Detection and material identification of loose particles inside the aerospace power supply via stochastic resonance and LVQ network

Abstract

The detection of loose particles inside an aerospace power supply is important to improve the reliability of the whole space system. This paper investigates the detection and material identification of loose particles within an aerospace power supply based on the particle impact noise detection (PIND) test. A stochastic resonance algorithm is employed to detect the presence of tiny particles. A learning vector quantization (LVQ)-based material identification method is proposed. Finally, experiments are conducted to demonstrate the effectiveness of the proposed technique. Experimental results show that the accuracies of particle detection and material identification are above 90% and 80%, respectively, which meets end-user requirements.