Analog Circuit Fault Prognostic Approach using Optimized RVM

Abstract

In this paper, a novel analog circuit fault prognostic approach is presented. The Pearson product-moment correlation coefficient (PPMCC) is used to calculate the circuit's health degree on the basis of the extracted output voltages. The relevance vector machine (RVM) algorithm with kernel function optimized by the quantum-behaved particle swarm optimization (QPSO) algorithm is utilized to estimate the remaining useful performance (RUP). A leapfrog filter is used in a fault prognostic experiment to verify the prognostic approach, and the experimental results reveal that the presented approach can forecast the analog circuit's RUP precisely.