To improve the diagnosis accuracy of tolerance fault for analog circuits, a Wavelet Neural Network (WNN) diagnosis method based on a modified UKF algorithm is proposed on the basis of feature extraction. An adaptive factor based on variance inflation principle is firstly introduced to improve the performance of UKF algorithm. Then, the modified UKF algorithm is used to perform optimal estimation for WNN parameters, establishing the tolerance fault diagnosis model based on feature extraction. The simulation results on Sallen-Key band-pass filter show that, the proposed method has a good convergence rate and diagnosis correct rate, which validates the feasibility and effectiveness of this method.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Tolerance Fault with Variance Inflation Factor for Analog Circuit


    Contributors:


    Publication date :

    2019-10-01


    Size :

    64761 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Kalman Filter and Correlated Measurement Noise: The Variance Inflation Factor

    Kermarrec, Gael / Jain, Ankit / Schon, Steffen | IEEE | 2022




    Analog Circuit Fault Diagnosis with Multi-Objective Particle Swarm Optimization

    Xu, Y. / Sun, J. / Chen, X. et al. | British Library Online Contents | 2012


    Statistical tolerance limits using components of variance

    NADOLSKI, L. / WOLTING, DUANE | AIAA | 1992