IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i13p3877-d583601.html
   My bibliography  Save this article

Fault Detection Algorithm for Multiple-Simultaneous Refrigerant Charge and Secondary Fluid Flow Rate Faults in Heat Pumps

Author

Listed:
  • Samuel Boahen

    (Department of Mechanical Engineering, Kwame Nkrumah University of Science and Technology, Kumasi AK-039-5028, Ghana)

  • Kwesi Mensah

    (Graduate School of Mechanical Engineering, Hanbat National University, Daejeon 34158, Korea)

  • Selorm Kwaku Anka

    (Graduate School of Mechanical Engineering, Hanbat National University, Daejeon 34158, Korea)

  • Kwang Ho Lee

    (Department of Architecture, Korea University, Seoul 02741, Korea)

  • Jong Min Choi

    (Department of Mechanical Engineering, Hanbat National University, Daejeon 34158, Korea)

Abstract

The detection and diagnosis of faults is becoming necessary in ensuring energy savings in heat pump units. Faults can exist independently or simultaneously in heat pumps at the refrigerant side and secondary fluid flow loops. In this work, we discuss the effects that simultaneous refrigerant charge faults and faults associated with the flow rate of secondary fluids have on the performance of a heat pump operating in summer season and we developed a correlation to detect and diagnose these faults using multiple linear regression. The faults considered include simultaneous refrigerant charge and indoor heat exchanger secondary fluid flow rate faults (IFRFs), simultaneous refrigerant charge and outdoor heat exchanger secondary fluid flow rate faults (OFRFs) and simultaneous refrigerant charge, IFRF and OFRF. The occurrence of simultaneous refrigerant charge fault, IFRF and OFRF caused up to a 5.7% and 8% decrease in cooling capacity compared to simultaneous refrigerant charge and indoor heat exchanger secondary fluid flow rate faults, and simultaneous refrigerant charge and outdoor heat exchanger secondary fluid flow rate faults, respectively. Simultaneous refrigerant charge fault, IFRF and OFRF resulted in up to an 11.6% and 5.9% decrease in COP of the heat pump unit compared to simultaneous refrigerant charge fault and IFRF, and simultaneous refrigerant charge fault and OFRF, respectively. The developed FDD correlations accurately predicted the simultaneous refrigerant charge and faults in the flow rate of the secondary fluid within an error margin of 7.7%.

Suggested Citation

  • Samuel Boahen & Kwesi Mensah & Selorm Kwaku Anka & Kwang Ho Lee & Jong Min Choi, 2021. "Fault Detection Algorithm for Multiple-Simultaneous Refrigerant Charge and Secondary Fluid Flow Rate Faults in Heat Pumps," Energies, MDPI, vol. 14(13), pages 1-19, June.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:13:p:3877-:d:583601
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/13/3877/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/13/3877/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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).
    2. Samuel Boahen & Kwang Ho Lee & Jong Min Choi, 2019. "Refrigerant Charge Fault Detection and Diagnosis Algorithm for Water-to-Water Heat Pump Unit," Energies, MDPI, vol. 12(3), pages 1-25, February.
    3. Samuel Boahen & Kwesi Mensah & Yujin Nam & Jong Min Choi, 2020. "Fault Detection Methodology for Secondary Fluid Flow Rate in a Heat Pump Unit," Energies, MDPI, vol. 13(11), pages 1-17, June.
    4. Ze Zhang & Xiaojun Dong & Zheng Ren & Tianwei Lai & Yu Hou, 2017. "Influence of Refrigerant Charge Amount and EEV Opening on the Performance of a Transcritical CO 2 Heat Pump Water Heater," Energies, MDPI, vol. 10(10), pages 1-14, October.
    5. Antonio Gálvez & Alberto Diez-Olivan & Dammika Seneviratne & Diego Galar, 2021. "Fault Detection and RUL Estimation for Railway HVAC Systems Using a Hybrid Model-Based Approach," Sustainability, MDPI, vol. 13(12), pages 1-18, June.
    Full references (including those not matched with items on IDEAS)

    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. 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.
    2. Bode, Gerrit & Thul, Simon & Baranski, Marc & Müller, Dirk, 2020. "Real-world application of machine-learning-based fault detection trained with experimental data," Energy, Elsevier, vol. 198(C).
    3. Kenneth R. Uren & George van Schoor & Martin van Eldik & Johannes J. A. de Bruin, 2020. "An Energy Graph-Based Approach to Fault Diagnosis of a Transcritical CO 2 Heat Pump," Energies, MDPI, vol. 13(7), pages 1-34, April.
    4. Samuel Boahen & Kwesi Mensah & Yujin Nam & Jong Min Choi, 2020. "Fault Detection Methodology for Secondary Fluid Flow Rate in a Heat Pump Unit," Energies, MDPI, vol. 13(11), pages 1-17, June.
    5. Zhong, Fangliang & Calautit, John Kaiser & Wu, Yupeng, 2022. "Assessment of HVAC system operational fault impacts and multiple faults interactions under climate change," Energy, Elsevier, vol. 258(C).
    6. Ifaei, Pouya & Tayerani Charmchi, Amir Saman & Loy-Benitez, Jorge & Yang, Rebecca Jing & Yoo, ChangKyoo, 2022. "A data-driven analytical roadmap to a sustainable 2030 in South Korea based on optimal renewable microgrids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    7. Wang, Zhi & Peng, Xianyong & Zhou, Huaichun & Cao, Shengxian & Huang, Wenbo & Yan, Weijie & Li, Kuangyu & Fan, Siyuan, 2024. "A dynamic modeling method using channel-selection convolutional neural network: A case study of NOx emission," Energy, Elsevier, vol. 290(C).
    8. 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).
    9. Ignacio López Paniagua & Ángel Jiménez Álvaro & Javier Rodríguez Martín & Celina González Fernández & Rafael Nieto Carlier, 2019. "Comparison of Transcritical CO 2 and Conventional Refrigerant Heat Pump Water Heaters for Domestic Applications," Energies, MDPI, vol. 12(3), pages 1-17, February.
    10. Samuel Boahen & Kwang Ho Lee & Jong Min Choi, 2019. "Refrigerant Charge Fault Detection and Diagnosis Algorithm for Water-to-Water Heat Pump Unit," Energies, MDPI, vol. 12(3), pages 1-25, February.
    11. Antonio Rosato & Marco Savino Piscitelli & Alfonso Capozzoli, 2023. "Data-Driven Fault Detection and Diagnosis: Research and Applications for HVAC Systems in Buildings," Energies, MDPI, vol. 16(2), pages 1-6, January.
    12. Prashant Kumar & Salman Khalid & Heung Soo Kim, 2023. "Prognostics and Health Management of Rotating Machinery of Industrial Robot with Deep Learning Applications—A Review," Mathematics, MDPI, vol. 11(13), pages 1-37, July.
    13. 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).
    14. Fang, Xi & Gong, Guangcai & Li, Guannan & Chun, Liang & Li, Wenqiang & Peng, Pei, 2021. "A hybrid deep transfer learning strategy for short term cross-building energy prediction," Energy, Elsevier, vol. 215(PB).
    15. Zhong, Fangliang & Calautit, John Kaiser & Wu, Yupeng, 2023. "Fault data seasonal imbalance and insufficiency impacts on data-driven heating, ventilation and air-conditioning fault detection and diagnosis performances for energy-efficient building operations," Energy, Elsevier, vol. 282(C).
    16. 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.
    17. 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.
    18. Xiaoxia Liang & Fang Duan & Ian Bennett & David Mba, 2020. "A Comprehensive Health Indicator Integrated by the Dynamic Risk Profile from Condition Monitoring Data and the Function of Financial Losses," Energies, MDPI, vol. 14(1), pages 1-25, December.
    19. 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).
    20. Weigert, Andreas & Hopf, Konstantin & Günther, Sebastian A. & Staake, Thorsten, 2022. "Heat pump inspections result in large energy savings when a pre-selection of households is performed: A promising use case of smart meter data," Energy Policy, Elsevier, vol. 169(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:gam:jeners:v:14:y:2021:i:13:p:3877-:d:583601. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.