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Development of Fault Diagnosing System for Ice-Storage Air-Conditioning Systems

Author

Listed:
  • Ching-Jui Tien

    (Department of Electrical Engineering, Cheng-Shiu University, Kaohsiung 833, Taiwan)

  • Chung-Yuen Yang

    (Department of Electrical Engineering, Cheng-Shiu University, Kaohsiung 833, Taiwan)

  • Ming-Tang Tsai

    (Department of Electrical Engineering, Cheng-Shiu University, Kaohsiung 833, Taiwan)

  • Hong-Jey Gow

    (Kuen-Ling Machinery Refrigerating Co., Ltd., Kaohsiung 826, Taiwan)

Abstract

This paper proposes a fault diagnosing system for the Ice-Storage Air-Conditioning System (ISACS) to supervise the operation conditions of the brine chillers. Combining the Radial Basis Function Network (RBFN) and Robust Quality Design (RQD), an Enhanced RBFN (ERBFN) is proposed to pursue fast and accurate fault diagnosis. The RQD method is used to adjust the parameters in the RBFN training stage to improve the searching ability, and good performance with a close spike tracking capability can be seen. The efficiency of the brine chiller in the ISACS was considered as the quality characteristics, the values measured by all instruments were considered as control factors, and noise factors were abnormal variable control factors in the system. ERBFN can improve the efficiency of the ISACS and prevent the equipment from being damaged without warning. ERBFN is used for fault diagnosis to ensure the ISACS performance is normal. Experimental results are provided to show the effectiveness of the proposed method. The new artificial neural network algorithm proposed in this paper was successfully applied to the fault diagnosis of ISACS. It not only provides a reference for enterprises but can also be applied to studies on other topics in the future.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:11:p:3981-:d:826367
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    References listed on IDEAS

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    1. 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.
    2. Dong, Xiao-Jian & Shen, Jia-Ni & He, Guo-Xin & Ma, Zi-Feng & He, Yi-Jun, 2021. "A general radial basis function neural network assisted hybrid modeling method for photovoltaic cell operating temperature prediction," Energy, Elsevier, vol. 234(C).
    3. Simone Mancin & Marco Noro, 2020. "Reversible Heat Pump Coupled with Ground Ice Storage for Annual Air Conditioning: An Energy Analysis," Energies, MDPI, vol. 13(23), pages 1-16, November.
    4. Whei-Min Lin & Chia-Sheng Tu & Ming-Tang Tsai & Chi-Chun Lo, 2015. "Optimal Energy Reduction Schedules for Ice Storage Air-Conditioning Systems," Energies, MDPI, vol. 8(9), pages 1-18, September.
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    Cited by:

    1. Lin Pan & Sheng Wang & Jiying Wang & Min Xiao & Zhirong Tan, 2022. "Research on Central Air Conditioning Systems and an Intelligent Prediction Model of Building Energy Load," Energies, MDPI, vol. 15(24), pages 1-31, December.

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