{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T16:26:53Z","timestamp":1759336013366,"version":"build-2065373602"},"reference-count":24,"publisher":"Wiley","issue":"7","license":[{"start":{"date-parts":[[2025,6,25]],"date-time":"2025-06-25T00:00:00Z","timestamp":1750809600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Quality &amp; Reliability Eng"],"published-print":{"date-parts":[[2025,11]]},"abstract":"<jats:title>ABSTRACT<\/jats:title><jats:p>In modern manufacturing, the quality of many products is characterized by a functional relationship, known as profile. Many scholars have developed process diagnosis methods for independent profile data. However, with the advancement of automation, a large amount of autocorrelated profile data has emerged in manufacturing. Methods established under the assumption of independence are not effective for diagnosing profile\u2010correlated data. Due to its complexity, which includes correlations within profile and between profiles, there have been no reports on process diagnosis for profile\u2010correlated data to date. This paper focuses on the case of autocorrelation within profile and proposes a diagnosis method suitable for such data via transforming problem to variable selection. Numerical simulations show that the proposed method has higher diagnostic capabilities and greater robustness than methods established under the assumption of independence. Finally, the article gives an example to illustrate the implementation process of the proposed\u00a0method.<\/jats:p>","DOI":"10.1002\/qre.70012","type":"journal-article","created":{"date-parts":[[2025,6,25]],"date-time":"2025-06-25T12:34:12Z","timestamp":1750854852000},"page":"3089-3108","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Diagnostic Approach of General Linear Profile in the Presence of Within Profile Autocorrelation"],"prefix":"10.1002","volume":"41","author":[{"given":"Dan","family":"Xiong","sequence":"first","affiliation":[{"name":"School of Mathematics and Statistics Guilin University of Technology Guilin China"}]},{"given":"Guangming","family":"Deng","sequence":"additional","affiliation":[{"name":"School of Mathematics and Statistics Guilin University of Technology Guilin China"},{"name":"Guangxi Colleges and Universities Key Laboratory of Applied Statistics Guilin China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3818-0468","authenticated-orcid":false,"given":"Feng","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Mathematics and Statistics Guilin University of Technology Guilin China"},{"name":"Guangxi Colleges and Universities Key Laboratory of Applied Statistics Guilin China"}]}],"member":"311","published-online":{"date-parts":[[2025,6,25]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1080\/0740817X.2011.649386"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1080\/00224065.2003.11980225"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2019.113105"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1198\/004017004000000455"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1002\/qre.788"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1198\/004017007000000164"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1080\/16843703.2021.1918439"},{"issue":"1","key":"e_1_2_9_9_1","first-page":"49","article-title":"Real\u2010Time Profile Monitoring Schemes Considering Covariates Using Gaussian Process via Sensor Data","volume":"50","author":"Ding N.","year":"2023","journal-title":"Quality Technology & Quantitative Management"},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.1080\/03610926.2021.1971246"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cie.2005.07.001"},{"key":"e_1_2_9_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cie.2009.04.005"},{"key":"e_1_2_9_13_1","doi-asserted-by":"publisher","DOI":"10.1002\/qre.1502"},{"key":"e_1_2_9_14_1","doi-asserted-by":"publisher","DOI":"10.1080\/03610920701653136"},{"key":"e_1_2_9_15_1","doi-asserted-by":"publisher","DOI":"10.1002\/qre.1762"},{"key":"e_1_2_9_16_1","doi-asserted-by":"publisher","DOI":"10.1590\/S0103-65132007000300002"},{"key":"e_1_2_9_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cie.2018.10.008"},{"key":"e_1_2_9_18_1","doi-asserted-by":"publisher","DOI":"10.1198\/TECH.2011.10034"},{"key":"e_1_2_9_19_1","doi-asserted-by":"crossref","unstructured":"F.Xu \u201cA Bayesian Approach to Diagnosing General Linear Profiles \u201d in2023 The 6th International Conference on Computers in Management and Business (ICCMB) (ICCMB 2023) (ACM New York NY USA 2023) https:\/\/doi.org\/10.1145\/3584816.3584833.","DOI":"10.1145\/3584816.3584833"},{"volume-title":"Introduction to Statistical Quality Control","year":"2020","author":"Montgomery D. 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