基于多尺度影像融合的气缸盖内壁热疲劳损伤检测方法

Multiscale Image Fusion-Based Thermal Fatigue Damage Detection Method for Cylinder Head Inner Wall

针对气缸盖内壁热场数据重复率高、冗余大导致的小采样率下影像难以重构的问题,利用多个单尺度重构影像的融合,提出一种适用于小样本的气缸盖内壁热疲劳损伤检测方法.该方法首先通过对不同尺度下内壁热场散斑图案与其外壁总辐射能的关联运算,来获得缸盖内壁的多个重构影像,然后利用图像融合技术对不同尺度下的内壁重构影像进行融合,获得缸盖内壁的融合影像.通过对不同采样率下内壁影像重构结果的对比分析,说明了所提方法在小采样率影像重构中的优势.同时,还讨论了不同融合权重系数对融合影像的影响.实验结果表明:所提方法可在较低采样率下实现对内壁热疲劳损伤区域的检测;当采样次数为500时,较传统关联方法,所提方法的峰值信噪比和对比度分别提升了9.62%和26.13%;此外,所提方法在延缓重构影像质量下降方面也有独特优势,有效打破实际工程中数据获取有限所导致的热疲劳损伤检测无法实现的困局.

To solve the issue of complex image reconstruction at a small sampling rate due to the high repetition rate and redundancy of cylinder head inner wall thermal field data,a thermal fatigue damage detection method for cylinder head inner wall with a small sample is proposed via fusion of multiple singlescale reconstructed images.The proposed method begins by correlating the thermal scatter pattern of the inner wall with the total radiant energy of its outer wall at various scales to obtain multiple reconstructed images of the cylinder head inner wall.Then,the image fusion technology is used to fuse the reconstructed images of the inner wall at different scales to obtain the fused images of the cylinder head inner wall.The paper demonstrates the advantageous performance of the proposed method in small sampling rate image reconstruction by comparing the results of inner wall image reconstruction at various sampling rates.The effects of different fusion weight coefficients on the fused images are also discussed.The outcomes of the experiments demonstrate that the proposed method may detect the thermal fatigue damage zone of the inner wall at a lower sampling rate.The peak signaltonoise ratio and contrast ratio of the proposed method are enhanced by 9.62%and 26.13%,respectively,compared with those of the traditional correlation method when the number of samplings is 500.The proposed method also has the distinct benefit of postponing the deterioration of the reconstructed picture quality,thereby resolving the problem of thermal fatigue damage detection caused by insufficient data gathering in realworld applications.