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An approach based on sparse random projections (SRPs) and K-nearest neighbor (KNN) to the realization of analog circuit soft fault diagnosis has been presented in this paper. The proposed method uses the wavelet packet energy spectrum and sparse random projections to preprocess the time response for feature extraction. Then, the variables of the fault features are constructed, which are used to form the observation sequences of K-nearest neighbor classifier. K-nearest neighbor classifier is used to accomplish the fault diagnosis of analog circuit. In this paper, four-opamp biquad high-pass filter has been used as simulation example to verify the effectiveness of the proposed method. The simulations show that the proposed method offers higher correct fault location rate in analog circuit soft fault diagnosis application as compared with the other methods.<\/jats:p>","DOI":"10.1155\/2021\/8040140","type":"journal-article","created":{"date-parts":[[2021,11,2]],"date-time":"2021-11-02T19:37:21Z","timestamp":1635881841000},"page":"1-9","source":"Crossref","is-referenced-by-count":3,"title":["Analog Circuit Soft Fault Diagnosis Based on Sparse Random Projections and K-Nearest Neighbor"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2944-3596","authenticated-orcid":true,"given":"Jian","family":"Sun","sequence":"first","affiliation":[{"name":"College of Electronic and Information Engineering, Jinling Institute of Technology, Nanjing 211169, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guobin","family":"Hu","sequence":"additional","affiliation":[{"name":"College of Electronic and Information Engineering, Jinling Institute of Technology, Nanjing 211169, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chenghua","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1109\/tim.2002.806004"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.1109\/5326.971655"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.1109\/tie.2012.2224074"},{"key":"4","doi-asserted-by":"publisher","DOI":"10.1109\/access.2020.2964054"},{"key":"5","doi-asserted-by":"publisher","DOI":"10.1109\/tim.2020.2969008"},{"key":"6","doi-asserted-by":"publisher","DOI":"10.1109\/tim.2002.1017726"},{"key":"7","doi-asserted-by":"publisher","DOI":"10.1109\/tim.2010.2050356"},{"key":"8","doi-asserted-by":"publisher","DOI":"10.1007\/s10836-017-5697-2"},{"key":"9","doi-asserted-by":"publisher","DOI":"10.1007\/s10836-012-5342-z"},{"key":"10","doi-asserted-by":"publisher","DOI":"10.1007\/s10470-016-0775-4"},{"key":"11","doi-asserted-by":"publisher","DOI":"10.1109\/access.2019.2923017"},{"key":"12","doi-asserted-by":"publisher","DOI":"10.1007\/s10836-016-5597-x"},{"key":"13","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-012-0947-9"},{"key":"14","doi-asserted-by":"publisher","DOI":"10.1007\/s10470-018-1377-0"},{"key":"15","doi-asserted-by":"publisher","DOI":"10.1023\/a:1012864504306"},{"key":"16","doi-asserted-by":"publisher","DOI":"10.1007\/s10470-016-0721-5"},{"key":"17","doi-asserted-by":"publisher","DOI":"10.1109\/access.2018.2823765"},{"key":"18","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2015.11.041"},{"key":"19","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2011.11.018"},{"issue":"8","key":"20","first-page":"230","article-title":"A fault diagnosis approach of analog circuit using wavelet-based fractal analysis and kernel LDA","volume":"27","author":"Y. 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