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To address this issue, a convolutional neural network (CNN) based fault diagnosis approach is proposed. In the proposed method, envelope order spectra extracted from the raw vibration signals are used to provide abundant information about the fault characteristic orders, which are features invariant to the rotating speed. Subsequently, to extract these representative features automatically, a CNN model is constructed and employed, which avoid the manual feature selection. Finally, the type of bearing defects can be recognized successfully. In the experimental verification, the CNN is trained using a data set corresponds to one revolution per minute (RPM), while the data sets correspond to other RPMs are employed to verify the classification accuracy of the trained CNN, which can reflect the effectiveness of proposed method for bearing fault detection under different rotating speed. Experimental results show the satisfactory performance of fault-pattern recognition for the proposed method. When compared with some other approaches using intelligence-based fault diagnosis method, the results show the superiority of the proposed method.<\/jats:p>","DOI":"10.3233\/jifs-190101","type":"journal-article","created":{"date-parts":[[2019,7,30]],"date-time":"2019-07-30T11:29:08Z","timestamp":1564486148000},"page":"3027-3040","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":10,"title":["Fault diagnosis method for rolling element bearing with variable rotating speed using envelope order spectrum and convolutional neural network"],"prefix":"10.1177","volume":"37","author":[{"given":"Danchen","family":"Zhu","sequence":"first","affiliation":[{"name":"Department of Power Engineering, Naval University of Engineering, Wuhan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongxiang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Power Engineering, Naval University of Engineering, Wuhan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lei","family":"Zhao","sequence":"additional","affiliation":[{"name":"Department of Power Engineering, Naval University of Engineering, Wuhan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2019,7,29]]},"reference":[{"key":"e_1_3_1_2_2","article-title":"Recent advances in key-performance-indicator oriented prognosis and diagnosis with a matlab toolbox: DB-LIT","author":"Jiang Y.","year":"2018","unstructured":"JiangY. and YinS., Recent advances in key-performance-indicator oriented prognosis and diagnosis with a matlab toolbox: DB-LIT, IEEE Transactions on Industrial and Infomatics, 2018.","journal-title":"IEEE Transactions on Industrial and Infomatics"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/IECON.2016.7792957"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2018.11.083"},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1088\/0957-0233\/25\/9\/095004"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2018.07.043"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2004.09.001"},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jsv.2018.01.051"},{"key":"e_1_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6501\/aad499"},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.1049\/iet-rpg.2016.0070"},{"key":"e_1_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.3390\/e20040212"},{"key":"e_1_3_1_12_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2018.09.013"},{"key":"e_1_3_1_13_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2015.12.020"},{"key":"e_1_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.3390\/s18020337"},{"key":"e_1_3_1_15_2","doi-asserted-by":"publisher","DOI":"10.3390\/app7020158"},{"key":"e_1_3_1_16_2","article-title":"Faults","author":"Liang M.","year":"2018","unstructured":"LiangM., SuD., HuD., GeM. and NovelA., Faults Diagnosis Method for Rolling Element Bearings Based on ELCD and Extreme Learning Machine, Shock and Vibration 2018.","journal-title":"Diagnosis Method for Rolling Element Bearings Based on ELCD and Extreme Learning Machine"},{"key":"e_1_3_1_17_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2018.08.038"},{"key":"e_1_3_1_18_2","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6501\/aa6e22"},{"key":"e_1_3_1_19_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2014.09.003"},{"key":"e_1_3_1_20_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2018.02.016"},{"key":"e_1_3_1_21_2","doi-asserted-by":"publisher","DOI":"10.1088\/0957-0233\/26\/11\/115002"},{"key":"e_1_3_1_22_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.microrel.2017.03.006"},{"key":"e_1_3_1_23_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.sigpro.2016.07.028"},{"key":"e_1_3_1_24_2","author":"Jiang H.","year":"2018","unstructured":"JiangH., LiX., ShaoH. and ZhaoK. 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