针对电气设备的红外图像的优化OTSU细分算法基于温度特征提取方法.
Xueli Liu1, Zhanlong Zhang1, Yuefeng Hao1
1School of Electrical Engineering, Chongqing University, Chongqing 400044, China.
Sensors (Basel, Switzerland)
|February 24, 2024
概括
这项研究优化了用于电气故障诊断的红外图像处理,通过使用灰狼优化 (GWO) 增强目标设备细分,并通过K-最近邻居 (KNN) 提高温度提取精度. 这些方法加速了分析,并提供了更可靠的故障检测.
科学领域:
- 电气工程 电气工程
- 计算机视觉 计算机视觉
- 图像处理 图像处理
背景情况:
- 红外热图对于电气设备故障诊断至关重要.
- 关键步骤包括目标设备细分和温度特征提取.
- 现有的方法由于计算复杂性和非线性灰度尺度-温度关系而存在缓慢细分和不准确的温度读数问题.
研究的目的:
- 为了加速电气设备的红外图像细分.
- 为了提高从红外图像中提取温度特征的准确性.
- 提高基于红外线的故障诊断的整体可靠性.
主要方法:
- 建议采用优化的Otsu方法 (OTSU) 分段算法,并通过灰狼优化 (GWO) 算法进行增强.
- 开发了一个K-近邻 (KNN) 算法,用于从红外图像中准确地提取温度值.
- GWO-OTSU方法优化了值的确定,以实现更快的细分.
主要成果:
- 与非优化方法相比,优化细分方法显著增加了超过83.99%的值计算速度,保持了可比的细分质量.
- 基于KNN的温度提取方法显示了显著的改进,最大残余绝对值增加了73.68%,平均残余绝对值比线性方法增加了78.95%.
- 这些进步使电气设备的故障诊断更加有效和准确.
结论:
- 拟议的GWO优化的OTSU方法有效地加速了电气设备的红外图像细分.
- 基于KNN的温度提取方法显著提高了识别过热和潜在故障的准确性.
- 综合方法为使用红外成像进行电气设备故障诊断提供了更强大,更有效的解决方案.
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