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Comparison of High Adaptability and Low-Cost Intelligent Recognition Method for Evaporator Frosting Status Based on Image Texture Features
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Yingjie Xu1, Hengrui Zhang1, Yunyu Liu1, Xiaoxiao Zhou2, Xiaohong Han3, Guangming Chen3
Journal of Refrigeration | 2025, 46(4) : 61 - 74
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Journal of Refrigeration | 2025, 46(4): 61-74
Comparison of High Adaptability and Low-Cost Intelligent Recognition Method for Evaporator Frosting Status Based on Image Texture Features
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Yingjie Xu1, Hengrui Zhang1, Yunyu Liu1, Xiaoxiao Zhou2, Xiaohong Han3, Guangming Chen3
Affiliations
  • 1.College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou, 310023, China
  • 2.Zhejiang Dun'an Artificial Environment Co., Ltd., Shaoxing, 311835, China
  • 3.Institute of Refrigeration and Cryogenics, Zhejiang University, Hangzhou, 310007, China
Published: 2025-08-16 doi: 10.12465/j.issn.0253-4339.2025.04.061
Outline
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Unnecessary or delayed defrosting results in increased energy consumption, reduced stability, and increased failure rates in refrigeration and heat pump units. Accurately identifying the frost status and timely defrosting are important for improving the performance of refrigeration and heat pumps. Frost status identification methods based on digital and intelligent technologies have shown significant potential. However, existing technologies have significantly reduced accuracy in complex real-world conditions and require urgent improvement. In this paper, we proposed an intelligent recognition method based on the texture features of evaporator surface images. We used a gray-level co-occurrence matrix to extract texture features and combine them with the extreme learning machine optimized by the sparrow algorithm for classification. This is expected to mitigate the impact of external conditions, such as shooting angles and light intensity, thereby achieving strong adaptability. An experimental setup was established to collect 4 125 images of the evaporator in three different frost states under complex working conditions, and the proposed method was validated and compared. The results showed that the accuracy of the method in identifying different conditions can reach 95%, which is significantly higher than that of existing methods by 5-35%. Furthermore, this method has high stability and low cost thereby demonstrating great potential for practical applications.

frosting state recognition  /  gray level co-occurrence matrix  /  digital image processing  /  texture features  /  defrost
Yingjie Xu, Hengrui Zhang, Yunyu Liu, Xiaoxiao Zhou, Xiaohong Han, Guangming Chen. Comparison of High Adaptability and Low-Cost Intelligent Recognition Method for Evaporator Frosting Status Based on Image Texture Features[J]. Journal of Refrigeration, 2025 , 46 (4) : 61 -74 . DOI: 10.12465/j.issn.0253-4339.2025.04.061
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  • National Natural Science Foundation of China(52076185)
Appendix
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Year 2025 volume 46 Issue 4
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Article Info
doi: 10.12465/j.issn.0253-4339.2025.04.061
  • Receive Date：2024-01-31
  • Online Date：2026-03-13
  • Published：2025-08-16
Article Data
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History
  • Received：2024-01-31
  • Revised：2024-03-28
  • Accepted：2024-05-22
Funding
National Natural Science Foundation of China(52076185)
Affiliations
    1.College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou, 310023, China
    2.Zhejiang Dun'an Artificial Environment Co., Ltd., Shaoxing, 311835, China
    3.Institute of Refrigeration and Cryogenics, Zhejiang University, Hangzhou, 310007, China

Corresponding:

Han Xiaohong, female, professor, doctoral advisor, Institute of Refrigeration and Cryogenics, Zhejiang University, 86-571-87953944, E-mail: . Research fields: high heat flux heat dissipation technology (mainly heat pipe heat dissipation, microchannel heat dissipation and immersion liquid cooling technology), immersion liquid cooling of power battery, refrigerant replacement technology, refrigerant leakage, refrigerant recovery, recycling and reclamation.
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https://castjournals.cast.org.cn/joweb/zlxb/EN/10.12465/j.issn.0253-4339.2025.04.061
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