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Analog Circuit Fault Prognostic Approach using Optimized RVM

Volume 15, Number 5, May 2019, pp. 1453-1461
DOI: 10.23940/ijpe.19.05.p22.14531461

Chaolong Zhanga,b, Yigang Heb, Shanhe Jianga, Lanfang Zhanga, and Xiaolu Wangc

aSchool of Physics and Electronic Engineering, Anqing Normal University, Anqing, 246011, China
bSchool of Electrical Engineering and Automation, Wuhan University, Wuhan, 430072, China
cSchool of Information Technology, Jiangsu Vocational Institute of Commerce, Nanjing, 211168, China


(Submitted on December 12, 2018; Revised on January 15, 2019; Accepted on February 17, 2019)


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.

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