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
Hlavní autori: Lu, Cheng, Ma, Xiang, Yan, Ke
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier Ltd 01.07.2024
Predmet:
Chiller
Data augmentation
Fault diagnosis
Variational autoencoder
Fault diagnosis
Variational autoencoder
Data augmentation
Chiller
ISSN:2352-7102, 2352-7102
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Popis
Shrnutí: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.
ISSN:2352-7102
2352-7102
DOI:10.1016/j.jobe.2024.109137