{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T00:52:41Z","timestamp":1760230361999,"version":"build-2065373602"},"reference-count":36,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2022,7,18]],"date-time":"2022-07-18T00:00:00Z","timestamp":1658102400000},"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":["51906181","2021-83","Q20181110"],"award-info":[{"award-number":["51906181","2021-83","Q20181110"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"The 2021 year Construction Technology Plan Project of Hubei Province","award":["51906181","2021-83","Q20181110"],"award-info":[{"award-number":["51906181","2021-83","Q20181110"]}]},{"name":"Excel-lent Young and Middle-aged Talent in Universities of Hubei Province","award":["51906181","2021-83","Q20181110"],"award-info":[{"award-number":["51906181","2021-83","Q20181110"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Sensor drift fault calibration is essential to maintain the operation of heating, ventilation and air conditioning systems (HVAC) in buildings. Bayesian inference (BI) is becoming more and more popular as a commonly used sensor fault calibration method. However, this method focused mainly on sensor bias fault, and it could be difficult to calibrate drift fault that changes with time. Therefore, a dynamic calibration method for sensor drift fault of HVAC systems based on BI is developed. Taking the drift fault calibration of the chilled water supply temperature sensor of the chiller as an example, the performance of the proposed dynamic calibration method is evaluated. Results show that the combination of the Exponentially Weighted Moving-Average (EWMA) method with high detection accuracy and the proposed BI dynamic calibration method can effectively improve the calibration accuracy of drift fault, and the Mean Absolute Percentage Error (MAPE) value between the calibrated and normal data is less than 5%.<\/jats:p>","DOI":"10.3390\/s22145348","type":"journal-article","created":{"date-parts":[[2022,7,19]],"date-time":"2022-07-19T00:19:21Z","timestamp":1658189961000},"page":"5348","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Dynamic Calibration Method of Sensor Drift Fault in HVAC System Based on Bayesian Inference"],"prefix":"10.3390","volume":"22","author":[{"given":"Guannan","family":"Li","sequence":"first","affiliation":[{"name":"School of Urban Construction, Wuhan University of Science and Technology, Wuhan 430065, China"}]},{"given":"Haonan","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Urban Construction, Wuhan University of Science and Technology, Wuhan 430065, China"}]},{"given":"Jiajia","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Urban Construction, Wuhan University of Science and Technology, Wuhan 430065, China"}]},{"given":"Xi","family":"Fang","sequence":"additional","affiliation":[{"name":"College of Civil Engineering, Hunan University, Changsha 410082, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.autcon.2013.12.006","article-title":"Development and alpha testing of a cloud based automated fault detection and diagnosis tool for Air Handling Units","volume":"39","author":"Bruton","year":"2014","journal-title":"Autom. 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