IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i7p1783-d342567.html
   My bibliography  Save this article

An Energy Graph-Based Approach to Fault Diagnosis of a Transcritical CO 2 Heat Pump

Author

Listed:
  • Kenneth R. Uren

    (School of Electrical, Electronic and Computer Engineering, North-West University, Potchefstroom 2520, South Africa)

  • George van Schoor

    (Unit for Energy and Technology Systems, Faculty of Engineering, Potchefstroom 2520, South Africa)

  • Martin van Eldik

    (School of Mechanical Engineering, North-West University, Potchefstroom 2520, South Africa)

  • Johannes J. A. de Bruin

    (School of Electrical, Electronic and Computer Engineering, North-West University, Potchefstroom 2520, South Africa)

Abstract

The objective of this paper is to describe an energy-based approach to visualize, identify, and monitor faults that may occur in a water-to-water transcritical CO 2 heat pump system. A representation using energy attributes allows the abstraction of all physical phenomena present during operation into a compact and easily interpretable form. The use of a linear graph representation, with heat pump components represented as nodes and energy interactions as links, is investigated. Node signature matrices are used to present the energy information in a compact mathematical form. The resulting node signature matrix is referred to as an attributed graph and is populated in such a way as to retain the structural information, i.e., where the attribute points to in the physical system. To generate the energy and exergy information for the compilation of the attributed graphs, a descriptive thermal–fluid model of the heat pump system is developed. The thermal–fluid model is based on the specifications of and validated to the actual behavioral characteristics of a physical transcritical CO 2 heat pump test facility. As a first step to graph-matching, cost matrices are generated to represent a characteristic residual between a normal system node signature matrix and a faulty system node signature matrix. The variation in the eigenvalues and eigenvectors of the characteristic cost matrices from normal conditions to a fault condition was used for fault characterization. Three faults, namely refrigerant leakage, compressor failure and gas cooler fouling, were considered. The paper only aims to introduce an approach, with the scope limited to illustration at one operating point and considers only three relatively large faults. The results of the proposed method show promise and warrant further work to evaluate its sensitivity and robustness for small faults.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jeners:v:13:y:2020:i:7:p:1783-:d:342567
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/7/1783/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/13/7/1783/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. Chung-Won Cho & Ho-Seong Lee & Jong-Phil Won & Moo-Yeon Lee, 2012. "Measurement and Evaluation of Heating Performance of Heat Pump Systems Using Wasted Heat from Electric Devices for an Electric Bus," Energies, MDPI, vol. 5(3), pages 1-12, March.
    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. 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.
    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. 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).
    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. 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.
    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. 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).
    10. Zhang, Zhenying & Wang, Jiayu & Feng, Xu & Chang, Li & Chen, Yanhua & Wang, Xingguo, 2018. "The solutions to electric vehicle air conditioning systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 443-463.
    11. 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).
    12. Wang, Ran & Lu, Shilei & Feng, Wei, 2020. "A novel improved model for building energy consumption prediction based on model integration," Applied Energy, Elsevier, vol. 262(C).
    13. Vladimir Franki & Darin Majnarić & Alfredo Višković, 2023. "A Comprehensive Review of Artificial Intelligence (AI) Companies in the Power Sector," Energies, MDPI, vol. 16(3), pages 1-35, January.
    14. Yoon, Y. & Jung, S. & Im, P. & Salonvaara, M. & Bhandari, M. & Kunwar, N., 2023. "Empirical validation of building energy simulation model input parameter for multizone commercial building during the cooling season," Renewable and Sustainable Energy Reviews, Elsevier, vol. 188(C).
    15. Gertsvolf, David & Horvat, Miljana & Aslam, Danesh & Khademi, April & Berardi, Umberto, 2024. "A U-net convolutional neural network deep learning model application for identification of energy loss in infrared thermographic images," Applied Energy, Elsevier, vol. 360(C).
    16. Gao, Yuan & Miyata, Shohei & Akashi, Yasunori, 2023. "How to improve the application potential of deep learning model in HVAC fault diagnosis: Based on pruning and interpretable deep learning method," Applied Energy, Elsevier, vol. 348(C).
    17. Sairam, Seshapalli & Seshadhri, Subathra & Marafioti, Giancarlo & Srinivasan, Seshadhri & Mathisen, Geir & Bekiroglu, Korkut, 2022. "Edge-based Explainable Fault Detection Systems for photovoltaic panels on edge nodes," Renewable Energy, Elsevier, vol. 185(C), pages 1425-1440.
    18. Davide Di Battista & Roberto Cipollone, 2023. "Waste Energy Recovery and Valorization in Internal Combustion Engines for Transportation," Energies, MDPI, vol. 16(8), pages 1-28, April.
    19. Tong-Bou Chang & Jer-Jia Sheu & Jhong-Wei Huang, 2020. "High-Efficiency HVAC System with Defog/Dehumidification Function for Electric Vehicles," Energies, MDPI, vol. 14(1), pages 1-12, December.
    20. Luo, Zheng & Lin, Xiaojie & Qiu, Tianyue & Li, Manjie & Zhong, Wei & Zhu, Lingkai & Liu, Shuangcui, 2024. "Investigation of hybrid adversarial-diffusion sample generation method of substations in district heating system," Energy, Elsevier, vol. 288(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:13:y:2020:i:7:p:1783-:d:342567. 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.