A semi-supervised approach to fault detection and diagnosis for building HVAC systems based on the modified generative adversarial network
Developing efficient fault detection and diagnosis (FDD) techniques for building HVAC systems is important for improving buildings’ reliability and energy efficiency. The existing FDD methods can achieve satisfying results only if there are sufficient labeled training data. However, labelling the da...
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sg-ntu-dr.10356-1604162022-07-21T08:05:35Z A semi-supervised approach to fault detection and diagnosis for building HVAC systems based on the modified generative adversarial network Li, Bingxu Cheng, Fanyong Cai, Hui Zhang, Xin Cai, Wenjian School of Electrical and Electronic Engineering Interdisciplinary Graduate School (IGS) Energy Research Institute @ NTU (ERI@N) Engineering::Electrical and electronic engineering Imbalanced Learning Fault Detection and Diagnosis Developing efficient fault detection and diagnosis (FDD) techniques for building HVAC systems is important for improving buildings’ reliability and energy efficiency. The existing FDD methods can achieve satisfying results only if there are sufficient labeled training data. However, labelling the data is often costly and laborious, and most data collected in practice are unlabeled. Most of the existing FDD methods cannot leverage the unlabeled dataset which contains much information beneficial to fault classification, and this will impede the improvement of the FDD performance. To deal with this problem, a semi-supervised FDD approach is proposed for the building HVAC system based on the modified generative adversarial network (modified GAN). The binary discriminator in the original GAN is replaced with the multiclass classifier. After the modification, both the unlabeled and labeled datasets can be utilized simultaneously: the modified GAN can learn the data distribution information present in unlabeled samples and then combine this information with the limited number of labeled data to accomplish a supervised learning task. Additionally, a novel self-training scheme is proposed for the modified GAN to correct the class imbalance in both labeled and unlabeled data. With the self-training scheme, the modified GAN can still efficiently exploit the information contained in unlabeled data to enhance the FDD performance even if the class distribution is highly imbalanced. Experimental results demonstrate the effectiveness of the proposed modified GAN-based approach and the self-training scheme. 2022-07-21T08:05:35Z 2022-07-21T08:05:35Z 2021 Journal Article Li, B., Cheng, F., Cai, H., Zhang, X. & Cai, W. (2021). A semi-supervised approach to fault detection and diagnosis for building HVAC systems based on the modified generative adversarial network. Energy and Buildings, 246, 111044-. https://dx.doi.org/10.1016/j.enbuild.2021.111044 0378-7788 https://hdl.handle.net/10356/160416 10.1016/j.enbuild.2021.111044 2-s2.0-85107032871 246 111044 en Energy and Buildings © 2021 Elsevier B.V. All rights reserved. |
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Engineering::Electrical and electronic engineering Imbalanced Learning Fault Detection and Diagnosis Li, Bingxu Cheng, Fanyong Cai, Hui Zhang, Xin Cai, Wenjian A semi-supervised approach to fault detection and diagnosis for building HVAC systems based on the modified generative adversarial network |
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Developing efficient fault detection and diagnosis (FDD) techniques for building HVAC systems is important for improving buildings’ reliability and energy efficiency. The existing FDD methods can achieve satisfying results only if there are sufficient labeled training data. However, labelling the data is often costly and laborious, and most data collected in practice are unlabeled. Most of the existing FDD methods cannot leverage the unlabeled dataset which contains much information beneficial to fault classification, and this will impede the improvement of the FDD performance. To deal with this problem, a semi-supervised FDD approach is proposed for the building HVAC system based on the modified generative adversarial network (modified GAN). The binary discriminator in the original GAN is replaced with the multiclass classifier. After the modification, both the unlabeled and labeled datasets can be utilized simultaneously: the modified GAN can learn the data distribution information present in unlabeled samples and then combine this information with the limited number of labeled data to accomplish a supervised learning task. Additionally, a novel self-training scheme is proposed for the modified GAN to correct the class imbalance in both labeled and unlabeled data. With the self-training scheme, the modified GAN can still efficiently exploit the information contained in unlabeled data to enhance the FDD performance even if the class distribution is highly imbalanced. Experimental results demonstrate the effectiveness of the proposed modified GAN-based approach and the self-training scheme. |
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School of Electrical and Electronic Engineering |
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School of Electrical and Electronic Engineering Li, Bingxu Cheng, Fanyong Cai, Hui Zhang, Xin Cai, Wenjian |
format |
Article |
author |
Li, Bingxu Cheng, Fanyong Cai, Hui Zhang, Xin Cai, Wenjian |
author_sort |
Li, Bingxu |
title |
A semi-supervised approach to fault detection and diagnosis for building HVAC systems based on the modified generative adversarial network |
title_short |
A semi-supervised approach to fault detection and diagnosis for building HVAC systems based on the modified generative adversarial network |
title_full |
A semi-supervised approach to fault detection and diagnosis for building HVAC systems based on the modified generative adversarial network |
title_fullStr |
A semi-supervised approach to fault detection and diagnosis for building HVAC systems based on the modified generative adversarial network |
title_full_unstemmed |
A semi-supervised approach to fault detection and diagnosis for building HVAC systems based on the modified generative adversarial network |
title_sort |
semi-supervised approach to fault detection and diagnosis for building hvac systems based on the modified generative adversarial network |
publishDate |
2022 |
url |
https://hdl.handle.net/10356/160416 |
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1739837392434495488 |