{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T12:26:03Z","timestamp":1771331163445,"version":"3.50.1"},"reference-count":50,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2018,8,14]],"date-time":"2018-08-14T00:00:00Z","timestamp":1534204800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51577046"],"award-info":[{"award-number":["51577046"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the State Key Program of National Natural Science Foundation of China","award":["51637004"],"award-info":[{"award-number":["51637004"]}]},{"name":"the national key research and development plan \u201cimportant scientific instruments and equipment develop-ment\u201d","award":["2016YFF0102200"],"award-info":[{"award-number":["2016YFF0102200"]}]},{"name":"Equipment research project in advance","award":["41402040301"],"award-info":[{"award-number":["41402040301"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>In this paper, a novel method with cross-wavelet singular entropy (XWSE)-based feature extractor and support vector machine (SVM) is proposed for analog circuit fault diagnosis. Primarily, cross-wavelet transform (XWT), which possesses a good capability to restrain the environment noise, is applied to transform the fault signal into time-frequency spectra (TFS). Then, a simple segmentation method is utilized to decompose the TFS into several blocks. We employ the singular value decomposition (SVD) to analysis the blocks, then Tsallis entropy of each block is obtained to construct the original features. Subsequently, the features are imported into parametric t-distributed stochastic neighbor embedding (t-SNE) for dimension reduction to yield the discriminative and concise fault characteristics. Finally, the fault characteristics are entered into SVM classifier to locate circuits\u2019 defects that the free parameters of SVM are determined by quantum-behaved particle swarm optimization (QPSO). Simulation results show the proposed approach is with superior diagnostic performance than other existing methods.<\/jats:p>","DOI":"10.3390\/e20080604","type":"journal-article","created":{"date-parts":[[2018,8,14]],"date-time":"2018-08-14T10:31:16Z","timestamp":1534242676000},"page":"604","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["Analog Circuit Fault Diagnosis via Joint Cross-Wavelet Singular Entropy and Parametric t-SNE"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9289-1888","authenticated-orcid":false,"given":"Wei","family":"He","sequence":"first","affiliation":[{"name":"School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yigang","family":"He","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China"},{"name":"School of Electrical Engineering, Wuhan University, Wuhan 430072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2216-3471","authenticated-orcid":false,"given":"Bing","family":"Li","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3892-5124","authenticated-orcid":false,"given":"Chaolong","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China"},{"name":"School of Physics and Electronic Engineering, Anqing Normal University, Anqing 246011, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,8,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"544","DOI":"10.1109\/TIM.2002.1017726","article-title":"Analog Fault Diagnosis of Actual Circuits Using Neural Networks","volume":"51","author":"Aminian","year":"2002","journal-title":"IEEE Trans. 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