基于图像纹理特征的高适应性低成本蒸发器结霜状态智能识别方法对比

徐英杰; 张恒瑞; 柳云宇; 周晓晓; 韩晓红; 陈光明

doi:10.12465/j.issn.0253-4339.2025.04.061

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基于图像纹理特征的高适应性低成本蒸发器结霜状态智能识别方法对比

更新时间：2025-08-28

- 基于图像纹理特征的高适应性低成本蒸发器结霜状态智能识别方法对比
- Comparison of High Adaptability and Low-Cost Intelligent Recognition Method for Evaporator Frosting Status Based on Image Texture Features
- 制冷学报 2025年46卷第4期页码：61-74
- 作者机构：
  
  1.浙江工业大学机械工程学院　杭州　310023
  2.浙江盾安人工环境股份有限公司　绍兴　311835
  3.浙江大学制冷与低温研究所　杭州　310007
- 作者简介：
  
  韩晓红，女，教授，博士生导师，浙江大学制冷与低温研究所，（0571）87953944，E-mail：hanxh66@zju.edu.cn。研究方向：高热流散热技术（主要指热管散热、微通道散热及浸没液体冷却技术）、动力电池浸没液冷热管理技术、制冷剂替代技术、制冷剂泄漏与回收及再利用技术。
- 基金信息：
  
  国家自然科学基金(52076185)
- DOI：10.12465/j.issn.0253-4339.2025.04.061
  中图分类号： TB61⁺1;TK172;TP75
- 收稿日期：2024-01-31，
  
  修回日期：2024-03-28，
  
  录用日期：2024-05-22，
  
  纸质出版日期：2025-08-16
- 稿件说明：
移动端阅览
徐英杰, 张恒瑞, 柳云宇, 等. 基于图像纹理特征的高适应性低成本蒸发器结霜状态智能识别方法对比[J]. 制冷学报, 2025,46(4):61-74.

Xu Yingjie, Zhang Hengrui, Liu Yunyu, et al. 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.
徐英杰, 张恒瑞, 柳云宇, 等. 基于图像纹理特征的高适应性低成本蒸发器结霜状态智能识别方法对比[J]. 制冷学报, 2025,46(4):61-74. DOI： 10.12465/j.issn.0253-4339.2025.04.061.

Xu Yingjie, Zhang Hengrui, Liu Yunyu, et al. 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.

摘要

误除霜或除霜不及时将导致制冷/热泵机组能耗增加、稳定性降低、故障率提升。精准识别结霜状态从而及时除霜是提升制冷/热泵性能的重要方向。基于数字/智能技术的结霜状态识别方法展现出较好的潜力，但现有技术在实际应用的复杂条件下，识别准确率明显降低，亟待改善。基于此，以蒸发器表面图像的纹理特征为切入点，提出以灰度共生矩阵提取纹理特征并结合麻雀算法优化后的极限学习机进行分类的智能识别方法。有望规避拍摄角度、光照强度等外部条件的影响，实现较强的适应性。建立实验装置，实验采集4 125张复杂工况下3种结霜状态的蒸发器图像样本，对新方法进行了验证和对比研究。结果表明，该方法在不同工况下识别准确率可接近或高于95%，显著高于现有的方法5%~35%。该方法的稳定性较高，成本较低，表现出良好的应用潜力。

Abstract

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.

关键词

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references

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