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Analog Circuit Fault Diagnosis Based on the Fractional Sliding Model Observer

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Abstract

A novel approach based on the FSMO (fractional sliding model observer) is proposed to address the problem of nonlinear fault diagnosis in analog circuits. First, the fractional transform is extensively analyzed to derive the kernel functions. Next, the kernel functions are calculated and input into the SMO (sliding model observer). Then, the fractional sliding surface data are used to construct the FSMO of the fault feature extraction model. By analyzing the fractional sliding surface data, the digital fault features are extracted and used in the nonlinear fault diagnosis of the analog circuit. Finally, the experiments demonstrate the availability of the proposed method.

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Data Availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

The authors would like to thank the reviewers and the editors for their constructive comments and suggestions.

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Authors and Affiliations

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

    Yong Deng & Xian Zeng

  2. Oil and Gas Equipment Technology Sharing and Service Platform of Sichuan Province, Chengdu, 610500, China

    Yong Deng

  3. China Electronics Technology Cyber Security Co., Ltd, Chengdu, 610041, China

    Di Zhang & Ting Chen

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  1. Yong Deng
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  2. Xian Zeng
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  3. Di Zhang
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  4. Ting Chen
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Correspondence to Xian Zeng.

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Deng, Y., Zeng, X., Zhang, D. et al. Analog Circuit Fault Diagnosis Based on the Fractional Sliding Model Observer. Circuits Syst Signal Process 42, 6460–6480 (2023). https://doi.org/10.1007/s00034-023-02432-0

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