IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v225y2018icp732-745.html
   My bibliography  Save this article

Deep learning-based fault diagnosis of variable refrigerant flow air-conditioning system for building energy saving

Author

Listed:
  • Guo, Yabin
  • Tan, Zehan
  • Chen, Huanxin
  • Li, Guannan
  • Wang, Jiangyu
  • Huang, Ronggeng
  • Liu, Jiangyan
  • Ahmad, Tanveer

Abstract

The fault diagnosis of air-conditioning systems is of great significance to the energy saving of buildings. This study proposes a novel fault diagnosis approach for building energy saving based on the deep learning method which is deep belief network, and its application potential in the air conditioning fault diagnosis field is investigated. Then, a parameter optimization selection strategy is developed for model optimization. Four kinds of faults of the variable flow refrigerant system under heating mode are used to evaluate the performance of the models. The fault diagnosis results show that the deep belief network model with initial parameters can be used to diagnose the faults of the variable flow refrigerant system. Through the parameter optimization selection strategy, the fault diagnosis correct rate of the optimized model is 97.7%, which is improved by 5.05% compared with the model with initial parameters. The number of hidden layers of the deep belief network model is selected to be 2 layers. This result indicates that the fault diagnosis for variable flow refrigerant systems may not require a very deep model. Additionally, the performance of the optimized deep belief network model is compared with that of the traditional back propagation neural network, and the former is better. This finding also shows that the unsupervised restricted Boltzmann machine layer for data feature reconstruction can improve the fault diagnosis performance.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:appene:v:225:y:2018:i:c:p:732-745
    DOI: 10.1016/j.apenergy.2018.05.075
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261918307943
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2018.05.075?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. Fan, Cheng & Xiao, Fu & Zhao, Yang, 2017. "A short-term building cooling load prediction method using deep learning algorithms," Applied Energy, Elsevier, vol. 195(C), pages 222-233.
    2. 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.
    3. Wang, H.Z. & Wang, G.B. & Li, G.Q. & Peng, J.C. & Liu, Y.T., 2016. "Deep belief network based deterministic and probabilistic wind speed forecasting approach," Applied Energy, Elsevier, vol. 182(C), pages 80-93.
    4. Wang, Shengwei & Cui, Jingtan, 2005. "Sensor-fault detection, diagnosis and estimation for centrifugal chiller systems using principal-component analysis method," Applied Energy, Elsevier, vol. 82(3), pages 197-213, November.
    5. Wang, Huai-zhi & Li, Gang-qiang & Wang, Gui-bin & Peng, Jian-chun & Jiang, Hui & Liu, Yi-tao, 2017. "Deep learning based ensemble approach for probabilistic wind power forecasting," Applied Energy, Elsevier, vol. 188(C), pages 56-70.
    6. Coelho, Igor M. & Coelho, Vitor N. & Luz, Eduardo J. da S. & Ochi, Luiz S. & Guimarães, Frederico G. & Rios, Eyder, 2017. "A GPU deep learning metaheuristic based model for time series forecasting," Applied Energy, Elsevier, vol. 201(C), pages 412-418.
    7. Du, Zhimin & Jin, Xinqiao & Yang, Yunyu, 2009. "Fault diagnosis for temperature, flow rate and pressure sensors in VAV systems using wavelet neural network," Applied Energy, Elsevier, vol. 86(9), pages 1624-1631, September.
    8. Yang, Liu & Yan, Haiyan & Lam, Joseph C., 2014. "Thermal comfort and building energy consumption implications – A review," Applied Energy, Elsevier, vol. 115(C), pages 164-173.
    9. Yu, Xinqiao & Yan, Da & Sun, Kaiyu & Hong, Tianzhen & Zhu, Dandan, 2016. "Comparative study of the cooling energy performance of variable refrigerant flow systems and variable air volume systems in office buildings," Applied Energy, Elsevier, vol. 183(C), pages 725-736.
    10. Tóth, Pál & Garami, Attila & Csordás, Bernadett, 2017. "Image-based deep neural network prediction of the heat output of a step-grate biomass boiler," Applied Energy, Elsevier, vol. 200(C), pages 155-169.
    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. 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. 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).
    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. Eom, Yong Hwan & Yoo, Jin Woo & Hong, Sung Bin & Kim, Min Soo, 2019. "Refrigerant charge fault detection method of air source heat pump system using convolutional neural network for energy saving," Energy, Elsevier, vol. 187(C).
    5. Guo, Yabin & Li, Yuduo & Li, Weilin, 2023. "On-site fault experiment and diagnosis research of the carbon dioxide transcritical heat pump system for energy saving," Energy, Elsevier, vol. 274(C).
    6. 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.
    7. 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.
    8. 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).
    9. Jie Yang & Zhimeng Dong & Huihan Yang & Yanyan Liu & Yunjie Wang & Fujiang Chen & Haifei Chen, 2022. "Numerical and Experimental Study on Thermal Comfort of Human Body by Split-Fiber Air Conditioner," Energies, MDPI, vol. 15(10), pages 1-24, May.
    10. Zhang, Liang & Leach, Matt & Chen, Jianli & Hu, Yuqing, 2023. "Sensor cost-effectiveness analysis for data-driven fault detection and diagnostics in commercial buildings," Energy, Elsevier, vol. 263(PB).
    11. Hwang, Jun Kwon & Yun, Geun Young & Lee, Sukho & Seo, Hyeongjoon & Santamouris, Mat, 2020. "Using deep learning approaches with variable selection process to predict the energy performance of a heating and cooling system," Renewable Energy, Elsevier, vol. 149(C), pages 1227-1245.
    12. Ahmad, Tanveer & Chen, Huanxin, 2019. "Deep learning for multi-scale smart energy forecasting," Energy, Elsevier, vol. 175(C), pages 98-112.
    13. Jun Kwon Hwang & Patrick Nzivugira Duhirwe & Geun Young Yun & Sukho Lee & Hyeongjoon Seo & Inhan Kim & Mat Santamouris, 2020. "A Novel Hybrid Deep Neural Network Model to Predict the Refrigerant Charge Amount of Heat Pumps," Sustainability, MDPI, vol. 12(7), pages 1-23, April.
    14. Tien, Paige Wenbin & Wei, Shuangyu & Liu, Tianshu & Calautit, John & Darkwa, Jo & Wood, Christopher, 2021. "A deep learning approach towards the detection and recognition of opening of windows for effective management of building ventilation heat losses and reducing space heating demand," Renewable Energy, Elsevier, vol. 177(C), pages 603-625.
    15. Dong, Zhe & Liu, Miao & Guo, Zhiwu & Huang, Xiaojin & Zhang, Yajun & Zhang, Zuoyi, 2019. "Adaptive state-observer for monitoring flexible nuclear reactors," Energy, Elsevier, vol. 171(C), pages 893-909.
    16. Wang, Jingfan & Tchapmi, Lyne P. & Ravikumar, Arvind P. & McGuire, Mike & Bell, Clay S. & Zimmerle, Daniel & Savarese, Silvio & Brandt, Adam R., 2020. "Machine vision for natural gas methane emissions detection using an infrared camera," Applied Energy, Elsevier, vol. 257(C).
    17. Qunli Wu & Hongjie Zhang, 2019. "A Novel Expertise-Guided Machine Learning Model for Internal Fault State Diagnosis of Power Transformers," Sustainability, MDPI, vol. 11(6), pages 1-19, March.
    18. Ching-Jui Tien & Chung-Yuen Yang & Ming-Tang Tsai & Hong-Jey Gow, 2022. "Development of Fault Diagnosing System for Ice-Storage Air-Conditioning Systems," Energies, MDPI, vol. 15(11), pages 1-13, May.
    19. 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.
    20. Huang, Tian-en & Guo, Qinglai & Sun, Hongbin & Tan, Chin-Woo & Hu, Tianyu, 2019. "A deep spatial-temporal data-driven approach considering microclimates for power system security assessment," Applied Energy, Elsevier, vol. 237(C), pages 36-48.
    21. Ahmed Gassar, Abdo Abdullah & Yun, Geun Young & Kim, Sumin, 2019. "Data-driven approach to prediction of residential energy consumption at urban scales in London," Energy, Elsevier, vol. 187(C).
    22. Fei Mei & Yong Ren & Qingliang Wu & Chenyu Zhang & Yi Pan & Haoyuan Sha & Jianyong Zheng, 2018. "Online Recognition Method for Voltage Sags Based on a Deep Belief Network," Energies, MDPI, vol. 12(1), pages 1-16, December.
    23. Guangxun E & He Gao & Youfu Lu & Xuehan Zheng & Xiaoying Ding & Yuanhao Yang, 2023. "A Novel Attention Temporal Convolutional Network for Transmission Line Fault Diagnosis via Comprehensive Feature Extraction," Energies, MDPI, vol. 16(20), pages 1-21, October.

    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. Suryanarayana, Gowri & Lago, Jesus & Geysen, Davy & Aleksiejuk, Piotr & Johansson, Christian, 2018. "Thermal load forecasting in district heating networks using deep learning and advanced feature selection methods," Energy, Elsevier, vol. 157(C), pages 141-149.
    2. Lago, Jesus & De Ridder, Fjo & De Schutter, Bart, 2018. "Forecasting spot electricity prices: Deep learning approaches and empirical comparison of traditional algorithms," Applied Energy, Elsevier, vol. 221(C), pages 386-405.
    3. Wang, Yun & Zou, Runmin & Liu, Fang & Zhang, Lingjun & Liu, Qianyi, 2021. "A review of wind speed and wind power forecasting with deep neural networks," Applied Energy, Elsevier, vol. 304(C).
    4. Zhang, Shuai & Chen, Yong & Xiao, Jiuhong & Zhang, Wenyu & Feng, Ruijun, 2021. "Hybrid wind speed forecasting model based on multivariate data secondary decomposition approach and deep learning algorithm with attention mechanism," Renewable Energy, Elsevier, vol. 174(C), pages 688-704.
    5. 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).
    6. Danxiang Wei & Jianzhou Wang & Kailai Ni & Guangyu Tang, 2019. "Research and Application of a Novel Hybrid Model Based on a Deep Neural Network Combined with Fuzzy Time Series for Energy Forecasting," Energies, MDPI, vol. 12(18), pages 1-38, September.
    7. 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).
    8. Wang, Zhanwei & Wang, Zhiwei & He, Suowei & Gu, Xiaowei & Yan, Zeng Feng, 2017. "Fault detection and diagnosis of chillers using Bayesian network merged distance rejection and multi-source non-sensor information," Applied Energy, Elsevier, vol. 188(C), pages 200-214.
    9. Huang, Tian-en & Guo, Qinglai & Sun, Hongbin & Tan, Chin-Woo & Hu, Tianyu, 2019. "A deep spatial-temporal data-driven approach considering microclimates for power system security assessment," Applied Energy, Elsevier, vol. 237(C), pages 36-48.
    10. 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.
    11. Munir Husein & Il-Yop Chung, 2019. "Day-Ahead Solar Irradiance Forecasting for Microgrids Using a Long Short-Term Memory Recurrent Neural Network: A Deep Learning Approach," Energies, MDPI, vol. 12(10), pages 1-21, May.
    12. 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.
    13. Rongjiang Ma & Xianlin Wang & Ming Shan & Nanyang Yu & Shen Yang, 2020. "Recognition of Variable-Speed Equipment in an Air-Conditioning System Using Numerical Analysis of Energy-Consumption Data," Energies, MDPI, vol. 13(18), pages 1-14, September.
    14. Wang, Kejun & Qi, Xiaoxia & Liu, Hongda & Song, Jiakang, 2018. "Deep belief network based k-means cluster approach for short-term wind power forecasting," Energy, Elsevier, vol. 165(PA), pages 840-852.
    15. Wang, Jujie & Li, Yaning, 2018. "Multi-step ahead wind speed prediction based on optimal feature extraction, long short term memory neural network and error correction strategy," Applied Energy, Elsevier, vol. 230(C), pages 429-443.
    16. Chen, Qun & Wang, Yi-Fei & Xu, Yun-Chao, 2015. "A thermal resistance-based method for the optimal design of central variable water/air volume chiller systems," Applied Energy, Elsevier, vol. 139(C), pages 119-130.
    17. Amasyali, Kadir & El-Gohary, Nora M., 2021. "Real data-driven occupant-behavior optimization for reduced energy consumption and improved comfort," Applied Energy, Elsevier, vol. 302(C).
    18. Yuansheng Huang & Shijian Liu & Lei Yang, 2018. "Wind Speed Forecasting Method Using EEMD and the Combination Forecasting Method Based on GPR and LSTM," Sustainability, MDPI, vol. 10(10), pages 1-15, October.
    19. Huaizhi Wang & Xian Zhang & Qing Li & Guibin Wang & Hui Jiang & Jianchun Peng, 2018. "Recursive Method for Distribution System Reliability Evaluation," Energies, MDPI, vol. 11(10), pages 1-15, October.
    20. Wang, Jingfan & Tchapmi, Lyne P. & Ravikumar, Arvind P. & McGuire, Mike & Bell, Clay S. & Zimmerle, Daniel & Savarese, Silvio & Brandt, Adam R., 2020. "Machine vision for natural gas methane emissions detection using an infrared camera," Applied Energy, Elsevier, vol. 257(C).

    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:appene:v:225:y:2018:i:c:p:732-745. 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.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.