{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T17:54:26Z","timestamp":1777571666980,"version":"3.51.4"},"reference-count":25,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2018,8,3]],"date-time":"2018-08-03T00:00:00Z","timestamp":1533254400000},"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":["Grant No. 51705360, 51775380"],"award-info":[{"award-number":["Grant No. 51705360, 51775380"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Key Research and Development Project of China","award":["Grant No. 2017YFF0204802"],"award-info":[{"award-number":["Grant No. 2017YFF0204802"]}]},{"name":"the Foundation for Innovative Research Groups of the National Natural Science Foundation of China","award":["Grant No. 51721003"],"award-info":[{"award-number":["Grant No. 51721003"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The fault diagnosis of dimensional variation plays an essential role in the production of an automotive body. However, it is difficult to identify faults based on small labeled sample data using traditional supervised learning methods. The present study proposed a novel feature extraction method named, semi-supervised complete kernel Fisher discriminant (SS-CKFDA), and a new fault diagnosis flow for automotive assembly was introduced based on this method. SS-CKFDA is a combination of traditional complete kernel Fisher discriminant (CKFDA) and semi-supervised learning. It adjusts the Fisher criterion with the data global structure extracted from large unlabeled samples. When the number of labeled samples is small, the global structure that exists in the measured data can effectively improve the extraction effects of the projected vector. The experimental results on Tennessee Eastman Process (TEP) data demonstrated that the proposed method can improve diagnostic performance, when compared to other Fisher discriminant algorithms. Finally, the experimental results on the optical coordinate data proves that the method can be applied in the automotive assembly process, and achieve a better performance.<\/jats:p>","DOI":"10.3390\/s18082545","type":"journal-article","created":{"date-parts":[[2018,8,3]],"date-time":"2018-08-03T11:03:26Z","timestamp":1533294206000},"page":"2545","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["A Novel Semi-Supervised Feature Extraction Method and Its Application in Automotive Assembly Fault Diagnosis Based on Vision Sensor Data"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2036-5926","authenticated-orcid":false,"given":"Xuan","family":"Zeng","sequence":"first","affiliation":[{"name":"State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin 30072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shi-Bin","family":"Yin","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin 30072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yin","family":"Guo","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin 30072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jia-Rui","family":"Lin","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin 30072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ji-Gui","family":"Zhu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin 30072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,8,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"733","DOI":"10.1007\/s00170-004-2232-2","article-title":"Periodic trend detection from CMM data based on the continuous wavelet transform","volume":"27","author":"Wang","year":"2006","journal-title":"Int. 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