基于深度学习的空预器转子红外补光图像积灰状态识别
摘要：目前大型电站锅炉广泛采用的回转式空气预热器(简称“空预器”)普遍存在堵塞现象，严重时甚至会限制锅炉出力。针对这一问题，提出一种基于深度学习的空预器转子红外图像积灰演化分析方法。针对获取的空预器转子红外补光图像样本数据进行预处理，去噪后转化为灰度曲线图像，并采用高斯滤波方法进行图像增强。然后建立灰度共生矩阵(gray level co-occurrence matrix，GLCM)计算相关统计量，提取了角二阶矩(angular second moment，ASM)能量、对比度、熵、逆方差(inverse difference moment，IDM)和自相关性5类纹理特征参数。最后建立了深度信念网络(deep belief network，DBN)模型并进行训练与测试。结果表明：所提方法不但可以实现对空预器转子积灰程度的有效检测和监视，而且能够提前预测空预器堵塞可能性，从而指导运行人员优化运行吹灰系统，保证空预器正常运行。
Abstract:Ash plugging of the rotary air preheater widely used in large-scale power station often occurs and even reduces the efficiency of the boiler in sever cases. Therefore, a deep learning-based method was proposed for analyzing the evolution of ash accumulation for the infrared compensation images of the air preheater rotor. The sample data of the infrared compensation images of air preheater rotor was preprocessed, and the denoised image was transformed into the gray-level curve image, and the Gaussian filtering method was used for the image enhancement. Then, the gray-level co-occurrence matrix (GLCM) was established, the correlation statistics were calculated, and five different types of texture feature parameters of angular second moment (ASM) energy, contrast, entropy, inverse difference moment (IDM) and correlation were extracted. Finally, a deep belief network (DBN) model was established, which was trained with those preprocessed infrared images. The testing results show that the proposed method can not only detect effectively and monitor the ash accumulation of the air preheater rotor, but also predict the occurrence of ash blockage in advance, so as to guide the operators to optimize the operation of the ash blowing system and ensure the normal operation of the air preheater.
标题：基于深度学习的空预器转子红外补光图像积灰状态识别
title:Ash Accumulation State Identification for Infrared Compensation Images of Air Preheater Rotor Based on Deep Learning Method
作者：刘君, 邓毅, 杨延西, 魏永贵, 薛燕辉, 史雯雯
authors:Jun LIU, Yi DEND, Yanxi YANG, Yonggui WEI, Yanhui XUE, Wenwen SHI
关键词：电站锅炉,回转式空预器,积灰,红外补光成像,纹理特征,灰度共生矩阵(GLCM),深度信念网络(DBN),
keywords:power station boiler,rotary air preheater,ash accumulation,infrared compensation imaging,texture feature,gray-level co-occurrence matrix (GLCM),deep belief network (DBN),
发表日期：2022-06-30
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