A multiple heterogeneous kernel RVM approach for analog circuit fault prognostic

  • Chaolong Zhang
  • Yigang He
  • Lifen Yuan
  • Sheng Xiang
Article
  • 16 Downloads

Abstract

This paper presents a multiple heterogeneous kernel relevance vector machine (MHKRVM) approach for analog circuit fault prognostic. Compared to other kernel learning methods, multiple kernel learning method produces the optimal kernel function because many effective kernels’ combination always generates better generalization performance. By using the multiple heterogeneous kernel learning method, the proposed MHKRVM method’s kernel function holds its diversification. Meanwhile, the sparse weights of consisted basic kernels in the MHKRVM are helpful in prediction accuracy, and they are yielded through particle swarm optimization algorithm. Six fault prognostic cases are conducted to demonstrate the whole prognostic procedure, and prove that the presented MHKRVM can predict the trend of falling circuit elements’ health degree trajectories closely and estimate the remaining useful performance of failing circuit elements accurately.

Keywords

Analog circuits Fault prognostic Relevance vector machine Multiple heterogeneous kernel learning method PSO algorithm 

Notes

Acknowledgement

This work was supported by the National Natural Science Foundation of China under Grant Nos. 51607004, 51577046, the State Key Program of National Natural Science Foundation of China under Grant No. 51637004, the national key research and development plan “important scientific instruments and equipment development” Grant No. 2016YFF0102200, Anhui Provincial Natural Science Foundation No. 1608085QF157, equipment research project in advance Grant No. 41402040301, and Anhui Provincial College Student’s Creative Lab Construction Plan under Grant 2016ckjh112.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Chaolong Zhang
    • 1
    • 2
  • Yigang He
    • 2
    • 3
  • Lifen Yuan
    • 2
  • Sheng Xiang
    • 2
  1. 1.School of Physics and Electronic EngineeringAnqing Normal UniversityAnqingChina
  2. 2.School of Electrical Engineering and AutomationHefei University of TechnologyHefeiChina
  3. 3.School of Electrical EngineeringWuhan UniversityWuhanChina

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