Chiller fault diagnosis based on improved variational autoencoder and co-training framework: A case study of insufficient samples
Implementing efficient automatic fault diagnosis is critical for saving energy and minimizing financial losses in the heating ventilation air-conditioning (HVAC) systems of commercial buildings. However, the limited quantity and weak features of fault samples acquired during HVAC operations hinder t...
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| Vydané v: | Journal of Building Engineering Ročník 88; s. 109137 |
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| Médium: | Journal Article |
| Jazyk: | English |
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Elsevier Ltd
01.07.2024
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| ISSN: | 2352-7102, 2352-7102 |
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| Abstract | Implementing efficient automatic fault diagnosis is critical for saving energy and minimizing financial losses in the heating ventilation air-conditioning (HVAC) systems of commercial buildings. However, the limited quantity and weak features of fault samples acquired during HVAC operations hinder the effectiveness of conventional machine learning-based fault diagnosis methodologies. This paper proposes a method based on an improved conditional variational autoencoder (MCVAE) and co-training (CT) ideology to address the issue of insufficient training samples. Initially, we employ MCVAE to synthesize an extensive dataset of chiller fault samples from the original training dataset. Subsequently, the beneficial samples for training our fault diagnostic classifier, namely high-quality samples, are selected from the generated dataset using the CT-based framework. Finally, the selected high-quality samples are merged into the original training dataset to train the ultimate fault classifiers. Experimental results demonstrate that our proposed method outperforms in effectiveness and efficiency compared to recently published methods. For instance, in the case of fault level 1 compared to the suboptimal model, our approach exhibits improvements of 2.41% when each type has 5 fault samples.
•The combination of MIXUP algorithm and conditional variational autoencoder are used to synthesize fault samples.•A high-quality sample selection framework is proposed based on Co-Training.•The proposed method improves both the effectiveness and efficiency of the original data augmentation framework for chiller FDD. |
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| AbstractList | Implementing efficient automatic fault diagnosis is critical for saving energy and minimizing financial losses in the heating ventilation air-conditioning (HVAC) systems of commercial buildings. However, the limited quantity and weak features of fault samples acquired during HVAC operations hinder the effectiveness of conventional machine learning-based fault diagnosis methodologies. This paper proposes a method based on an improved conditional variational autoencoder (MCVAE) and co-training (CT) ideology to address the issue of insufficient training samples. Initially, we employ MCVAE to synthesize an extensive dataset of chiller fault samples from the original training dataset. Subsequently, the beneficial samples for training our fault diagnostic classifier, namely high-quality samples, are selected from the generated dataset using the CT-based framework. Finally, the selected high-quality samples are merged into the original training dataset to train the ultimate fault classifiers. Experimental results demonstrate that our proposed method outperforms in effectiveness and efficiency compared to recently published methods. For instance, in the case of fault level 1 compared to the suboptimal model, our approach exhibits improvements of 2.41% when each type has 5 fault samples.
•The combination of MIXUP algorithm and conditional variational autoencoder are used to synthesize fault samples.•A high-quality sample selection framework is proposed based on Co-Training.•The proposed method improves both the effectiveness and efficiency of the original data augmentation framework for chiller FDD. |
| ArticleNumber | 109137 |
| Author | Lu, Cheng Ma, Xiang Yan, Ke |
| Author_xml | – sequence: 1 givenname: Cheng surname: Lu fullname: Lu, Cheng organization: College of Information Engineering, China Jiliang University, Hangzhou, 310018, China – sequence: 2 givenname: Xiang orcidid: 0000-0003-4269-7603 surname: Ma fullname: Ma, Xiang email: maxiang@cjlu.edu.cn organization: College of Information Engineering, China Jiliang University, Hangzhou, 310018, China – sequence: 3 givenname: Ke surname: Yan fullname: Yan, Ke email: keddiyan@gmail.com organization: Mechanical and Electrical Engineering, Hunan University, Changsha, 410006, China |
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| Cites_doi | 10.1016/j.enbuild.2022.112207 10.1016/j.enbenv.2019.11.003 10.1016/j.ijrefrig.2006.12.012 10.1016/j.apenergy.2023.121030 10.1016/j.dcan.2022.03.023 10.1016/j.engappai.2023.106316 10.3390/su13126828 10.1016/j.buildenv.2018.01.029 10.1016/j.asoc.2020.106333 10.1007/s10462-023-10579-0 10.1016/j.enbuild.2022.112241 10.1016/j.autcon.2019.04.002 10.1016/j.buildenv.2022.108821 10.1016/j.ijrefrig.2019.03.008 10.1007/s10845-019-01522-8 10.1016/j.rser.2022.112395 10.1016/j.buildenv.2021.107982 10.1016/j.enbuild.2013.12.038 10.1016/j.enbuild.2021.111467 10.1109/TASE.2020.3035620 10.1109/TASE.2006.888053 10.1080/10789669.2005.10391123 10.1016/j.arcontrol.2004.12.002 10.3390/en12030527 10.1016/j.measurement.2022.111045 10.1002/er.1530 |
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| Keywords | Fault diagnosis Variational autoencoder Data augmentation Chiller |
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| SubjectTerms | Chiller Data augmentation Fault diagnosis Variational autoencoder |
| Title | Chiller fault diagnosis based on improved variational autoencoder and co-training framework: A case study of insufficient samples |
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