Department of Automatic Control and Robotics, Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, AGH University of Science and Technology, 30-059 Kraków, Poland.
Sensors (Basel). 2021 Apr 18;21(8):2853. doi: 10.3390/s21082853.
The paper presents an analysis and classification method to evaluate the working condition of angle grinders by means of infrared (IR) thermography and IR image processing. An innovative method called BCAoMID-F (Binarized Common Areas of Maximum Image Differences-Fusion) is proposed in this paper. This method is used to extract features of thermal images of three angle grinders. The computed features are 1-element or 256-element vectors. Feature vectors are the sum of pixels of matrix or PCA of matrix or histogram of matrix . Three different cases of thermal images were considered: healthy angle grinder, angle grinder with 1 blocked air inlet, angle grinder with 2 blocked air inlets. The classification of feature vectors was carried out using two classifiers: Support Vector Machine and Nearest Neighbor. Total recognition efficiency for 3 classes () was in the range of 98.5-100%. The presented technique is efficient for fault diagnosis of electrical devices and electric power tools.
本文提出了一种通过红外（IR）热成像和图像处理来评估角磨机工作状态的分析和分类方法。本文提出了一种名为 BCAoMID-F（二值公共区域最大图像差异融合）的创新方法。该方法用于提取三个角磨机热图像的特征。计算出的特征是 1 元素或 256 元素向量。特征向量是矩阵的像素总和或矩阵的 PCA 或矩阵的直方图。考虑了三种不同的热图像情况：健康的角磨机、一个进气口堵塞的角磨机、两个进气口堵塞的角磨机。使用两种分类器（支持向量机和最近邻）对特征向量进行分类。三类的总体识别效率（）在 98.5-100%的范围内。所提出的技术可有效用于电气设备和电动工具的故障诊断。
