{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T14:14:19Z","timestamp":1774966459617,"version":"3.50.1"},"reference-count":27,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2021,11,26]],"date-time":"2021-11-26T00:00:00Z","timestamp":1637884800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61603223"],"award-info":[{"award-number":["61603223"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Jiangsu Provincial Qinglan Project","award":["2021"],"award-info":[{"award-number":["2021"]}]},{"name":"Research Development Fund of XJTLU","award":["RDF-18-02-30"],"award-info":[{"award-number":["RDF-18-02-30"]}]},{"name":"Research Development Fund of XJTLU","award":["RDF-20-01-18"],"award-info":[{"award-number":["RDF-20-01-18"]}]},{"name":"Key Program Special Fund in XJTLU","award":["KSF-E-34"],"award-info":[{"award-number":["KSF-E-34"]}]},{"name":"The Natural Science Foundation of the Jiangsu Higher Education Institutions of China","award":["20KJB520034"],"award-info":[{"award-number":["20KJB520034"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Data-driven based rolling bearing fault diagnosis has been widely investigated in recent years. However, in real-world industry scenarios, the collected labeled samples are normally in a different data distribution. Moreover, the features of bearing fault in the early stages are extremely inconspicuous. Due to the above mentioned problems, it is difficult to diagnose the incipient fault under different scenarios by adopting the conventional data-driven methods. Therefore, in this paper a new unsupervised rolling bearing incipient fault diagnosis approach based on transfer learning is proposed, with a novel feature extraction method based on a statistical algorithm, wavelet scattering network, and a stacked auto-encoder network. Then, the geodesic flow kernel algorithm is adopted to align the feature vectors on the Grassmann manifold, and the k-nearest neighbor classifier is used for fault classification. The experiment is conducted based on two bearing datasets, the bearing fault dataset of Case Western Reserve University and the bearing fault dataset of Xi\u2019an Jiaotong University. The experiment results illustrate the effectiveness of the proposed approach on solving the different data distribution and incipient bearing fault diagnosis issues.<\/jats:p>","DOI":"10.3390\/s21237894","type":"journal-article","created":{"date-parts":[[2021,12,1]],"date-time":"2021-12-01T01:45:02Z","timestamp":1638323102000},"page":"7894","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["Rolling Bearing Incipient Fault Diagnosis Method Based on Improved Transfer Learning with Hybrid Feature Extraction"],"prefix":"10.3390","volume":"21","author":[{"given":"Zhengni","family":"Yang","sequence":"first","affiliation":[{"name":"School of Advanced Technology, Xi\u2019an Jiaotong-Liverpool University, Suzhou 215123, China"},{"name":"Institute of Information Technology, Xinjiang Teacher\u2019s College, Urumqi 830043, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5634-5476","authenticated-orcid":false,"given":"Rui","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Advanced Technology, Xi\u2019an Jiaotong-Liverpool University, Suzhou 215123, China"},{"name":"Research Institute of Big Data Analytics, Xi\u2019an Jiaotong-Liverpool University, Suzhou 215123, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8163-8679","authenticated-orcid":false,"given":"Mengjie","family":"Huang","sequence":"additional","affiliation":[{"name":"Design School, Xi\u2019an Jiaotong-Liverpool University, Suzhou 215123, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"158","DOI":"10.3390\/app7020158","article-title":"Fault Diagnosis of Roller Bearings Based on a Wavelet Neural Network and Manifold Learning","volume":"7","author":"Wu","year":"2017","journal-title":"Appl. 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