{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,12]],"date-time":"2025-11-12T03:19:36Z","timestamp":1762917576625,"version":"build-2065373602"},"reference-count":17,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2015,2,16]],"date-time":"2015-02-16T00:00:00Z","timestamp":1424044800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>We propose a method for online sensor fault detection that is based on the evolving Strong Tracking Filter (STCKF). The cubature rule is used to estimate states to improve the accuracy of making estimates in a nonlinear case. A residual is the difference in value between an estimated value and the true value. A residual will be regarded as a signal that includes fault information. The threshold is set at a reasonable level, and will be compared with residuals to determine whether or not the sensor is faulty. The proposed method requires only a nominal plant model and uses STCKF to estimate the original state vector. The effectiveness of the algorithm is verified by simulation on a drum-boiler model.<\/jats:p>","DOI":"10.3390\/s150204578","type":"journal-article","created":{"date-parts":[[2015,2,16]],"date-time":"2015-02-16T10:30:38Z","timestamp":1424082638000},"page":"4578-4591","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Online Sensor Fault Detection Based on an Improved Strong Tracking Filter"],"prefix":"10.3390","volume":"15","author":[{"given":"Lijuan","family":"Wang","sequence":"first","affiliation":[{"name":"College of Information Engineering, Capital Normal University, Beijing 100048, China"},{"name":"Beijing Engineering Research Center of High Reliable Embedded System, Capital Normal University, Beijing 100048, China"},{"name":"Beijing Key Laboratory of Electronic System Reliable Technology, Capital Normal University, Beijing 100048, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lifeng","family":"Wu","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Capital Normal University, Beijing 100048, China"},{"name":"Beijing Engineering Research Center of High Reliable Embedded System, Capital Normal University, Beijing 100048, China"},{"name":"Beijing Key Laboratory of Electronic System Reliable Technology, Capital Normal University, Beijing 100048, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yong","family":"Guan","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Capital Normal University, Beijing 100048, China"},{"name":"Beijing Engineering Research Center of High Reliable Embedded System, Capital Normal University, Beijing 100048, China"},{"name":"Beijing Key Laboratory of Electronic System Reliable Technology, Capital Normal University, Beijing 100048, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guohui","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Capital Normal University, Beijing 100048, China"},{"name":"Beijing Engineering Research Center of High Reliable Embedded System, Capital Normal University, Beijing 100048, China"},{"name":"Beijing Key Laboratory of Electronic System Reliable Technology, Capital Normal University, Beijing 100048, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2015,2,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"304","DOI":"10.1016\/j.ins.2013.06.045","article-title":"Residual-Based Fault Detection Using Soft Computing Techniques for Condition Monitoring at Rolling Mills","volume":"259","author":"Serdio","year":"2014","journal-title":"Inf. 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Process Control"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1109\/TASE.2012.2226155","article-title":"Building energy doctors: An SPC and Kalman filter-based method for system-level fault detection in HVAC systems","volume":"11","author":"Sun","year":"2014","journal-title":"Trans. Autom. Sci. Eng."},{"key":"ref_6","unstructured":"Rjaraman, S. (2006). Robust Model-Based Fault Diagnosis for Chemical Process Systems. [PhD Thesis, Texas A&M University]."},{"key":"ref_7","first-page":"1","article-title":"Model-Based Water Wall Fault Detection and Diagnosis of FBC Boiler Using Strong Tracking Filter","volume":"2014","author":"Sun","year":"2014","journal-title":"Math. Probl. Eng."},{"key":"ref_8","unstructured":"Wan, E.A., and van der Merwe, R. The unscented Kalman filter for nonlinear estimation. Lake Louise, AB, Canada."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1254","DOI":"10.1109\/TAC.2009.2019800","article-title":"Cubature Kalman filters","volume":"54","author":"Arasaratnam","year":"2009","journal-title":"IEEE Trans. Autom. Control"},{"key":"ref_10","first-page":"1336","article-title":"Sensor fault diagnosis and fault-tolerant control method of underwater vehicles","volume":"24","author":"Zhu","year":"2009","journal-title":"Control Decis."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1007\/s12530-014-9108-y","article-title":"Self-Adaptive and Local Strategies for a Smooth Treatment of Drifts in Data Streams","volume":"5","author":"Shaker","year":"2014","journal-title":"Evol. Syst."},{"key":"ref_12","first-page":"1","article-title":"Suboptimal fading extended Kalman filtering for nonlinear systems","volume":"5","author":"Zhou","year":"1990","journal-title":"Control Decis."},{"key":"ref_13","first-page":"1063","article-title":"Strong tracking filter based on unscented transformation","volume":"25","author":"Wang","year":"2010","journal-title":"Control Decis."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"272","DOI":"10.1016\/j.inffus.2014.03.006","article-title":"Fault Detection in Multi-Sensor Networks based on Multivariate Time-Series Models and Orthogonal Transformations","volume":"20","author":"Serdio","year":"2014","journal-title":"Inf. Fusion"},{"key":"ref_15","first-page":"124","article-title":"Improved residual \u03c72 inspection in integrated navigation fault detection of application","volume":"35","author":"Ruan","year":"2012","journal-title":"Electron. Meas. Technol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1016\/j.measurement.2013.11.015","article-title":"Robust sensor fault detection based on nonlinear unknown input observer","volume":"48","author":"Zarei","year":"2014","journal-title":"Measurement"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"363","DOI":"10.1016\/S0005-1098(99)00171-5","article-title":"Drum-boiler dynamics","volume":"36","author":"Bell","year":"2000","journal-title":"Automatica"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/15\/2\/4578\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T20:42:45Z","timestamp":1760215365000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/15\/2\/4578"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015,2,16]]},"references-count":17,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2015,2]]}},"alternative-id":["s150204578"],"URL":"https:\/\/doi.org\/10.3390\/s150204578","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2015,2,16]]}}}