{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T11:32:06Z","timestamp":1774524726140,"version":"3.50.1"},"reference-count":42,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2019,2,14]],"date-time":"2019-02-14T00:00:00Z","timestamp":1550102400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"The National Natural Science Fund","award":["31501213"],"award-info":[{"award-number":["31501213"]}]},{"name":"Science and Technology Key project of Henan Province","award":["172102310244"],"award-info":[{"award-number":["172102310244"]}]},{"name":"Science and Technology Key project of Henan Province","award":["182102110250"],"award-info":[{"award-number":["182102110250"]}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2017M612399"],"award-info":[{"award-number":["2017M612399"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>The parametric fault diagnosis of analog circuits is very crucial for condition-based maintenance (CBM) in prognosis and health management. In order to improve the diagnostic rate of parametric faults in engineering applications, a semi-supervised machine learning algorithm was used to classify the parametric fault. A lifting wavelet transform was used to extract fault features, a local preserving mapping algorithm was adopted to optimize the Fisher linear discriminant analysis, and a semi-supervised cooperative training algorithm was utilized for fault classification. In the proposed method, the fault values were randomly selected as training samples in a range of parametric fault intervals, for both optimizing the generalization of the model and improving the fault diagnosis rate. Furthermore, after semi-supervised dimensionality reduction and semi-supervised classification were applied, the diagnosis rate was slightly higher than the existing training model by fixing the value of the analyzed component.<\/jats:p>","DOI":"10.3390\/sym11020228","type":"journal-article","created":{"date-parts":[[2019,2,14]],"date-time":"2019-02-14T11:54:13Z","timestamp":1550145253000},"page":"228","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Parametric Fault Diagnosis of Analog Circuits Based on a Semi-Supervised Algorithm"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9296-2503","authenticated-orcid":false,"given":"Ling","family":"Wang","sequence":"first","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Henan Agricultural University, Zhengzhou 450002, China"}]},{"given":"Dongfang","family":"Zhou","sequence":"additional","affiliation":[{"name":"Department of Communication, National Digital Switching System Engineering and Technology R&amp;D Center (NDSC), Zhengzhou 450002, China"}]},{"given":"Hui","family":"Tian","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Henan Agricultural University, Zhengzhou 450002, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7600-3967","authenticated-orcid":false,"given":"Hao","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Henan Agricultural University, Zhengzhou 450002, China"}]},{"given":"Wei","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Henan Agricultural University, Zhengzhou 450002, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,2,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Tang, S., Li, Z., and Chen, L. 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