{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,26]],"date-time":"2025-10-26T15:08:20Z","timestamp":1761491300337,"version":"build-2065373602"},"reference-count":24,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2021,10,18]],"date-time":"2021-10-18T00:00:00Z","timestamp":1634515200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61571161","62071150"],"award-info":[{"award-number":["61571161","62071150"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In the actual fault diagnosis process of an analog circuit, there is often a problem due to the lack of fault samples, leading to the low-accuracy of diagnostic models. Therefore, using positive samples that are easy to obtain to establish diagnostic models became a research hotspot in the field of analog circuit fault diagnosis. This paper proposes a method based on Support Vector Data Description (SVDD) and Dempster\u2013Shafer evidence theory (D\u2013S evidence theory) for fault diagnosis of modular analog circuit. Firstly, the principle of circuit module partition is proposed to divide the analog circuit under test, and the output port of each module is selected as test point. Secondly, the paper extracts the feature of the time-domain and frequency-domain output signals of the circuit module through Principal Component Analysis (PCA). Thirdly, four state detection models based on SVDD are established to judge the working state of each circuit module, including TSG, TSP, FSG, and FSP state detection model. Finally, the D\u2013S theory is introduced to integrate the test results of each model for locating fault circuit module. To verify the effectiveness of the proposed method, the dual bandpass filter circuit is selected for simulation and hardware experiment. The results show that the proposed method can locate the analog fault effectively and has a higher diagnosis accuracy.<\/jats:p>","DOI":"10.3390\/s21206889","type":"journal-article","created":{"date-parts":[[2021,10,20]],"date-time":"2021-10-20T21:31:26Z","timestamp":1634765486000},"page":"6889","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["A Fault Diagnosis Method of Modular Analog Circuit Based on SVDD and D\u2013S Evidence Theory"],"prefix":"10.3390","volume":"21","author":[{"given":"Peng","family":"Sun","sequence":"first","affiliation":[{"name":"School of Electronics and Information Engineering, Harbin Institute of Technology, 150100 Harbin, China"}]},{"given":"Zhiming","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Engineering, Harbin Institute of Technology, 150100 Harbin, China"}]},{"given":"Yueming","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Engineering, Harbin Institute of Technology, 150100 Harbin, China"}]},{"given":"Shaohua","family":"Jia","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Engineering, Harbin Institute of Technology, 150100 Harbin, China"}]},{"given":"Xiyuan","family":"Peng","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Engineering, Harbin Institute of Technology, 150100 Harbin, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2841","DOI":"10.1109\/TCSI.2021.3076282","article-title":"Soft Fault Diagnosis of Analog Circuits Based on a ResNet with Circuit Spectrum Map","volume":"68","author":"Ji","year":"2021","journal-title":"IEEE Trans. Circuits Syst. I"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1277","DOI":"10.1049\/el.2019.2892","article-title":"Analogue circuit fault diagnosis based on convolution neural network","volume":"55","author":"Du","year":"2019","journal-title":"Electron. Lett."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10470-021-01851-w","article-title":"Soft fault diagnosis of analog circuits based on semi-supervised support vector machine","volume":"108","author":"Wang","year":"2021","journal-title":"Analog. Integr. Circuits Signal Process."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2337","DOI":"10.1109\/TIE.2019.2907500","article-title":"Review on diagnosis techniques for intermittent faults in dynamic systems","volume":"67","author":"Zhou","year":"2020","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1766","DOI":"10.1109\/TASE.2020.3017755","article-title":"Discrete Component Prognosis for Hybrid Systems Under Intermittent Faults","volume":"18","author":"Xiao","year":"2020","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"782","DOI":"10.1109\/TIM.2019.2905307","article-title":"A Combined Method for Analog Circuit Fault Diagnosis Based on Dependence Matrices and Intelligent Classifiers","volume":"69","author":"Shi","year":"2019","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_7","first-page":"1","article-title":"A Novel Incipient Fault Diagnosis Method for Analog Circuits Based on GMKL-SVM and Wavelet Fusion Features","volume":"70","author":"Gao","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"106370","DOI":"10.1016\/j.epsr.2020.106370","article-title":"Fault Diagnosis for Power Electronics Converters based on Deep Feedforward Network and Wavelet Compression","volume":"185","author":"Lei","year":"2020","journal-title":"Electr. Power Syst. Res."},{"key":"ref_9","unstructured":"Song, G.-M., Li, Q., Luo, G., Jiang, S.-Y., and Wang, H.-J. (2015, January 16\u201318). Analog circuit fault diagnosis using wavelet feature optimization approach. Proceedings of the IEEE International Conference on Electronic Measurement and Instruments, Qingdao, China."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1016\/j.neucom.2013.11.012","article-title":"Improved diagnostics for the incipient faults in analog circuits using LSSVM based on PSO algorithm with Mahalanobis distance","volume":"133","author":"Long","year":"2014","journal-title":"Neurocomputing"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1007\/s10836-012-5342-z","article-title":"A New Analog Circuit Fault Diagnosis Method Based on Improved Mahalanobis Distance","volume":"29","author":"Han","year":"2013","journal-title":"J. Electron. Test."},{"key":"ref_12","first-page":"2631","article-title":"A Novel Method for Analog Fault Diagnosis Based on Neural Networks and Genetic Algorithms","volume":"29","author":"Tan","year":"2008","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1102","DOI":"10.1016\/j.neucom.2010.12.003","article-title":"A novel approach for analog fault diagnosis based on neural networks and improved kernel PCA","volume":"74","author":"Xiao","year":"2011","journal-title":"Neurocomputing"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2175","DOI":"10.1109\/TIM.2007.908152","article-title":"Automated Diagnostics of Analog Systems Using Fuzzy Logic Approach","volume":"56","author":"Bilski","year":"2007","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Tang, J.-Y., Shi, Y.-B., and Jiang, D. (2009, January 28\u201329). Analog Circuit Fault Diagnosis with Hybrid PSO-SVM. Proceedings of the IEEE Circuits and Systems International Conference on Testing and Diagnosis, Chengdu, China.","DOI":"10.1109\/CAS-ICTD.2009.4960778"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"23053","DOI":"10.1109\/ACCESS.2018.2823765","article-title":"Analog Circuit Incipient Fault Diagnosis Method Using DBN Based Features Extraction","volume":"6","author":"Zhang","year":"2018","journal-title":"IEEE Access"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1109\/TSM.2006.873524","article-title":"Multiblock principal component analysis based on a combined index for semiconductor fault detection and diagnosis","volume":"19","author":"Cherry","year":"2006","journal-title":"IEEE Trans. Semicond. Manuf."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"457","DOI":"10.1016\/S0967-0661(98)00027-6","article-title":"Joint diagnosis of process and sensor faults using principal component analysis","volume":"6","author":"Dunia","year":"1998","journal-title":"Control. Eng. Pract."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.jprocont.2017.03.004","article-title":"Fault detection using multiscale PCA-based moving window GLRT","volume":"54","author":"Sheriff","year":"2017","journal-title":"J. Process. Control"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1023\/B:MACH.0000008084.60811.49","article-title":"Support vector data description","volume":"54","author":"Tax","year":"2004","journal-title":"Mach. Learn."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Shafer, G. (1976). A Mathematical Theory of Evidence, Princeton University Press.","DOI":"10.1515\/9780691214696"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"559","DOI":"10.1109\/21.57269","article-title":"Generalizing the Dempster-Schafer theory to fuzzy sets","volume":"20","author":"Yen","year":"1990","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"366","DOI":"10.1016\/j.patrec.2005.08.025","article-title":"Fault diagnosis of machines based on D\u2013S evidence theory. Part 1: D\u2013S evidence theory and its improvement","volume":"27","author":"Fan","year":"2006","journal-title":"Pattern Recognit. Lett."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Li, S.-B., Liu, G.-K., Tang, X.-H., Lu, J.-G., and Hu, J.-J. (2017). An Ensemble Deep Convolutional Neural Network Model with Improved D\u2013S Evidence Fusion for Bearing Fault Diagnosis. Sensors, 17.","DOI":"10.3390\/s17081729"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/20\/6889\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:16:47Z","timestamp":1760167007000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/20\/6889"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,18]]},"references-count":24,"journal-issue":{"issue":"20","published-online":{"date-parts":[[2021,10]]}},"alternative-id":["s21206889"],"URL":"https:\/\/doi.org\/10.3390\/s21206889","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2021,10,18]]}}}