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dc.contributor.authorWu, Binjieen_US
dc.contributor.authorCai, Wenjianen_US
dc.contributor.authorChen, Haoranen_US
dc.contributor.authorZhang, Xinen_US
dc.identifier.citationWu, 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-.
dc.description.abstractSimultaneous 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.en_US
dc.relationIAF-ICP I1801E0020en_US
dc.relation.ispartofEnergy and Buildingsen_US
dc.rights© 2021 Elsevier B.V. All rights reserved.en_US
dc.subjectEngineering::Electrical and electronic engineeringen_US
dc.titleA hybrid data-driven simultaneous fault diagnosis model for air handling unitsen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.contributor.researchSJ-NTU Corporate Laben_US
dc.subject.keywordsEnergy Conservationen_US
dc.subject.keywordsClassifier Chainsen_US
dc.description.acknowledgementThe work is supported by SJ-NTU corporate lab (IAF-ICP I1801E0020) in Singapore.en_US
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