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In view of the traditional methods\u2019 excessive dependence on prior knowledge to manually extract features, their limited capacity to learn complex nonlinear relations in fault signals and the mixing of the collected signals with environmental noise in the course of the work of rotating machines, this article proposes a novel approach for detecting the bearing fault, which is based on deep learning. To effectively detect, locate, and identify faults in rolling bearings, a stacked noise reduction autoencoder is utilized for abstracting characteristic from the original vibration of signals, and then, the characteristic is provided as input for backpropagation (BP) network classifier. The results output by this classifier represent different fault categories. Experimental results obtained on rolling bearing datasets show that this method can be used to effectively diagnose bearing faults based on original time\u2010domain signals.<\/jats:p>","DOI":"10.1155\/2021\/9790053","type":"journal-article","created":{"date-parts":[[2021,5,15]],"date-time":"2021-05-15T19:35:07Z","timestamp":1621107307000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["A Denoising Autoencoder\u2010Based Bearing Fault Diagnosis System for Time\u2010Domain Vibration Signals"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7962-9466","authenticated-orcid":false,"given":"Yi","family":"Gu","sequence":"first","affiliation":[]},{"given":"Jiawei","family":"Cao","sequence":"additional","affiliation":[]},{"given":"Xin","family":"Song","sequence":"additional","affiliation":[]},{"given":"Jian","family":"Yao","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2021,5,15]]},"reference":[{"key":"e_1_2_8_1_2","doi-asserted-by":"crossref","unstructured":"JunboT. 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