{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T20:00:31Z","timestamp":1760385631264,"version":"build-2065373602"},"reference-count":54,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2018,6,3]],"date-time":"2018-06-03T00:00:00Z","timestamp":1527984000000},"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":["U1604158 , 61333005"],"award-info":[{"award-number":["U1604158 , 61333005"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Early detection of slowly varying small faults is an essential step for fault prognosis. In this paper, we first propose an average accumulative (AA) based time varying principal component analysis (PCA) model for early detection of slowly varying faults. The AA based method can increase the fault size as well as decrease the noise energy. Then, designated component analysis (DCA) is introduced for developing an AA-DCA method to diagnose the root cause of the fault, which is helpful for the operator to make maintenance decisions. Combining the advantage of the cumulative sum (CUSUM) based method and the AA based method, a CUSUM-AA based method is developed to detect faults at earlier times. Finally, the remaining useful life (RUL) prediction model with error correction is established by nonlinear fitting. Once online fault size defined by detection statistics is obtained by an early diagnosis algorithm, real-time RUL prediction can be directly estimated without extra recursive regression.<\/jats:p>","DOI":"10.3390\/s18061804","type":"journal-article","created":{"date-parts":[[2018,6,4]],"date-time":"2018-06-04T08:52:03Z","timestamp":1528102323000},"page":"1804","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Average Accumulative Based Time Variant Model for Early Diagnosis and Prognosis of Slowly Varying Faults"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3592-9664","authenticated-orcid":false,"given":"Funa","family":"Zhou","sequence":"first","affiliation":[{"name":"School of Computer and Information Engineering, Henan University, Kaifeng 475004, China"},{"name":"Department of Electrical Engineering, Yeungnam University, 280 Daehak-Ro, Kyongsan 38541, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0218-2333","authenticated-orcid":false,"given":"Ju H.","family":"Park","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Yeungnam University, 280 Daehak-Ro, Kyongsan 38541, Korea"}]},{"given":"Chenglin","family":"Wen","sequence":"additional","affiliation":[{"name":"School of Automatic, Hangzhou Dianzi University, Hangzhou 310018, China"}]},{"given":"Po","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Computer and Information Engineering, Henan University, Kaifeng 475004, China"}]}],"member":"1968","published-online":{"date-parts":[[2018,6,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1016\/j.automatica.2015.12.028","article-title":"Model-based fault detection, estimation, and prediction for a class of linear distributed parameter systems","volume":"66","author":"Cai","year":"2016","journal-title":"Automatica"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2152","DOI":"10.1016\/j.apenergy.2009.02.011","article-title":"Gas turbine performance prognostic for condition-based maintenance","volume":"86","author":"Li","year":"2009","journal-title":"Appl. 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