whether there is a fault or not. If the fault is detected, a multi-class SVM is used to identify fault type. The experimental results show that the method is efficient and practical for fault detection and diagnosis of spacecraft system.