{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,30]],"date-time":"2025-12-30T15:40:36Z","timestamp":1767109236822,"version":"build-2065373602"},"reference-count":35,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2021,6,25]],"date-time":"2021-06-25T00:00:00Z","timestamp":1624579200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"SJ-NTU Corp Lab","award":["IAF-ICP I1801E0020"],"award-info":[{"award-number":["IAF-ICP I1801E0020"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The stable operation of air handling units (AHU) is critical to ensure high efficiency and to extend the lifetime of the heating, ventilation, and air conditioning (HVAC) systems of buildings. In this paper, an online data-driven diagnosis method for AHU in an HVAC system is proposed and elaborated. The rule-based method can roughly detect the sensor condition by setting threshold values according to prior experience. Then, an efficient feature selection method using 1D convolutional neural networks (CNNs) is proposed for fault diagnosis of AHU in HVAC systems according to the system\u2019s historical data obtained from the building management system. The new framework combines the rule-based method and CNNs-based method (RACNN) for sensor fault and complicated fault. The fault type of AHU can be accurately identified via the offline test results with an accuracy of 99.15% and fast online detection within 2 min. In the lab, the proposed RACNN method was validated on a real AHU system. The experimental results show that the proposed RACNN improves the performance of fault diagnosis.<\/jats:p>","DOI":"10.3390\/s21134358","type":"journal-article","created":{"date-parts":[[2021,6,25]],"date-time":"2021-06-25T11:07:40Z","timestamp":1624619260000},"page":"4358","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["An Online Data-Driven Fault Diagnosis Method for Air Handling Units by Rule and Convolutional Neural Networks"],"prefix":"10.3390","volume":"21","author":[{"given":"Huanyue","family":"Liao","sequence":"first","affiliation":[{"name":"SJ-NTU Corporate Lab, Nanyang Technological University, Singapore 637335, Singapore"}]},{"given":"Wenjian","family":"Cai","sequence":"additional","affiliation":[{"name":"School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore"}]},{"given":"Fanyong","family":"Cheng","sequence":"additional","affiliation":[{"name":"SJ-NTU Corporate Lab, Nanyang Technological University, Singapore 637335, Singapore"}]},{"given":"Swapnil","family":"Dubey","sequence":"additional","affiliation":[{"name":"Energy Research Institute @ NTU, Singapore 637141, Singapore"}]},{"given":"Pudupadi Balachander","family":"Rajesh","sequence":"additional","affiliation":[{"name":"Surbana Jurong Consultants Pte Ltd., Singapore 150168, Singapore"}]}],"member":"1968","published-online":{"date-parts":[[2021,6,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"220","DOI":"10.1016\/j.enbuild.2005.05.008","article-title":"HVAC system optimization for energy management by evolutionary programming","volume":"38","author":"Fong","year":"2006","journal-title":"Energy Build."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.enbuild.2003.12.007","article-title":"HVAC system optimization\u2014In-building section","volume":"37","author":"Lu","year":"2005","journal-title":"Energy Build."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.autcon.2009.11.019","article-title":"Overview of HVAC system simulation","volume":"19","author":"Hensen","year":"2010","journal-title":"Autom. 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