基于改进剪切波和Canny的故障区域检测算法研究

针对机械故障红外热图像存在各种噪声造成故障区域测量参数提取精度低的问题,本文提出了基于改进剪切波和Canny的故障区域检测算法。该算法包括基于改进剪切波的去噪算法和改进Canny算子的边缘检测算法,在去噪中本文提出的是一种基于剪切波和高阶谱相结合的新算法,可以有效地去除高斯噪声与椒盐噪声的混合噪声并保留图像细节；在图像的边缘检测中本文利用剪切波与Canny算子相结合的改进新算法对红外故障图像进行边缘提取,从而消除了传统的Canny算子在检测时出现的伪边缘现象。实验仿真结果表明本文提出的去噪算法与传统的去噪算法相比显著提高了图像的峰值信噪比(PSNR),同时图像的边缘和细节也得到了很好的保留,实现了故障区域特征参数的精确有效提取。

Research on fault region detection algorithm based on improved shear wave and Canny

For the problem of mechanical fault infrared thermal image,there are various noises causing low accuracy of measurement parameters in fault area.This paper proposes a fault region detection algorithm based on improved shear wave and Canny.The algorithm includes a remove noise algorithm based on improved shear wave and an edge detection algorithm based on improved Canny operator.In the remove noise process,a new remove noise algorithm based on combination of shear wave and higher order spectrum is proposed.By remove noise the high-frequency and low-frequency parts of the infrared image,the algorithm can effectively remove the mixed noise composed of additive Gaussian noise and salt and pepper noise while preserving the image details.In the edge detection of the image,the paper uses the scissors.The improved new algorithm combined with the Canny operator performs edge extraction on the infrared fault image,thus eliminating the false edge phenomenon that the traditional Canny operator appears in the detection.The experimental results show that the remove noise algorithm proposed in this paper improves the peak signal-to-noise ratio (PSNR) of the image compared with the traditional remove noise algorithm.At the same time,the edge and details of the image are well preserved,and the characteristic parameters of the fault region are extracted accurately and effectively.