{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T02:47:56Z","timestamp":1767926876016,"version":"3.49.0"},"reference-count":30,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2021,11,4]],"date-time":"2021-11-04T00:00:00Z","timestamp":1635984000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Nature Science Foundation of Zhejiang Province","award":["LGG20F030005"],"award-info":[{"award-number":["LGG20F030005"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The reliability and safety of the cascade system, which is widely applied, have attached attention increasingly. Fault detection and diagnosis can play a significant role in enhancing its reliability and safety. On account of the complexity of the double closed-loop system in operation, the problem of fault diagnosis is relatively complex. For the single fault of the second-order valued system sensors, a real-time fault diagnosis method based on data-driven is proposed in this study. Off-line data is employed to establish static fault detection, location, estimation, and separation models. The static models are calibrated with on-line data to obtain the real-time fault diagnosis models. The real-time calibration, working flow and anti-interference measures of the real-time diagnosis system are given. Experiments results demonstrate the validity and accuracy of the fault diagnosis method, which is suitable for the general cascade system.<\/jats:p>","DOI":"10.3390\/s21217340","type":"journal-article","created":{"date-parts":[[2021,11,4]],"date-time":"2021-11-04T22:25:54Z","timestamp":1636064754000},"page":"7340","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Single Fault Diagnosis Method of Sensors in Cascade System Based on Data-Driven"],"prefix":"10.3390","volume":"21","author":[{"given":"Wenbo","family":"Na","sequence":"first","affiliation":[{"name":"College of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Siyu","family":"Guo","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanfeng","family":"Gao","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianxing","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junjie","family":"Huang","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"637","DOI":"10.1016\/0005-1098(71)90028-8","article-title":"An innovations approach to fault detection and diagnosis in dynamic systems","volume":"7","author":"Mehra","year":"1971","journal-title":"Automatic"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"459","DOI":"10.1016\/0005-1098(90)90018-D","article-title":"Fault diagnosis in dynamic systems using analytical and knowledge-based redundancya survey and some new results","volume":"26","author":"Frank","year":"1990","journal-title":"Automatic"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.neucom.2018.04.037","article-title":"A survey on model-based fault diagnosis for linear discrete time-varying systems","volume":"306","author":"Zhong","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"639","DOI":"10.1016\/S0967-0661(97)00046-4","article-title":"Supervision, fault-detection and fault-diagnosis methods\u2013An introduction","volume":"5","author":"Isermann","year":"1997","journal-title":"Control Eng. 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