{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T15:41:06Z","timestamp":1783438866100,"version":"3.54.6"},"reference-count":32,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2019,7,13]],"date-time":"2019-07-13T00:00:00Z","timestamp":1562976000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Science and Technology Major Project of Guizhou Province","award":["[2013]6019"],"award-info":[{"award-number":["[2013]6019"]}]},{"name":"Project of Guizhou High-Level Study Abroad Talents Innovation and Entrepreneurship","award":["2018.0002"],"award-info":[{"award-number":["2018.0002"]}]},{"name":"Project of China Scholarship Council","award":["201806675013"],"award-info":[{"award-number":["201806675013"]}]},{"name":"Open Fund of Guizhou Provincial Public Big Data Key Laboratory","award":["2017BDKFJJ019"],"award-info":[{"award-number":["2017BDKFJJ019"]}]},{"name":"Guizhou University Foundation for the introduction of talent","award":["(2016) No. 13"],"award-info":[{"award-number":["(2016) No. 13"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>In order to realize single fault detection (SFD) from the multi-fault coupling bearing data and further research on the multi-fault situation of bearings, this paper proposes a method based on features self-extraction of a Sparse Auto-Encoder (SAE) and results fusion of improved Dempster\u2013Shafer evidence theory (D\u2013S). Multi-fault signal compression features of bearings were extracted by SAE on multiple vibration sensors\u2019 data. Data sets were constructed by the extracted compression features to train the Support Vector Machine (SVM) according to the rule of single fault detection (R-SFD) this paper proposed. Fault detection results were obtained by the improved D\u2013S evidence theory, which was implemented via correcting the 0 factor in the Basic Probability Assignment (BPA) and modifying the evidence weight by Pearson Correlation Coefficient (PCC). Extensive evaluations of the proposed method on the experiment platform datasets showed that the proposed method could realize single fault detection from multi-fault bearings. Fault detection accuracy increases as the output feature dimension of SAE increases; when the feature dimension reached 200, the average detection accuracy of the three sensors for bearing inner, outer, and ball faults achieved 87.36%, 87.86% and 84.46%, respectively. The three types\u2019 fault detection accuracy\u2014reached to 99.12%, 99.33% and 98.46% by the improved Dempster\u2013Shafer evidence theory (IDS) to fuse the sensors\u2019 results\u2014is respectively 0.38%, 2.06% and 0.76% higher than the traditional D\u2013S evidence theory. That indicated the effectiveness of improving the D\u2013S evidence theory by evidence weight calculation of PCC.<\/jats:p>","DOI":"10.3390\/e21070687","type":"journal-article","created":{"date-parts":[[2019,7,15]],"date-time":"2019-07-15T04:55:27Z","timestamp":1563166527000},"page":"687","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["A Novel Method for Intelligent Single Fault Detection of Bearings Using SAE and Improved D\u2013S Evidence Theory"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2191-1570","authenticated-orcid":false,"given":"Jianguang","family":"Lu","sequence":"first","affiliation":[{"name":"Key Laboratory of Advanced Manufacturing Technology, Ministry of Education, Guizhou University, Guiyang 550025, China"},{"name":"State Key Laboratory of Public Big Data, Guizhou University, Guiyang 550025, China"},{"name":"School of Mechanical Engineering, Guizhou University, Guiyang 550025, China"},{"name":"Department of Computer Science, University of Texas Rio Grande Valley, Edinburg, TX 78541, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9654-631X","authenticated-orcid":false,"given":"Huan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Advanced Manufacturing Technology, Ministry of Education, Guizhou University, Guiyang 550025, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xianghong","family":"Tang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Advanced Manufacturing Technology, Ministry of Education, Guizhou University, Guiyang 550025, China"},{"name":"State Key Laboratory of Public Big Data, Guizhou University, Guiyang 550025, China"},{"name":"School of Mechanical Engineering, Guizhou University, Guiyang 550025, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,7,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.measurement.2017.08.036","article-title":"Early fault diagnosis of bearing and stator faults of the single-phase induction motor using acoustic signals","volume":"113","author":"Glowacz","year":"2018","journal-title":"Measurement"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.ymssp.2017.09.042","article-title":"Time\u2013frequency analysis based on ensemble local mean decomposition and fast kurtogram for rotating machinery fault diagnosis","volume":"103","author":"Wang","year":"2018","journal-title":"Mech. 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