{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:44:03Z","timestamp":1777704243520,"version":"3.51.4"},"reference-count":10,"publisher":"SAGE Publications","issue":"4","license":[{"start":{"date-parts":[[2020,1,5]],"date-time":"2020-01-05T00:00:00Z","timestamp":1578182400000},"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":[[2020,4,30]]},"abstract":"<jats:p>With the rapid development of modern industry and science and technology, mechanical equipment has become larger, faster and more intelligent. In real life, there is no absolutely safe and reliable equipment, so it is impossible to require mechanical equipment not to break down in the operation process, and the working environment of mechanical equipment is complex and harsh, aging is serious, and breakdowns occur frequently. Research on effective intelligent fault detection methods has become a theoretical hot spot of current discipline research. Intelligent fault diagnosis of mechanical equipment is based on the algorithm to analyze the problems of equipment fault. In this paper, a fault detection model of mechanical equipment is proposed based on the method of fuzzy pattern recognition, and the fault detection is classified by the method of Fuzzy C-Means clustering. In this paper, the method of mechanical equipment fault detection based on Convolutional Neural Network is compared with the method proposed in this paper. The experimental results show that the model has good performance in fault detection and has strong practicability.<\/jats:p>","DOI":"10.3233\/jifs-179588","type":"journal-article","created":{"date-parts":[[2020,1,17]],"date-time":"2020-01-17T09:19:05Z","timestamp":1579252745000},"page":"3657-3664","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":4,"title":["Intelligent fault diagnosis method of mechanical equipment based on fuzzy pattern recognition"],"prefix":"10.1177","volume":"38","author":[{"given":"Jiaofei","family":"Huo","sequence":"first","affiliation":[{"name":"School of Mechanical and Electrical, Xijing University, Xi\u2019an, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dong","family":"Lin","sequence":"additional","affiliation":[{"name":"School of Mechanical and Electrical, Xijing University, Xi\u2019an, China"},{"name":"School of Teaching Researc, Xijing University, Xi\u2019an, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wanqiang","family":"Qi","sequence":"additional","affiliation":[{"name":"College of Automotive Engineering, Jilin Engineering Normal University, Changchun, Jilin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2020,1,5]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1002\/aic.690351210"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/41.107100"},{"key":"e_1_3_1_4_2","first-page":"11","article-title":"Fault diagnosis technology for complex processes","author":"Liu Y.","year":"2001","unstructured":"LiuY. and GuiW.H., Fault diagnosis technology for complex processes, Computer Engineering and Applications (7) (2001), 11\u201316.","journal-title":"Computer Engineering and Applications"},{"issue":"6","key":"e_1_3_1_5_2","first-page":"93","article-title":"Design idea of knowledge-based fault fuzzy prediction system","volume":"27","author":"Huang J.D.","year":"2001","unstructured":"HuangJ.D. and WangQ., Design idea of knowledge-based fault fuzzy prediction system, Computer Engineering 27(6) (2001), 93\u201394.","journal-title":"Computer Engineering"},{"issue":"13","key":"e_1_3_1_6_2","first-page":"50","article-title":"Fault diagnosis method for rolling bearings based on local mean decomposition and K-nearest neighbor algorithm","volume":"38","author":"Cai E.","year":"2015","unstructured":"CaiE., LiC.M., LiuD. and TanX.W., Fault diagnosis method for rolling bearings based on local mean decomposition and K-nearest neighbor algorithm, Modern Electronic Technology 38(13) (2015), 50\u201352.","journal-title":"Modern Electronic Technology"},{"issue":"11","key":"e_1_3_1_7_2","first-page":"134","article-title":"Fault diagnosis based on LTSA and K-nearest neighbor classifier","volume":"36","author":"Jiang J.S.","year":"2017","unstructured":"JiangJ.S., WangH.Q., KeY.L. and XiangW., Fault diagnosis based on LTSA and K-nearest neighbor classifier, Vibration and Impact 36(11) (2017), 134\u2013139.","journal-title":"Vibration and Impact"},{"key":"e_1_3_1_8_2","first-page":"229","article-title":"An improved method for face recognition based on sparse representation","author":"Lin J.","year":"2017","unstructured":"LinJ. and YangC., An improved method for face recognition based on sparse representation, International Conference on Computational and Information Sciences (2017), pp. 229\u2013232.","journal-title":"International Conference on Computational and Information Sciences"},{"key":"e_1_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.1155\/2016\/3843192"},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.3390\/s151127869"},{"key":"e_1_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2015.2450352"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JIFS-179588","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.3233\/JIFS-179588","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JIFS-179588","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:40:46Z","timestamp":1777455646000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.3233\/JIFS-179588"}},"subtitle":[],"editor":[{"given":"Weiping","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2020,1,5]]},"references-count":10,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2020,4,30]]}},"alternative-id":["10.3233\/JIFS-179588"],"URL":"https:\/\/doi.org\/10.3233\/jifs-179588","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,1,5]]}}}