{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T11:32:05Z","timestamp":1774524725519,"version":"3.50.1"},"reference-count":21,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2021,12,31]],"date-time":"2021-12-31T00:00:00Z","timestamp":1640908800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Analog circuits play an important role in modern electronic systems. Aiming to accurately diagnose the faults of analog circuits, this paper proposes a novel variant of a convolutional neural network, namely, a multi-scale convolutional neural network with a selective kernel (MSCNN-SK). In MSCNN-SK, a multi-scale average difference layer is developed to compute multi-scale average difference sequences, and then these sequences are taken as the input of the model, which enables it to mine potential fault characteristics. In addition, a dynamic convolution kernel selection mechanism is introduced to adaptively adjust the receptive field, so that the feature extraction ability of MSCNN-SK is enhanced. Based on two well-known fault diagnosis circuits, comparison experiments are conducted, and experimental results show that our proposed method achieves higher performance.<\/jats:p>","DOI":"10.3390\/a15010017","type":"journal-article","created":{"date-parts":[[2022,1,3]],"date-time":"2022-01-03T20:49:38Z","timestamp":1641242978000},"page":"17","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Analog Circuit Fault Diagnosis Using a Novel Variant of a Convolutional Neural Network"],"prefix":"10.3390","volume":"15","author":[{"given":"Liang","family":"Han","sequence":"first","affiliation":[{"name":"School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100091, China"}]},{"given":"Feng","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100091, China"}]},{"given":"Kaifeng","family":"Chen","sequence":"additional","affiliation":[{"name":"General Administration Office, Shandong University, Weihai 250100, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,31]]},"reference":[{"key":"ref_1","first-page":"3502315","article-title":"A novel incipient fault diagnosis method for analog circuits based on GMKL-SVM and wavelet fusion features","volume":"70","author":"Gao","year":"2020","journal-title":"IEEE Trans. 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