Material Identification of Particles in Space-Borne Electronic Equipments Based on Principal Component Analysis

The existence of loose particle left inside the space-borne electronic equipments is one of the main factors affecting the reliability of the whole system. It is important to identify the particle material for analyzing their sources. The conventional material identification algorithms mainly rely on frequency and wavelet domain features. However, these features are usually overlapped and redundant, resulting in unsatisfactory material identification accuracy. The main objective of this paper is to improve the accuracy of material identification. The principal component analysis (PCA) is employed to reselect the nine features extracted from time and frequency domains, leading to six less correlated principal components. The reselected principal components are used for material identification using support vector machines (SVM). The experimental results show that this new method can effectively distinguish the type of materials including wire, aluminum and tin particles.