IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v279y2023ics0360544223014135.html
   My bibliography  Save this article

Novel data-pulling-based strategy for chiller fault diagnosis in data-scarce scenarios

Author

Listed:
  • Ren, Zhengxiong
  • Han, Hua
  • Cui, Xiaoyu
  • Lu, Hailong
  • Luo, Mingwen

Abstract

Data-driven fault detection and diagnosis (FDD) for chillers depends heavily on the quantity of effective fault data (labelled). Plenty of labelled data is needed for a robust FDD model. Under data-scarce scenario when just a few labelled data are available, the model is easily biased. This study proposes a novel data pulling (DP) strategy based on a semi-supervised method to deal with this problem. Different from the traditional data cleaning idea, DP is concentrated on finding correct data to use instead of trying to identify all the noise to delete. System-level faults and component-level faults are treated separately and differently according to their distinct characteristics. Validations on seven types of chiller faults show that with scarce data, overfitting is easy to occur, random forest (RF) needs not be optimized, and the quality of the supplemented data is normally more important than the quantity. DP strategy can pull out data of high-confidence level, 99.87% for component-level faults and 100.00% for system-level faults. With just 20 labelled data for each fault, compared with the supervised model and the semi-supervised models without DP, the accuracy of the proposed method is improved by 1.77%∼4.63%, and the performance for refrigerant leakage is especially promoted.

Suggested Citation

  • Ren, Zhengxiong & Han, Hua & Cui, Xiaoyu & Lu, Hailong & Luo, Mingwen, 2023. "Novel data-pulling-based strategy for chiller fault diagnosis in data-scarce scenarios," Energy, Elsevier, vol. 279(C).
    • Handle: RePEc:eee:energy:v:279:y:2023:i:c:s0360544223014135
    DOI: 10.1016/j.energy.2023.128019
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544223014135
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2023.128019?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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).
    2. Zhu, Xu & Zhang, Shuai & Jin, Xinqiao & Du, Zhimin, 2020. "Deep learning based reference model for operational risk evaluation of screw chillers for energy efficiency," Energy, Elsevier, vol. 213(C).
    3. Du, Zhimin & Liang, Xinbin & Chen, Siliang & Zhu, Xu & Chen, Kang & Jin, Xinqiao, 2023. "Knowledge-infused deep learning diagnosis model with self-assessment for smart management in HVAC systems," Energy, Elsevier, vol. 263(PD).
    4. Zhao, Yang & Li, Tingting & Zhang, Xuejun & Zhang, Chaobo, 2019. "Artificial intelligence-based fault detection and diagnosis methods for building energy systems: Advantages, challenges and the future," Renewable and Sustainable Energy Reviews, Elsevier, vol. 109(C), pages 85-101.
    5. Chaowen Zhong & Ke Yan & Yuting Dai & Ning Jin & Bing Lou, 2019. "Energy Efficiency Solutions for Buildings: Automated Fault Diagnosis of Air Handling Units Using Generative Adversarial Networks," Energies, MDPI, vol. 12(3), pages 1-11, February.
    6. Eom, Yong Hwan & Chung, Yoong & Park, Minsu & Hong, Sung Bin & Kim, Min Soo, 2021. "Deep learning-based prediction method on performance change of air source heat pump system under frosting conditions," Energy, Elsevier, vol. 228(C).
    7. Fan, Cheng & Lei, Yutian & Sun, Yongjun & Piscitelli, Marco Savino & Chiosa, Roberto & Capozzoli, Alfonso, 2022. "Data-centric or algorithm-centric: Exploiting the performance of transfer learning for improving building energy predictions in data-scarce context," Energy, Elsevier, vol. 240(C).
    8. Schreiber, Thomas & Netsch, Christoph & Eschweiler, Sören & Wang, Tianyuan & Storek, Thomas & Baranski, Marc & Müller, Dirk, 2021. "Application of data-driven methods for energy system modelling demonstrated on an adaptive cooling supply system," Energy, Elsevier, vol. 230(C).
    9. Jiang, Feifeng & Ma, Jun & Li, Zheng & Ding, Yuexiong, 2022. "Prediction of energy use intensity of urban buildings using the semi-supervised deep learning model," Energy, Elsevier, vol. 249(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Bi, Yubo & Wu, Qiulan & Wang, Shilu & Shi, Jihao & Cong, Haiyong & Ye, Lili & Gao, Wei & Bi, Mingshu, 2023. "Hydrogen leakage location prediction at hydrogen refueling stations based on deep learning," Energy, Elsevier, vol. 284(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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).
    2. Liang, Xinbin & Liu, Zhuoxuan & Wang, Jie & Jin, Xinqiao & Du, Zhimin, 2023. "Uncertainty quantification-based robust deep learning for building energy systems considering distribution shift problem," Applied Energy, Elsevier, vol. 337(C).
    3. Chen, Jianli & Zhang, Liang & Li, Yanfei & Shi, Yifu & Gao, Xinghua & Hu, Yuqing, 2022. "A review of computing-based automated fault detection and diagnosis of heating, ventilation and air conditioning systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    4. Li, Tingting & Zhou, Yangze & Zhao, Yang & Zhang, Chaobo & Zhang, Xuejun, 2022. "A hierarchical object oriented Bayesian network-based fault diagnosis method for building energy systems," Applied Energy, Elsevier, vol. 306(PB).
    5. Du, Zhimin & Liang, Xinbin & Chen, Siliang & Zhu, Xu & Chen, Kang & Jin, Xinqiao, 2023. "Knowledge-infused deep learning diagnosis model with self-assessment for smart management in HVAC systems," Energy, Elsevier, vol. 263(PD).
    6. Chen, Zhe & Xiao, Fu & Guo, Fangzhou, 2023. "Similarity learning-based fault detection and diagnosis in building HVAC systems with limited labeled data," Renewable and Sustainable Energy Reviews, Elsevier, vol. 185(C).
    7. 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).
    8. Du, Zhimin & Liang, Xinbin & Chen, Siliang & Li, Pengcheng & Zhu, Xu & Chen, Kang & Jin, Xinqiao, 2023. "Domain adaptation deep learning and its T-S diagnosis networks for the cross-control and cross-condition scenarios in data center HVAC systems," Energy, Elsevier, vol. 280(C).
    9. Li, Guannan & Li, Fan & Ahmad, Tanveer & Liu, Jiangyan & Li, Tao & Fang, Xi & Wu, Yubei, 2022. "Performance evaluation of sequence-to-sequence-Attention model for short-term multi-step ahead building energy predictions," Energy, Elsevier, vol. 259(C).
    10. Mario Pérez-Gomariz & Antonio López-Gómez & Fernando Cerdán-Cartagena, 2023. "Artificial Neural Networks as Artificial Intelligence Technique for Energy Saving in Refrigeration Systems—A Review," Clean Technol., MDPI, vol. 5(1), pages 1-21, January.
    11. Simon P. Melgaard & Kamilla H. Andersen & Anna Marszal-Pomianowska & Rasmus L. Jensen & Per K. Heiselberg, 2022. "Fault Detection and Diagnosis Encyclopedia for Building Systems: A Systematic Review," Energies, MDPI, vol. 15(12), pages 1-50, June.
    12. Mohammad Mahdi Forootan & Iman Larki & Rahim Zahedi & Abolfazl Ahmadi, 2022. "Machine Learning and Deep Learning in Energy Systems: A Review," Sustainability, MDPI, vol. 14(8), pages 1-49, April.
    13. Li, Guannan & Chen, Liang & Liu, Jiangyan & Fang, Xi, 2023. "Comparative study on deep transfer learning strategies for cross-system and cross-operation-condition building energy systems fault diagnosis," Energy, Elsevier, vol. 263(PD).
    14. Rongjiang Ma & Shen Yang & Xianlin Wang & Xi-Cheng Wang & Ming Shan & Nanyang Yu & Xudong Yang, 2020. "Systematic Method for the Energy-Saving Potential Calculation of Air-Conditioning Systems via Data Mining. Part I: Methodology," Energies, MDPI, vol. 14(1), pages 1-15, December.
    15. Antonio Rosato & Francesco Guarino & Mohammad El Youssef & Alfonso Capozzoli & Massimiliano Masullo & Luigi Maffei, 2022. "Faulty Operation of Coils’ and Humidifier Valves in a Typical Air-Handling Unit: Experimental Impact Assessment of Indoor Comfort and Patterns of Operating Parameters under Mediterranean Climatic Cond," Energies, MDPI, vol. 15(18), pages 1-38, September.
    16. Shariq, M. Hasan & Hughes, Ben Richard, 2020. "Revolutionising building inspection techniques to meet large-scale energy demands: A review of the state-of-the-art," Renewable and Sustainable Energy Reviews, Elsevier, vol. 130(C).
    17. Liang, Xinbin & Zhu, Xu & Chen, Siliang & Jin, Xinqiao & Xiao, Fu & Du, Zhimin, 2023. "Physics-constrained cooperative learning-based reference models for smart management of chillers considering extrapolation scenarios," Applied Energy, Elsevier, vol. 349(C).
    18. Ibrahim, Muhammad Sohail & Dong, Wei & Yang, Qiang, 2020. "Machine learning driven smart electric power systems: Current trends and new perspectives," Applied Energy, Elsevier, vol. 272(C).
    19. Ke Yan & Yuting Dai & Meiling Xu & Yuchang Mo, 2019. "Tunnel Surface Settlement Forecasting with Ensemble Learning," Sustainability, MDPI, vol. 12(1), pages 1-11, December.
    20. Abdellatif Elmouatamid & Brian Fricke & Jian Sun & Philip W. T. Pong, 2023. "Air Conditioning Systems Fault Detection and Diagnosis-Based Sensing and Data-Driven Approaches," Energies, MDPI, vol. 16(12), pages 1-20, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:279:y:2023:i:c:s0360544223014135. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.