{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:33:21Z","timestamp":1777703601741,"version":"3.51.4"},"reference-count":20,"publisher":"SAGE Publications","issue":"6","license":[{"start":{"date-parts":[[2018,6,11]],"date-time":"2018-06-11T00:00:00Z","timestamp":1528675200000},"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":[[2018,6,22]]},"abstract":"<jats:p>\n                    It is a great challenge to accurately and automatically identify different faults of the key components in rotating machinery. In this paper, a new method called feature fusion deep belief network is proposed for the intelligent fault diagnosis of rolling bearing. Firstly, a deep belief network (DBN) is constructed with several pre-trained restricted Boltzmann machines for feature learning of the raw vibration data. Secondly, locality preserving projection (LPP) is adopted to fuse the deep features to further enhance the quality of the learned deep features. Finally, the fusion deep features are fed into\n                    <jats:italic>Softmax<\/jats:italic>\n                    for automatic and accurate fault diagnosis. The proposed method is applied to analyze the experimental rolling bearing signals, and the results show that the proposed method is more effective than the traditional intelligent diagnosis methods.\n                  <\/jats:p>","DOI":"10.3233\/jifs-169530","type":"journal-article","created":{"date-parts":[[2018,6,12]],"date-time":"2018-06-12T18:29:08Z","timestamp":1528828148000},"page":"3513-3521","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":26,"title":["A feature fusion deep belief network method for intelligent fault diagnosis of rotating machinery"],"prefix":"10.1177","volume":"34","author":[{"given":"Hongkai","family":"Jiang","sequence":"first","affiliation":[{"name":"School of Aeronautics, Northwestern Polytechnical University, Xi\u2019an, People\u2019s Republic of China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haidong","family":"Shao","sequence":"additional","affiliation":[{"name":"School of Aeronautics, Northwestern Polytechnical University, Xi\u2019an, People\u2019s Republic of China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinxia","family":"Chen","sequence":"additional","affiliation":[{"name":"Shanghai Engineering Research Center of Civil Aircraft Monitoring, Shanghai, People\u2019s Republic of China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiayang","family":"Huang","sequence":"additional","affiliation":[{"name":"Shanghai Engineering Research Center of Civil Aircraft Monitoring, Shanghai, People\u2019s Republic of China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2018,6,11]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2012.12.010"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.isatra.2017.03.017"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2015.08.023"},{"key":"e_1_3_2_5_2","first-page":"53","article-title":"Automatic Feature extraction of time-seriesapplied to fault severity evaluation of helical gearbox instationary and non-stationary speed operation","volume":"58","author":"Cabrera D.","year":"2017","unstructured":"CabreraD., SanchoF., LiC., CerradaM., SanchezR.V., PachecoandF. and OliveiraJ.V.D., Automatic Feature extraction of time-seriesapplied to fault severity evaluation of helical gearbox instationary and non-stationary speed operation, Applied SoftComputing58 (2017), 53\u201364.","journal-title":"Applied SoftComputing"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2009.06.060"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2017.08.002"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIE.2013.2273471"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2017.03.034"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2014.09.003"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2015.10.025"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1088\/0957-0233\/26\/11\/115002"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2013.02.022"},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2013.12.026"},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2015.11.044"},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jsv.2014.09.026"},{"key":"e_1_3_2_17_2","doi-asserted-by":"publisher","DOI":"10.1126\/science.1127647"},{"key":"e_1_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2010.12.095"},{"key":"e_1_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2016.12.012"},{"key":"e_1_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2014.09.086"},{"key":"e_1_3_2_21_2","first-page":"3371","article-title":"stacked denoising autoencoders: Learning useful representations in adeep network with a local denoising criterion","volume":"11","author":"Vincent P.","year":"2010","unstructured":"VincentP., LarochelleH., LajoieI., BengioY. and ManzagolstackedP.A., stacked denoising autoencoders: Learning useful representations in adeep network with a local denoising criterion, Journal of Machine Learning Research11 (2010), 3371\u20133408.","journal-title":"Journal of Machine Learning Research"}],"container-title":["Journal of Intelligent &amp; 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