An End-to-End Mutually Exclusive Autoencoder Method for Analog Circuit Fault Diagnosis
Author:
Funder
the National Natural Science Foundation of China
Hebei Province Graduate Innovation Funding Project
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s10836-023-06097-0.pdf
Reference26 articles.
1. Arabi N, Bourouba A. Belaout, Ayad M (2019) An accurate classifier based on adaptive neuro-fuzzy and features selection techniques for fault classification in analog circuits. Integration 64:50–59. https://doi.org/10.1016/j.vlsi.2018.08.001
2. Fang F, Li L, Gu Y et al (2020) A novel hybrid approach for crack detection. Pattern Recogn 107:107474. https://doi.org/10.1016/j.patcog.2020.107474
3. Gao T (2021) A novel fault diagnosis method for analog circuits with noise immunity and generalization ability. Neural Comput Appl 33:10537–11055. https://doi.org/10.1007/s00521-021-05810-4
4. Haidong S, Hongkai J, Ke Z, Dongdong W, Xingqiu L (2018) A novel tracking deep wavelet autoencoder method for intelligent fault diagnosis of electric locom-otive bearings. Mech Syst Signal Process 110:193–209. https://doi.org/10.1016/j.ymssp.2018.03.011
5. He W, He Y, Li B, Zhang C (2020) A Naive-Bayes-Based Fault Diagnosis Approach for Analog Circuit by Using Image-Oriented Feature Extraction and Selection Technique. IEEE Access 8:5065–5079. https://doi.org/10.1109/ACCESS.2018.2888950
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