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Soft Fault Feature Extraction in Nonlinear Analog Circuit Fault Diagnosis

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Abstract

Aiming at the problem to diagnose soft faults in nonlinear analog circuits, a novel approach to extract fault features is proposed. The approach is based on the Wigner–Ville distribution (WVD) of the subband Volterra model. First, the subband Volterra kernels of the circuit under test are cleared. Then, the subband Volterra kernels are used to obtain the WVD functions. The fault features are extracted from the WVD functions and taken as input data into the hidden Markov model (HMM). Finally, with classification of features using HMMs, the soft fault diagnosis of the nonlinear analog circuit is achieved. The simulations and experiments show that the method proposed in this paper can extract the fault features effectively and improve the fault diagnosis.

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Acknowledgments

The authors would like to thank the reviewers and the editors for their constructive comments and suggestions. This work is supported by key research project of Sichuan Provincial Department of Education, China (13ZA0186)

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Authors and Affiliations

  1. School of Mechatronic Engineering, Southwest Petroleum University, Chengdu, 610500, China

    Yong Deng

  2. College of Applied Technology, Southwest Petroleum University, Nanchong, 637001, China

    Guodong Chai

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  1. Yong Deng
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  2. Guodong Chai
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Correspondence to Yong Deng.

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Deng, Y., Chai, G. Soft Fault Feature Extraction in Nonlinear Analog Circuit Fault Diagnosis. Circuits Syst Signal Process 35, 4220–4248 (2016). https://doi.org/10.1007/s00034-016-0265-z

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