针对实测振动信号易受噪声污染而淹没有用信息的问题,提出一种基于自适应结构元素广义形态滤波(ASEGMF)方法对旋转机械振动信号进行降噪处理.首先,根据待分析信号的性质,选择正弦形结构元素,并定义了结构元素的长度尺度和高度尺度.其次,根据信号局部峰值特征,定义了峰值间隔和峰值高度,结合自适应方法得到了正弦结构元素的长度尺度和高度尺度.最后,采用一小一大自适应结构元素级联而成的广义形态滤波器对振动信号进行降噪处理.该方法克服了以往形态滤波器结构元素尺寸选择的随机性,完全根据信号局部峰值特征自适应地确定结构元素,消除了人为因素的影响.仿真和实例分析结果表明,自适应结构元素广义形态滤波具有更强的降噪性能,非常适合旋转机械故障的在线监测和诊断.%Viewing that practical data are easily contaminated and useful informations are often covered by noises, a novel de-noising approach was proposed based on the adaptive structure element for generalized morphological filtering ( ASEGMF) . The sine structure element was selected according to the feature of vibration signal, and the length scale and the height scale of structure element were defined. The peak distance and the peak height were defined according to signal's local characteristics, and the length scale and the height scale of sine structure element were gotten by using adaptive method. The contaminated vibration signal was then de-noised by the generalized morphological filter cascaded successively by one small and one big adaptive structure elements. The method conquers the selective randomness of current morphological filter and the structure element are obtained adaptively in accordance with signal's local characteristics without artificial interference. Practical and simulation results show that the method is of better de-nosing effectiveness and is suitable for on-line monitoring and diagnosis of rotating machinery.
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