用于矿井电力系统的红外图像增强方法研究

Research on Enhancement Method of Infrared Images of Mine Power System

随着我国煤矿事业的高速发展和煤矿电力系统的日趋复杂,矿井电力系统故障的定位与预测变得更加困难,因此如何实现对矿井电力系统故障的智能诊断显得尤为重要。大多数矿井电力系统故障都与温度有着重要关系,具体体现在温度与其红外图像灰度值之间的非线性映射关系。针对矿井电力系统红外图像的对比度较差且细节不够明显的特点,提出了一种基于非采样Contourlet变换(Nonsubsampled Contourlet Transform,NSCT)的红外图像增强算法,并在算法模型中构造了一个非线性匹配函数。该函数能够对红外图像的边缘进行一定的增强处理,并且能够抑制噪声的干扰。实验结果表明,本文方法可以对矿井电力系统的过热故障进行智能诊断与定位,并可为用户提供复选解决方案。

With the rapid development of coal mine enterprises and the increasing complication of coal mine power systems in our country, the fault location and prediction of coal mine power systems become more difficult. Therefore, the realization of intelligent diagnosis of faults in mine power systems is very important. Most of faults in mine power systems have an important relationship with temperature. This relationship is embodied in the nonlinear mapping relationship between the temperature and its grey level in infrared images. In view of the poor contrast and unapparent details of infrared images of mine power systems, an infrared image enhancement algorithm based on Nonsubsampled Contourlet Transform （NSCT） is put forward. In the algorithm model, a nonlinear matching function is constructed. The function can be used to implement certain enhancement processing of the edge of an infrared image and suppress the interference of noises. The experimental results show that this method can implement intelligent diagnosis and location of the overheating faults in mine power systems and provide solutions for check.