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Fault diagnosis of analog circuit based on a second map SVDD

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Abstract

To improve the diagnosis accuracy of analog circuit, this paper presents a second map support vector data description (SM-SVDD) method, which uses an anomalous and close surface instead of a hypesphere to describe the target data. The fault classifier is constructed by a SM SVDD algorithm to realize analog circuit fault diagnosis. Experimental results on two typical circuits confirm that the proposed method is effective in analog circuit fault diagnosis with good accuracy, and the performance surpasses other intelligent diagnosis methods, such as back propagation neural network, support vector machine and the normal SVDD. The developed method can be applied to other multi-classifier in electronic applications.

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Acknowledgments

This work is supported by National Natural Science Foundation of China (No. 61371041), National Natural Science Foundation of China (No. 61401215), Aviation Science Foundation of China (No. 2013ZD52055), Fundamental Research Funds for the Central Universities and Funding of Jiangsu Innovation Program for Graduate Education (No. CXLX11_0183).

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Authors and Affiliations

  1. College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Yuanyuan Jiang & Youren Wang

  2. Anhui University of Science and Technology, Huainan, China

    Yuanyuan Jiang

  3. College of Engineering, Nanjing Agricultural University, Nanjing, China

    Hui Luo

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  1. Yuanyuan Jiang
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  2. Youren Wang
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  3. Hui Luo
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Correspondence to Yuanyuan Jiang.

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Jiang, Y., Wang, Y. & Luo, H. Fault diagnosis of analog circuit based on a second map SVDD. Analog Integr Circ Sig Process 85, 395–404 (2015). https://doi.org/10.1007/s10470-015-0597-9

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  • DOI: https://doi.org/10.1007/s10470-015-0597-9

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