Integrating active learning and semi-supervised learning for improved data-driven HVAC fault diagnosis performance
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DOI: 10.1016/j.apenergy.2023.122356
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- Chen, Yimin & Wen, Jin & Pradhan, Ojas & Lo, L. James & Wu, Teresa, 2022. "Using discrete Bayesian networks for diagnosing and isolating cross-level faults in HVAC systems," Applied Energy, Elsevier, vol. 327(C).
- Guo, Yabin & Tan, Zehan & Chen, Huanxin & Li, Guannan & Wang, Jiangyu & Huang, Ronggeng & Liu, Jiangyan & Ahmad, Tanveer, 2018. "Deep learning-based fault diagnosis of variable refrigerant flow air-conditioning system for building energy saving," Applied Energy, Elsevier, vol. 225(C), pages 732-745.
- Wen, Shuqing & Zhang, Weirong & Sun, Yifu & Li, Zhenxi & Huang, Boju & Bian, Shouguo & Zhao, Lin & Wang, Yan, 2023. "An enhanced principal component analysis method with Savitzky–Golay filter and clustering algorithm for sensor fault detection and diagnosis," Applied Energy, Elsevier, vol. 337(C).
- Zhao, Yang & Wang, Shengwei & Xiao, Fu, 2013. "Pattern recognition-based chillers fault detection method using Support Vector Data Description (SVDD)," Applied Energy, Elsevier, vol. 112(C), pages 1041-1048.
- Fan, Cheng & Lei, Yutian & Sun, Yongjun & Mo, Like, 2023. "Novel transformer-based self-supervised learning methods for improved HVAC fault diagnosis performance with limited labeled data," Energy, Elsevier, vol. 278(PB).
- Li, Bingxu & Cheng, Fanyong & Zhang, Xin & Cui, Can & Cai, Wenjian, 2021. "A novel semi-supervised data-driven method for chiller fault diagnosis with unlabeled data," Applied Energy, Elsevier, vol. 285(C).
- Chen, Zhelun & O’Neill, Zheng & Wen, Jin & Pradhan, Ojas & Yang, Tao & Lu, Xing & Lin, Guanjing & Miyata, Shohei & Lee, Seungjae & Shen, Chou & Chiosa, Roberto & Piscitelli, Marco Savino & Capozzoli, , 2023. "A review of data-driven fault detection and diagnostics for building HVAC systems," Applied Energy, Elsevier, vol. 339(C).
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Cited by:
- Zhang, Jiahao & Peng, Ruo & Lu, Chenbei & Wu, Chenye, 2025. "Computationally efficient data synthesis for AC-OPF: Integrating Physics-Informed Neural Network solvers and active learning," Applied Energy, Elsevier, vol. 378(PA).
- Fan, Cheng & Chen, Ruikun & Mo, Jinhan & Liao, Longhui, 2024. "Personalized federated learning for cross-building energy knowledge sharing: Cost-effective strategies and model architectures," Applied Energy, Elsevier, vol. 362(C).
- Wenhao Lu & Wei Wang & Xuefei Qin & Zhiqiang Cai, 2024. "Enhancing Fault Diagnosis in Mechanical Systems with Graph Neural Networks Addressing Class Imbalance," Mathematics, MDPI, vol. 12(13), pages 1-22, July.
- Zhang, Jian & Zhang, Chaobo & Lu, Jie & Zhao, Yang, 2025. "Domain-specific large language models for fault diagnosis of heating, ventilation, and air conditioning systems by labeled-data-supervised fine-tuning," Applied Energy, Elsevier, vol. 377(PA).
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Keywords
Active learning; Semi-supervised learning; HVAC fault diagnosis; Data-driven model; Artificial intelligence;All these keywords.
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