基于经验模态分解方法的圆度误差评定方法研究

针对圆度误差测量过程中因干扰信号影响估计精度的问题,提出基于经验模态分解的圆度误差评定方法。圆度误差测量数据包含多种信号成分,其中圆度误差信息只包含于表面形状误差信号,表面粗糙度误差信号、表面波纹度信号及噪声信号为干扰成分。利用具有数据自适应性的经验模态分解对圆度测量信号进行分解,实现各类信号成分分离;采用信号波数作为各信号成分的分离指标,实现形状误差信号提取,并利用其估计结构圆度误差。通过数值仿真和实例分析验证方法的有效性。     

基于经验模态分解的精密光学表面中频误差识别方法

对于高端光学元件,除限制低频面形误差和高频表面粗糙度之外,需要严格控制中频误差,以确保其使用性能。目前国际上广泛采用功率谱密度(Power spectral density,PSD)曲线评价中频误差,该方法以Fourier变换为基础,在全局水平上给出中频误差的综合评价。但是,光学元件磨削与抛光工艺过程中,局部波动和变周期波动是常见的中频误差存在形式。为更加准确地评价中频误差,指导补偿加工,需要识别中频误差频率及位置信息。鉴于此,从光学表面属于非平稳空间信号的角度出发,提出基于经验模态分解的精密光学表面中频误差提取和识别方法,对光学表面拟合残差进行经验模态分解,得到一系列固有模态函数,根据各阶固有模态函数特征,识别不同空间位置存在的表面误差和波动频率,并将其合成得到光学表面的中频和高频误差。仿真与实际检测结果分析证实该方法可以有效识别中频误差特征及其方位。     

经验模态分解停止准则及在故障诊断中的应用

停止准则是决定经验模态分解能否完整准确地提取出信号的内禀模态函数的重要因素之一,文中在总结现有的标准偏差(standard deviation,SD)准则、G.Rilling准则和能量差跟踪法的基础上,提出新的结合G.Rilling准则和能量差跟踪法的停止准则,并对这些准则进行仿真和实测数据的比较验证,试验结果表明,文中提出的停止准则优于SD准则和G.Rilling准则,为故障诊断提供更可靠的依据。     

基于二维双正交小波变换的贝壳表面形貌评定

研究表明海洋贝壳表面形貌与防污性能存在一定的关系。为评定贝壳表面形貌,探讨贝壳表面形貌与其防污性能关系,采用接触式表面轮廓测量仪对贝壳的表面形貌进行测量;运用二维双正交小波变换消除贝壳表面形貌测量数据中的形状误差,计算常用的表面特征参数。结果表明:运用二维双正交小波变换将贝壳表面形貌测量数据中的形状误差消除后,可得到更加合理的表面特征参数评定结果,可用于贝壳表面形貌的评定。     

基于经验模态分解方法的曲面加工误差补偿

采用误差补偿技术对曲面的加工误差进行补偿是提高该类零件加工精度的有效方法。针对加工误差进行经验模态分解,将加工误差分解为若干个固有模态函数(IMF)和一个res趋势项函数。根据系统误差的特征,趋势项函数中一定存在系统误差,利用自相关分析法和频谱图对固有模态函数分析是否存在周期性变化系统误差,最终分解出系统误差和随机误差。搭建了数控机床在线检测的实验平台,实现了曲面零件的系统误差补偿。通过一个曲面零件的加工实验表明,补偿加工后的曲面精度提高了86.0%。仿真算例和实验结果表明,基于经验模态分解方法的加工误差补偿能有效提高曲面零件的加工精度。     

经验模态分解中虚假模态分量消除法

基于能量原理,提出经验模态分解(EMD)中虚假模态分量消除方法.在正常采样的条件下,分析信号EMD分解误差与虚假模态分量的关系,讨论虚假模态分量的性质.从EMD分解的完备性角度考虑在虚假模态存在情况下能量不守恒,给出模态函数消除法,从EMD分解结果中剔出虚假模态分量,消除信号经验模态分解误差.其有效性在所给例子中得到证实.     

均值优化经验模态分解及其在转子故障诊断中的应用

经验模态分解（Empirical mode decomposition,EMD）作为一种自适应的信号分解方法已经被广泛应用于诸多工程领域。为了提高EMD的分解性能,分别考虑从不同权值均值曲线的迭代筛分结果中选择正交性最小以及从每层内禀模态函数迭代结果中选择最优以保证整体分解最优,发展了两种均值优化经验模态分解（Mean-optimized empirical mode decomposition,MOEMD）算法。通过仿真信号分析,将MOEMD方法与EMD等现有信号分解方法进行了对比,结果表明,MOEMD方法在分解性能和分解精度方面比EMD等方法有显著提高。最后,将MOEMD方法应用于转子碰摩故障信号分析,并与EMD进行了对比分析,结果表明,MOEMD方法不仅能够有效地识别转子碰摩故障,而且识别效果优于EMD方法。     

基于能量的振动信号经验模态分解终止条件

经验模态分解(Emprical Mode Decomposition,简称EMD)主要思想是把一个时间序列的信号分解成不同尺度的本征模函数(Intrinsic Mode Function,简称IMF).EMD算法用残余量极值点数量小于2来终止分解,该终止条件有无关组分多和分解速度慢的缺点.针对该缺点,提出了采用残余能量小于设定值的EMD分解终止条件.通过对非线性和实例振动信号的试验研究表明,基于能量的EMD分解终止条件既可以减少分解的无关组分,又可以提高分解的速度.该研究成果能广泛地用于振动信号分析领域.     

经验模态分解及其在降噪方面的应用

详细介绍经验模态分解(EMD)方法,描述了EMD算法实现步骤;通过EMD分解,任何信号序列都可分解为一系列不同尺度的固有模态函数(IMF),这种分解方法是从信号本身的尺度特征出发对信号进行分解,具有良好的自适应性,非常适合对非线性非平稳信号进行分析,并列举实例证明了其有效性.同时,提出了一种基于EMD的小波阈值降噪方法...     

小波阈值降噪与经验模态分解在信号瞬时特征提取中的应用

为了在信号瞬时特征提取过程中有效降低噪声干扰影响,提出一种基于小波阈值降噪和经验模态分解(EMD)的信号瞬时特征提取方法。根据信号特征选择适合的小波阈值函数进行降噪处理,然后对降噪信号进行EMD分解,以互相关系数作为判别依据,保留含有信号瞬时特征的本征模函数(IMF),并进行Hilbert时频谱图和边际谱图分析,最终完成信号瞬时特征的提取。     

Relative vibration identification of cutter and workpiece based on improved bidimensional empirical mode decomposition

Frontiers of Mechanical Engineering - In the process of cutting, the relative vibration between the cutter and the workpiece has an important effect on the surface topography. In this study, the...     

基于多元经验模态分解的旋转机械早期故障诊断方法*

针对旋转机械早期微弱故障诊断问题,提出了基于多元经验模态分解的旋转机械早期故障诊断新方法。首先将多个加速度传感器合理布置在轴承座的关键位置,同步采集多通道振动信息;再利用多元经验模态分解同时对多通道振动信号进行自适应分解,得到一系列多元IMF分量;最后,依据峭度准则和相关系数从中选取包含故障主要信息的IMF分量进行信号重构,提取故障特征。多元经验模态分解方法克服了EMD等方法在进行多通道数据融合时缺乏理论依据的局限性。仿真信号和旋转机械故障信号的实验结果表明,该方法明显优于EEMD方法,对齿轮和滚动轴承故障的检测精度更高,可以在强背景噪声情况下更好地提取出故障冲击特征。     

Quantitative diagnosis for bearing faults by improving ensemble empirical mode decomposition

In the bearing health assessment issues, using the adaptive nonstationary vibration signal processing methods in the time-frequency domain, lead to improving of early fault detection. On the other hand, the noise and random impulses which contaminates the input data, are a major challenge in extracting fault-related features. The main goal of this paper is to improve the Ensemble Empirical mode decomposition (EEMD) algorithm and combine it with a new proposed denoising process and the higher order spectra to increase the accuracy and speed of the fault severity and type detection. The main approach is to use statistical features without using any dimension reduction and data training. To eliminate unrelated components from faulty condition, the best combination of denoising parameters based on the wavelet transform, is determined by a proposed performance index. In order to enhance the efficiency of the EEMD algorithm, a systematic method is presented to determine the proper amplitude of the additive noise and the Intrinsic Mode Functions (IMFs) selection scheme. The fault occurrence detection and the fault severity level identification are performed by the Fault Severity Index (FSI) definition based on the energy level of the Combined Fault-Sensitive IMF (CFSIMF) envelope using the central limit theorem. Also, taking the advantages of a bispectrum analysis of CFSIMF envelope, fault type recognition can be achieved by Fault Type Index (FTI) quantification. Finally, the proposed method is validated using experimental data set from two different test rigs. Also, the role of the optimum denoising process and the algorithm of systematic selection of the EEMD parameters are described regardless of its type and estimating the consistent degradation pattern.     

3D surface form error evaluation using high definition metrology

This paper presents an approach to evaluate 3D surface form error of machined surface using high definition metrology that can measure millions of data points representing the entire surface. A data preprocessing method was developed to convert the mass data into a height-encoded and position-maintained gray image. With the converted image, a modified gray level co-occurrence matrix method was adopted to extract 3D surface form error characteristics, including entropy, contrast and correlation. Entropy measures the randomness of surface height distribution. Contrast indicates the degree of surface local deviations. Correlation could be used to identify different machining techniques. These characteristics can be used with flatness together to evaluate 3D surface form error of large complex surface.     

基于经验模态分解和Teager峭度的语音端点检测

采用经验模态分解和Teager峭度的统计特性对噪声环境下的语音信号端点进行检测。利用经验模态分解获得语音信号的本征模态函数,用Teager能量算子计算每个本征模态函数的瞬时能量,并对本征模态函数进行系数—峭度计算,提取信号期望的统计特征信息实现语音端点的检测。通过自适应EMD分解和Teager能量算子的处理,这种方法可以有效地消除白噪声或有色高斯噪声的影响。通过仿真例子说明这种方法可以取得良好的端点检测效果,仿真研究结果表明用经验模态分解和Teager峭度对噪声环境下的语音端点检测是可行的和有效的,提高了检测的可靠性。     

基于中值滤波-SVD和EMD的声发射信号特征提取

针对随机噪声和脉冲干扰对经验模态分解(EMD)质量的影响,提出中值滤波和奇异值分解(SVD)联合降噪方法,并将其与EMD分解相结合形成一种新的声发射(AE)信号特征提取方法.首先对原始AE信号进行中值滤波,去除幅值较大的异常值;其次对去除异常值的信号序列进行相空间重构和SVD分解,并针对难以确定重构阶数这一问题,提出奇异值能量差分谱概念,利用谱峰的较大值位置来确定重构阶数,以进一步降噪;最后对降噪信号进行EMD分解,以本征模态函数(IMF)的能量占比作为表征各损伤信号的特征向量.数值仿真和5层胶合板损伤的实测数据表明,该方法不仅能够滤除噪声干扰,提高EMD分解的时效性和准确性,而且能够有效地提取出胶合板AE信号特征,对其损伤类型进行有效地识别.     

基于独立分量分析的EMD模态混叠消除方法研究

针对经验模态分解(empirical mode decomposition,EMD)算法中存在的模态混叠现象,提出一种应用独立分量分析进行模态混叠消除的新方法.首先应用形态学滤波方法将信号中的噪声予以消除,减少由于噪声因素造成的模态混叠现象.然后将信号进行EMD分解,获得不同的IMF分量,将存在模态混叠成分的IMF分量进行相空间重构,利用基于峭度最大化的独立分量分解算法实现混叠成分的分离,仿真分析和工程应用的结果表明,所提方法能有效地消除EMD分解过程中的模态混叠现象.     

Source separation of diesel engine vibration based on the empirical mode decomposition and independent component analysis

Vibration signals from diesel engine contain many different components mainly caused by combustion and mechanism operations,several blind source separation techniques are available for decomposing the signal into its components in the case of multichannel measurements,such as independent component analysis(ICA).However,the source separation of vibration signal from single-channel is impossible.In order to study the source separation from single-channel signal for the purpose of source extraction,the combination method of empirical mode decomposition(EMD) and ICA is proposed in diesel engine signal processing.The performance of the described methods of EMD-wavelet and EMD-ICA in vibration signal application is compared,and the results show that EMD-ICA method outperforms the other,and overcomes the drawback of ICA in the case of single-channel measurement.The independent source signal components can be separated and identified effectively from one-channel measurement by EMD-ICA.Hence,EMD-ICA improves the extraction and identification abilities of source signals from diesel engine vibration measurements.     

基于经验模态分解和BP神经网络的油气两相流流型辨识

基于经验模态分解（empidcal mode decomposition，EMD）BP神经网络，提出了油气两相流流型辨识的新方法。应用EMD将差压信号分解成不同频率尺度上的单组分之和，并提取组分的归一化能量作为流型辨识特征量。BP神经网络以这些能量特征量为输入对油气两相流不同流型（包括泡状流、塞状流、层状流、弹状流和环状流）进行分类。实验结果表明，本文提出的流型辨识方法是有效的，其中泡状流、塞状流、层状流、弹状流和环状流的辨识精度分别为100％、89．4％,93．3％、96.3％和96.9％。     

Accurate evaluation of free-form surface profile error based on quasi particle swarm optimization algorithm and surface subdivision

Although significant progress has been made in precision machining of free-form surfaces recently, inspection of such surfaces remains a difficult problem. In order to solve the problem that no specific standards for the verification of free-form surface profile are available, the profile parameters of free-form surface are proposed by referring to ISO standards regarding form tolerances and considering its complexity and non-rotational symmetry. Non-uniform rational basis spline(NURBS) for describing free-form surface is formulated. Crucial issues in surface inspection and profile error verification are localization between the design coordinate system(DCS) and measurement coordinate system(MCS) for searching the closest points on the design model corresponding to measured points. A quasi particle swarm optimization(QPSO) is proposed to search the transformation parameters to implement localization between DCS and MCS. Surface subdivide method which does the searching in a recursively reduced range of the parameters u and v of the NURBS design model is developed to find the closest points. In order to verify the effectiveness of the proposed methods, the design model is generated by NURBS and the measurement data of simulation example are generated by transforming the design model to arbitrary position and orientation, and the parts are machined based on the design model and are measured on CMM. The profile errors of simulation example and actual parts are calculated by the proposed method. The results verify that the evaluation precision of freeform surface profile error by the proposed method is higher 10%-22% than that by CMM software. The proposed method deals with the hard problem that it has a lower precision in profile error evaluation of free-form surface.