Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/160417
Title: A hybrid data-driven simultaneous fault diagnosis model for air handling units
Authors: Wu, Binjie
Cai, Wenjian
Chen, Haoran
Zhang, Xin
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2021
Source: Wu, B., Cai, W., Chen, H. & Zhang, X. (2021). A hybrid data-driven simultaneous fault diagnosis model for air handling units. Energy and Buildings, 245, 111069-. https://dx.doi.org/10.1016/j.enbuild.2021.111069
Project: IAF-ICP I1801E0020
Journal: Energy and Buildings
Abstract: Simultaneous faults are situations where two or more faults occur at the same time, which are difficult to be diagnosed by simple and stand-alone standard machine learning methods as a multi-label problem. Simultaneous faults for HVAC systems are not given enough attention under the challenges of insufficient sensors, coupled faults, and sophisticated mathematical models. A novel simultaneous fault diagnosis model based on a hybrid method of classifier chains integrated with random forest (CC-RF) is proposed in this study. On-site experiments involving six single fault cases and seven simultaneous fault cases for an air handling unit (AHU) system are conducted to verify this model. The results demonstrate a satisfactory performance with the test accuracy of 99.50% and F1 score of 99.66% for the fault diagnosis model. The model is proven to be neither underfitting nor overfitting and can be scalable with a reasonable training time. Through online analysis, the proposed method demonstrates a good competence of diagnosing not only single faults but also simultaneous fault. The CC-RF method has a better performance compared with classifier chains with logistic regression and support vector machine. Besides, the proposed method of classifier chains outperforms binary relevance due to the benefitting of label relevance.
URI: https://hdl.handle.net/10356/160417
ISSN: 0378-7788
DOI: 10.1016/j.enbuild.2021.111069
Schools: School of Electrical and Electronic Engineering 
Research Centres: SJ-NTU Corporate Lab
Rights: © 2021 Elsevier B.V. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:EEE Journal Articles

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