{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:46:13Z","timestamp":1777704373246,"version":"3.51.4"},"reference-count":22,"publisher":"SAGE Publications","issue":"4","license":[{"start":{"date-parts":[[2018,6,28]],"date-time":"2018-06-28T00:00:00Z","timestamp":1530144000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"published-print":{"date-parts":[[2018,10,27]]},"abstract":"<jats:p>The traditional fault detection system of complex electronic equipment based on image analysis theory only analyzes the image characteristics of complex electronic equipment for artificial intelligent fault diagnosis. It cannot deal with the system diagnosis problem of qualitative fault data and has the problems of low accuracy and long time consuming of fault detection. To address these problems, an artificial intelligent fault diagnosis system of complex electronic equipment based on BP neural network is designed in this paper. BP neural network model for artificial intelligent fault diagnosis of complex electronic equipment is built based on system overall structure. The structure of BP neural network and learning algorithm is determined according to the actual fault problem. Learning and training of BP neural network are carried out by using sample data of fault. Artificial intelligent fault diagnosis algorithm of complex electronic equipment based on BP neural network and qualitative fault data is used, which combines the BP neural network and qualitative fault data. The preprocessing method is applied to quantify the fault data. Fault diagnosis is achieved by BP neural network technology. The system database and the implementation process of the BP neural network are designed. Experimental results show that the designed system can significantly improve the accuracy of fault detection of complex electronic equipment, improve the effect of fault detection, and reduce the time consuming of fault detection.<\/jats:p>","DOI":"10.3233\/jifs-169735","type":"journal-article","created":{"date-parts":[[2018,6,29]],"date-time":"2018-06-29T16:39:11Z","timestamp":1530290351000},"page":"4141-4151","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":12,"title":["Artificial intelligent fault diagnosis system of complex electronic equipment"],"prefix":"10.1177","volume":"35","author":[{"given":"Xiangmin","family":"Wang","sequence":"first","affiliation":[{"name":"School of Automation, Nanjing University of Science and Technology, Nanjing, China"}]},{"given":"Jun","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Automation, Nanjing University of Science and Technology, Nanjing, China"}]},{"given":"M.","family":"Privault","sequence":"additional","affiliation":[{"name":"National Science Foundation, Computer and Information Science and Engineering Directorate, Arlington, VA, USA"}]}],"member":"179","published-online":{"date-parts":[[2018,6,28]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.epsr.2017.04.025"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1609\/aimag.v36i4.2616"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1615\/JPorMedia.v18.i9.60"},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.3991\/ijet.v9i3.3367"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11269-014-0773-1"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2016.10.002"},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.2118\/150314-PA"},{"key":"e_1_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-012-9327-1"},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.1111\/exsy.12048"},{"key":"e_1_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.1007\/s12665-014-3997-8"},{"issue":"4","key":"e_1_3_1_12_2","first-page":"1","article-title":"Introduction to the special issue on CSP technologies in artificial intelligence","volume":"20","author":"\u00c9ric G.","year":"2015","unstructured":"\u00c9ricG. and MazureB., Introduction to the special issue on CSP technologies in artificial intelligence, Constraints20(4) (2015), 1\u20132.","journal-title":"Constraints"},{"key":"e_1_3_1_13_2","doi-asserted-by":"publisher","DOI":"10.1007\/s40593-014-0019-7"},{"key":"e_1_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.1002\/cpt.650"},{"key":"e_1_3_1_15_2","doi-asserted-by":"publisher","DOI":"10.2112\/SI70-070.1"},{"key":"e_1_3_1_16_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.sjbs.2016.09.005"},{"issue":"8","key":"e_1_3_1_17_2","first-page":"5217","article-title":"Performance of Financial Expenditure in China\u2019s basic science and math education: Panel Data Analysis Based on CCR Model and BBC Model","volume":"13","author":"Si L.","year":"2017","unstructured":"SiL. and QiaoH., Performance of Financial Expenditure in China\u2019s basic science and math education: Panel Data Analysis Based on CCR Model and BBC Model, Eurasia Journal of Mathematics Science and Technology Education13(8) (2017), 5217\u20135224.","journal-title":"Eurasia Journal of Mathematics Science and Technology Education"},{"issue":"2","key":"e_1_3_1_18_2","first-page":"46","article-title":"On pointwise convergence of bivariate nonlinear singular integral operators","volume":"44","author":"Uysal G.","year":"2017","unstructured":"UysalG., YilmazM.M. and IbikliE., On pointwise convergence of bivariate nonlinear singular integral operators, Kuwait Journal of Science44(2) (2017), 46\u201357.","journal-title":"Kuwait Journal of Science"},{"key":"e_1_3_1_19_2","doi-asserted-by":"publisher","DOI":"10.1080\/09720502.2016.1179488"},{"key":"e_1_3_1_20_2","doi-asserted-by":"publisher","DOI":"10.1080\/09720529.2016.1178908"},{"key":"e_1_3_1_21_2","doi-asserted-by":"publisher","DOI":"10.3934\/dcdss.2018029"},{"key":"e_1_3_1_22_2","doi-asserted-by":"publisher","DOI":"10.21042\/AMNS.2016.1.00012"},{"key":"e_1_3_1_23_2","doi-asserted-by":"publisher","DOI":"10.21042\/AMNS.2016.1.00014"}],"container-title":["Journal of Intelligent &amp; 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