Detection of Bearing Faults Using a Novel Adaptive Morphological Update Lifting Wavelet

The current morphological wavelet technologies utilize a fixed filter or a linear decomposition algorithm, which cannot cope with the sudden changes, such as impulses or edges in a signal effectively. This paper pre- sents a novel signal processing scheme, adaptive morpho- logical update lifting wavelet （AMULW）, for rolling element bearing fault detection. In contrast with the widely used morphological wavelet, the filters in AMULW are no longer fixed. Instead, the AMULW adaptively uses a morphological dilation-erosion filter or an average filter as the update lifting filter to modify the approximation signal. Moreover, the nonlinear morphological filter is utilized to substitute the traditional linear filter in AMULW. The effectiveness of the proposed AMULW is evaluated using a simulated vibration signal and experimental vibration sig- nals collected from a bearing test rig. Results show that the proposed method has a superior performance in extracting fault features of defective roiling element bearings.

Morphological Undecimated Wavelet Decomposition Fusion Algorithm and Its Application on Fault Feature Extraction of Hydraulic Pump

Since vibration signals of hydraulic pump are mostly nonlinear and traditional fusion algorithm cannot satisfyingly process them,a morphological undecimated wavelet decomposition fusion(MUWDF)algorithm is proposed.Firstly,under the framework of morphological undecimated wavelet decomposition(MUWD),multi-channel signals are decomposed.Approximate signals of all decomposition layers are selected by feature energy factor and fused according to the presented fusion rules.Furthermore,specific method for optimal selection of MUWDF parameters is presented to avoid subjective influences.Finally,the proposed algorithm is verified by simulation signals and pump vibration signals.

Morphological Filters and Wavelet-based Histogram Equalization Image Enhancement for Weak Target Detection

Image enhancement methods are typically aimed at improvement of the overall visibility of features. Though histogram equalization can enhance the contrast by redistributing the gray levels, it has the drawback that it reduces the information in the processed image. In this paper, we present a new image enhancement algorithm. After histogram equalization is carried out, morphological filters and wavelet-based enhancement algorithm is used to clean out the unwanted details and further enhance the image and compensate for the information loss during histogram equalization. Experimental results show that the morphological filters and wavelet-based histogram equalization algorithm can significantly enhance the contrast and increase the information entropy of the image.

Ground-roll separation of seismic data based on morphological component analysis in twodimensional domain

Ground roll is an interference wave that severely degrades the signal-to-noise ratio of seismic data and affects its subsequent processing and interpretation.In this study,according to differences in morphological characteristics between ground roll and reflected waves,we use morphological component analysis based on two-dimensional dictionaries to separate ground roll and reflected waves.Because ground roll is characterized by lowfrequency,low-velocity,and dispersion,we select two-dimensional undecimated discrete wavelet transform as a sparse representation dictionary of ground roll.Because of a strong local correlation of the reflected wave,we select two-dimensional local discrete cosine transform as the sparse representation dictionary of reflected waves.A sparse representation model of seismic data is constructed based on a two-dimensional joint dictionary then a block coordinate relaxation algorithm is used to solve the model and decompose seismic record into reflected wave part and ground roll part.The good effects for the synthetic seismic data and application of real seismic data indicate that when using the model,strong-energy ground roll is considerably suppressed and the waveform of the reflected wave is effectively protected.

基于形态学构建尺度空间与谱峭度的轴承故障诊断方法

针对高速列车轴箱轴承服役环境恶劣,故障特征难以提取的问题,提出了基于形态学构建尺度空间表征的经验小波变换(EWT)故障诊断方法。首先,提出基于形态学的尺度空间表征方法,实现共振频带边界的自适应识别;其次,将基于形态学构建的尺度空间表征与谱峭度相结合,识别出共振频带;然后,根据所识别的频带边界构造滤波器组,对信号进行自适应分解;最后,对分解信号进行包络谱分析,识别故障特征频率,实现轴承故障的诊断。高速列车轴箱轴承振动试验结果表明,该方法能够有效提高频带划分的准确性以及计算效率,准确诊断轴承故障。

基于Morphology和SVM对细胞组织的分析

随着计算机技术、成像技术、图像处理技术、医学形态学研究的发展,计算机辅助图像处理成为了医学研究的得力工具。论文利用数学形态学(Morphology)和小波变换(Wavelet)对细胞图像进行分析和识别,充分发挥形态学善于处理图像形态的优势和小波变换系数反映细节的特点,通过图像预处理、分割和特征提取,最后通过支持向量机分类器(SVM)来完成细胞状态的自动分类和识别,试验表明该方法具有较高的准确率和可靠性。

Nonlinear Adaptive Wavelet Transform for Lossless Image Compression

The paper presents a class of nonlinear adaptive wavelet transforms for lossless image compression. In update step of the lifting the different operators are chosen by the local gradient of original image. A nonlinear morphological predictor follows the update adaptive lifting to result in fewer large wavelet coefficients near edges for reducing coding. The nonlinear adaptive wavelet transforms can also allow perfect reconstruction without any overhead cost. Experiment results are given to show lower entropy of the adaptive transformed images than those of the non-adaptive case and great applicable potentiality in lossless image compresslon.

EDGE DETECTION METHOD OF REMOTE SENSING IMAGES BASED ON MATHEMATICAL MORPHOLOGY OF MULTI-STRUCTURE ELEMENTS

This paper puts forward an effective, specific algorithm for edge detection. Based on multi-structure elements of gray mathematics morphology, in the light of difference between noise and edge shape of RS images, the paper establishes multi-structure elements to detect edge by utilizing the grey form transformation principle. Compared with some classical edge detection operators, such as Sobel Edge Detection Operator, LOG Edge Detection Operator, and Canny Edge Detection Operator, the experiment indicates that this new algorithm possesses very good edge detection ability, which can detect edges more effectively, but its noise-resisting ability is relatively low. Because of the bigger noise & remote sensing image, the authors probe into putting forward other edge detection method based on combination of wavelet directivity checkout technology and small-scale Mathematical Morphology finally. So, position at the edge can be accurately located, the noise can be inhibited to a certain extent and the effect of edge detection is obvious.

A New Text Location Approach Based Wavelet

With the advancement of content-based retrieval technology, the importance of semantics for text information contained in images attracts many researchers. An algorithm which will automatically locate the textual regions in the input image will facilitate the retrieving task, and the optical character recognizer can then be applied to only those regions of the image which contain text. In this paper a new text location method based wavelet is described, which can be used to locate textual regions from complex image and video frame. Experimental results show that the textual regions in image can be located effectively and quickly.

Electrocardiogram Feature Extraction Technique Based on Wavelet Domain Lorentz Differential Deconvolution

In order to extract the cardiac characteristics in electrocardiogram (ECG), a feature extraction technique was developed based on wavelet domain Lorentz differential deconvolution. During the feature extraction of QRS complex, baseline drifts were firstly removed from raw ECG records by a mathematical morphology method and the feature sub-band of QRS complex was separated by using wavelet transform. Then an evolving Lorentz differential deconvolution technique was applied to estimating the local features of QRS complex from this sub-band. During the feature extraction of P and T waves, the above steps were similarly employed and, before wavelet transform, QRS complex was eliminated through locating their positions to avoid relevant disturbance. The proposed technique achieved a recognition of 99.37% for QRS recognition and a detection rate of 98.62% for P waves detection when tested with the MIT/BIH Database. And validated with the QT Database, the results of QT intervals detection also showed an obvious improvement (85.26% when target domain less than 14 ms and 95.34% when target domain less than 28 ms separately on average).

基于小波去噪与改进Canny算法的带钢表面缺陷检测

针对带钢表面图像亮度不均匀、对比度低以及缺陷种类多、形式复杂的问题,提出一种基于小波去噪与改进Canny算法的带钢表面缺陷检测算法。首先通过小波变换将原始图像分解,对低频分量采用改进的同态滤波提高亮度和对比度,对高频分量采用改进的阈值函数进行去噪,并通过小波重构得到增强图像。其次对传统Canny算法进行改进,通过改进的自适应加权中值滤波进行平滑,并增加梯度方向模板;然后采用迭代式最优阈值选择法与最大类间方差法来求取高低阈值,提高算法的自适应性。最后采用形态学处理对缺陷边缘填充,并去除干扰边缘及毛刺,得到带钢表面缺陷区域。实验结果表明,所提算法对带钢表面缺陷的检测效果较好、精度较高,适用于多种类型的带钢表面缺陷检测。

风电场柔直送出系统联接变故障特性分析及差动保护方案

联接变是衔接交直流系统的桥梁,对其故障特性的分析是构建保护方案的重要基础。然而,双馈风电经柔直送出系统中整流侧联接变发生故障时,机侧短路电流呈现出频偏和弱馈故障特性,而阀侧短路电流不仅含有大量谐波,在不同的控制策略下还存在幅值差异、相角差异,甚至会出现断流的情况。如此复杂的故障特性给联接变的差动保护正确动作带来十分严峻的挑战。为此,该文以联接变阀侧发生最为常见的单相接地故障为例,分析双馈风电场柔直送出系统联接变风电场侧及阀侧短路电流故障特性及致使差动保护性能降低的原因。在此基础上,提出分别利用形态学滤波分解及同步挤压小波变换对换变流两侧电流进行处理,并以处理后的两侧电流轨迹图斜率为判据,对区内外故障和涌流进行识别的保护方案。最后,基于PSCAD/EMTDC的仿真结果表明:所提出的方案能很好地对风电联接变区内外故障和涌流进行识别,在不同影响因素条件下该方案也具有良好的适用性。

融合小波变换与形态学的车身刮痕边缘检测

针对车身表面外部噪声干扰的影响,导致无法准确提取到车身刮痕边缘有效信息,提出一种融合小波变换和数学形态学的边缘检测方法。首先,为减弱车身刮痕边缘提取过程中受光照等外界噪声干扰影响,对刮痕源图像利用小波模极大值边缘检测方法,消除部分噪声干扰,提高边缘检测定位的准确性;然后,为了优化刮痕边缘的连续性,避免在多尺度小波检测下边缘有效信息的丢失,提出一种改进的数学形态学边缘检测算法;最后,将小波模极大值与数学形态学两种方法的检测结果进行小波分解,分别得到图像的高频和低频分量。两图像低频分量根据图像能量大小分配权重进行融合,对应的高频分量通过绝对值取大的方式融合,将融合后的高频和低频分量重构,获取最终的刮痕边缘。通过实验分析,该融合方法能保证在去噪的同时,获得更完善清晰的边缘特征信息,效果优于传统边缘检测算法,能为后续刮痕精确定位修复提供理论与技术支持。

压缩传感图像边缘自适应增强方法研究

传感图像存储过程中受压缩处理的影响,造成边缘细节模糊,为此,提出基于加性小波的压缩传感图像边缘自适应增强方法。基于高斯平滑滤波算法对压缩传感图像进行去噪处理,对经过去噪处理的图像展开多尺度加性小波分形,采用形态学梯度对低频图像进行滤波处理,获取形态学梯度图。通过模极大值方法提取边缘,叠加多层边缘信息获取高频边缘图。将形态学梯度图和高频边缘图两者作加性小波逆变换,对逆变换结果进行二值化处理,获取压缩传感图像边缘图,实现边缘自适应增强。主观测试结果表明,所提方法可以准确增强压缩后传感图像的边缘;客观测试结果表明,所提方法信息熵均值为7.81、平均梯度为7.89、对比度为273.7,可以获取较多的图像边缘细节。

基于形态学的机械臂传动机构振动信号降噪研究

为了提高机械臂传动机构的运行稳定性,设计了一种基于形态学方法的机械臂振动信号降噪方法。形态学算法可以获得理想的脉冲噪声去除效果,实现良好脉冲噪声抑制性能。利用形态学滤波方法可以对振动信号误差起到更优的抑制性能。搭建了实验平台,并开展信号测试。研究结果表明:设计算法可充分保留特征频率,同时获取的噪声抑制性能都相对更为理想。实验测试结果得出在抑制噪声性能方面设计算法的效果相对更为显著,所提取状态特征的准确度更高。该研究可以拓宽到其他的机械传动领域,具有很好的推广价值。

基于形态学特征的机械零件焊接裂纹无损图像检测方法

在机械零件焊接裂纹检测中,由于采集的图像中焊接边界受到反射波干扰,其存在模糊化噪点,导致检测不准确。为解决该问题,基于边界形态中形状基不变原理,提出考虑形态学特征的机械零件焊接裂纹无损图像检测方法。该方法采用机器视觉技术采集机械零件焊接的视觉图像,通过照明反射模型和傅里叶变换,滤除图像受到光学干扰产生的模糊化噪点。通过二层小波分解图像,构建低频近似分量和高频细节分量。引入形态学算法,以结构元素为核心,通过腐蚀和膨胀处理,并且通过优化腐蚀次数和同态滤波进行优化,提取机械零件焊接裂纹的形态学特征,实现机械零件焊接裂纹无损检测。经过试验证明,所提方法采集的视觉图像更加清晰,可以有效提取裂纹形态学特征和裂纹,其机械零件焊接裂纹检测位置与实际位置基本一致,误差仅为0.01,说明该方法检测精度较高。

基于彩色图像的绝缘子污秽等级检测

绝缘子积污易导致污闪事故,危害电网的稳定运行,因此有必要检测绝缘子的污秽等级,以便安排清理作业。文章提出一种基于彩色图像的绝缘子污秽等级判别方法。通过一种基于小波变换与形态学的前景分割算法及一种基于三颜色空间的污秽分割算法,从原始图像中提取污秽部分,同时利用椭圆检测算法圈出绝缘子盘面,通过计算圈出的各个绝缘子盘面内污秽面积与盘面面积的比值均值大小,判断绝缘子的污秽等级,实现绝缘子污秽等级的识别与检测。经实验与对比可知,文章算法对绝缘子污秽等级的检测准确率为99.5%,优于普通的图像检测方法,表明文章方法是一种在线检测绝缘子污秽等级的可行方法。

基于形态小波的转子轴心轨迹提纯

针对汽轮发电机组轴心轨迹的提纯问题,提出采用形态小波提纯转子轴心轨迹的新方法.形态小波作为一种非线性小波,兼顾数学形态学的形态特征和小波的多分辨率特性,具有良好的细节保留和抗噪声性能.采用形态小波提纯轴心轨迹,无须考虑信号的频谱特征,通过对形态小波分解得到的分量进行阈值降噪处理,然后重构原信号得到提纯的轴心轨迹.将实际数据分别通过传统的Haar小波与形态小波进行处理,结果表明,形态小波在轴心轨迹提纯方面的效果更明显.由于形态小波变换算法只涉及加减和极大、极小运算,算法简单且执行高效,适合汽轮发电机组故障的在线监测和诊断.

红外序列图像小目标检测与跟踪技术综述

指出了小目标检测与跟踪中存在的难点问题;综合该领域近年来的研究成果,按照检测与跟踪的先后次序,将算法分为DBT和TBD两大类,并给出相应的数学模型和各自的主流方法;对两大类算法的执行性能进行了比较;最后指出该领域可以深入研究的方向。

基于小波包和数学形态学结合的图像特征提取方法

提出了一种基于小波包和数学形态学相结合的图像特征提取方法,应用于车牌字符等的图像提取及边缘检测。采用了小波包对图像进行分解并重构其近似部分,用形态学膨胀和腐蚀等形态学基本运算以及形态学梯度对图像进行边缘检测,并应用形态学重构填充了车牌字符的空隙,以便后续的字符识别。仿真实验表明与Edge边缘检测算子相比较,该方法能够更好地提取图像特征,检测出的边缘更清晰,并更好地填充了车牌字符的空隙。