泄漏蒸汽红外温度场静动态锤尾特性表征与辨识

针对复杂工业现场泄漏蒸汽智能识别需要，文中提出红外温度场表征与辨识方法。利用模拟分析对蒸汽泄漏过程进行研究，揭示其温度场发生发展规律，即扩散特性、锤尾特性、动态特性，中心向源特性为一体的静动态锤尾特性。通过对温度层划分后的蒸汽温度场进行研究，提取蒸汽温度分布细节特征，提出变尺度灰度处理方法，实现了蒸汽红外温度场的高清化表征。为提高辨识速度和精度，建立Mask R-CNN网络模型，对泄漏蒸汽的锤尾特征进行学习和动态挖掘，对单张锤尾样本的识别准确率达到90.71%，总体算法识别准确率达到了99.93%，实现了蒸汽以及泄漏特性的有效辨识。现场实测表明，该算法对连续5帧图像处理时间为0.48 s，对于复杂工业现场的蒸汽泄漏辨识达到了快速准确的辨识效果。

Characterization and identification of static and dynamic hammer tail characteristics in infrared temperature field of leaking steam

To make intelligent identification of leaked steam in complex industrial field, a temperature field characterization and recognition method using infrared vision technology is proposed. The steam leakage process is simulated to reveal the occurrence and development characteristics of its temperature field, including diffusion characteristics, hammer tail characteristics, dynamic characteristics, and centrality characteristics. The temperature layer of steam temperature field is extracted, the detailed characteristics of temperature distribution are analyzed, and the variable scale gray processing method is proposed to realize the high-definition image representation of steam infrared temperature field. To improve the identification speed and accuracy, the MASK-RCNN model is established to make deep learning and dynamic mining of hammer tail image features of leaked steam. In this way, the recognition accuracy of single hammer tail is about 90.71%, and that of the overall algorithm is up to 99.93%. The algorithm is tested with leaked steam recognition in power plant equipment operation. Results show that time consumed to process 5 consecutive frames is about 0.50 s, and steam leakage of various particle sizes can be identified quickly and accurately.