摘要
表面生锈的铁磁材料的微裂纹检测是个难点。当微裂纹深度与表面腐蚀深度接近时,表面腐蚀凹陷所造成的噪声几乎淹没裂纹信号,造成深度在0.6mm以下的裂纹检测数据相互覆盖。基于LabWindows/CVI的虚拟微裂纹信号分析仪,利用三种模糊聚类算法(模糊C-均值聚类算法、“max—min”准则下的模糊聚类算法、基于遗传算法的模糊C-均值算法)对采样数据进行分类,再通过小波变换与信号分形技术,有效地从噪声中提取了0.6mm以下的极微裂纹信号,同时成功的对0.6mm以下、不同深度的裂纹进行分形识别,从而改善了硬件检测系统对这种极微裂纹的分辨率,提前了材料寿命的预报时机。
When the depth of micro-crack is close to the depth of rust, the noise from the rust will almost inundate the signal from micro-crack. For this reason, it is difficult to test the micro-cracks of depth <0.6mm on a rusted surface of ferromagnetic material. In order to effectively extract and identify the signals from those micro-cracks, a virtual signal analyzer was developed on the LabWindows/CVI. In this analyzer the fuzzy C-mean clustering algorithm, fuzzy clustering algorithm base on max-min criterion and fuzzy C-mean clustering algorithm base on genetic algorithm(GA) are used to clusterize the sampling data first, and then wavelet transform and signal discrimination are used to extract the signals. It can successfully identify the micro-cracks down to 0.4mm, and thus improve the accuracy on predicting the material's life.
出处
《中国电机工程学报》
EI
CSCD
北大核心
2004年第8期142-146,共5页
Proceedings of the CSEE
关键词
铁磁材料
极微裂纹信号
聚类
锈蚀
Electrotechnology
Ferromagnetic material
Extraction of micro-cracks signal from noise
Fuzzy clustering algorithm
Wavelet transform
Signal fractal
