Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/153931
Title: An online data-driven fault diagnosis method for air handling units by rule and convolutional neural networks
Authors: Liao, Huanyue
Cai, Wenjian
Cheng, Fanyong
Dubey, Swapnil
Rajesh, Pudupadi Balachander
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2021
Source: Liao, H., Cai, W., Cheng, F., Dubey, S. & Rajesh, P. B. (2021). An online data-driven fault diagnosis method for air handling units by rule and convolutional neural networks. Sensors, 21(13), 4358-. https://dx.doi.org/10.3390/s21134358
Journal: Sensors 
Abstract: 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's 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.
URI: https://hdl.handle.net/10356/153931
ISSN: 1424-8220
DOI: 10.3390/s21134358
Rights: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles
ERI@N Journal Articles

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