基于动态光散射信号分形的颗粒测量技术研究

颗粒的动态光散射信号具有分形特征，通过对其特征值的分析，可以得到颗粒粒径信息。采用100nm、200nm、500nm和1000nm4种亚微米粒径范围的标准聚苯乙烯乳胶颗粒样品，分别求取了它们的动态光散射信号的分形维数。实验结果表明，颗粒粒径越小，对应的动态散射光分形维数越大；粒径越大，分形维数越小。从而证明了用分形维数表征颗粒粒径的可行性。与相关方法相比，分形方法具有所需的采样数据少、计算简便、适于实时测量等特点。     

关联维数分析方法在液压泵多故障诊断中的应用研究

针对液压泵故障信号非线性和非平稳性特征,提出了利用相空间重构技术和分形理论相结合的特征关联维数提取方法。该方法将液压泵不同故障模式下获取的一维振动信号重构到高维相空间,进行信息深层挖掘;通过对相空间特征信号关联维数变化规律的分析,找出对故障反映敏感的关联维数,由此进行故障识别。通过实验验证,该方法提取的关联维数能有效反映液压泵的故障特征,为液压泵多故障诊断方法的研究提供可靠的特征信息,具有良好的应用前景。     

A fractal-dimension-based signal-processing technique and its use for nondestructive testing

A fractal-dimension-based signal-processing technique has been extensively applied to various fields, but the use of the method to characterize discrete time-domain ultrasonic signals reflecting defects and any other structural-material inhomogeneities has not been fully investigated. The fractal features of the ultrasonic echoes with fractal dimensions and their implementation in nondestructive testing are investigated. In order to obtain a faithful representation of the fractal dimensions, two improved fractal dimension algorithms are presented: the box-counting method and the R/S (range/standard deviation) method. Their capabilities are evaluated with two kinds of fractal signals: the FBM (fractal Brownian motion) and WM (Weierstrass-Mandelbrot) signals. A new method to guarantee the feasibility of the calculated fractal dimensions is proposed on the basis of the analysis of the results simulated above. Then, the fractal dimensions of ultrasonic signals measured from a pipeline sample and from carbon-steel and aluminum specimens are calculated and statistically analyzed to find the fractal properties of the ultrasonic signals. The experimental results show that ultrasonic signals have the property of scale invariance that the fractal set possessed. The fractal dimension is indicative of the complexity and degree of irregularity of the waveform of an ultrasonic signal. The fractal dimensions of ultrasonic signals from various defects and microstructures are found to possess solid distribution intervals, which can be used to identify the presence of defects and the features of materials. The potential of the technique for testing defects and assessing the microstructure of materials via the use of ultrasonic echoes is revealed. The text was submitted by the authors in English.     

结构损伤的分形神经网络检测方法

利用神经网络具有强大的非线性并行处理能力以及分形几何方法不依赖于系统的数学模型的特点,将分形维数与神经网络相结合,建立了结构损伤的分形神经网络检测方法。研究结果表明,结构不同状态下的振动信号的分形维数有明显的不同,可以将分形维数作为结构损伤检测的特征量,并用神经网络将结构的不同状态模式识别出来。     

矢双谱分析及其在机械故障诊断中的应用

双谱分析由于可以有效提取信号中的非线性特征被广泛应用于转子故障诊断。但常规双谱分析是以单通道信号为研究对象,不能全面地反映转子系统的非线性特征,存在着信息遗漏的问题,而且由同一截面的两个通道信号得出的分析结论会不一致。为解决这个问题,以全矢谱分析方法为基础提出矢双谱信号分析的新方法。矢双谱是融合了同一截面上双通道信号的幅值信息而保留了各自的相位信息的全矢双谱分析方法,能够真实地反映转子运转所包含的各种信息,且能满足分析结论的一致性要求。给出矢双谱的定义与算法,通过仿真和齿轮箱故障试验,研究结果表明,该方法能够更加全面地反映信号中所包含的非线性特征信息,分析结论具有一致性和可信性,从而提高智能故障诊断的准确性。     

基于EMD和非线性峭度的齿轮故障诊断

采用经验模式分解(empirical mode decomposition,简称EMD)和非线性峭度的统计特性对振动加速度传感器获取的齿轮箱振动响应信号进行特性分析。利用EMD分解获得振动响应信号的本征模式函数,用非线性Tea-ger能量算子计算每个本征模式函数的瞬时能量,并对本征模式函数进行系数的非线性峭度计算,提取系统的特征信息。仿真结果表明,用经验模式分解和非线性峭度可实现在线监测齿轮运转工作状态,及时发现齿轮的早期故障,提高了故障检测的可靠性。     

Fractal roundness modelling of a measured profile of a cylindrical object

Recently, a fractal analysis has been used as a useful tool in the characterization of the geometry. Especially in fractal analysis, a fractal interpolation method has been widely used to estimate and analyse experimental geometry. Due to the chaotic nature of the dynamic roundness profile in round shape machining, a more desirable method must be used for the analysis of experimental data which is natural to sequential data. A fractal method which is analysed in this paper focused its objects on the scope of the fractal interpolation and fractal dimension. Also for the calculation of fractal dimension, two methods for computing the fractal dimension have been introduced and discussed. These two methods can calculate the fractal dimensions of dynamic roundness profile according to the number of data points in which the fixed data are generally lower than 120 data points. This fractal analysis shows that it is possible to analysis fractal characteristics of roundness profile that have some different geometric properties. The fractal parameters were calculated and analysed using the measured profile of workpiece after turning.     

汽车传动轴振动信号分形维数计算

讨论了确定混沌时间序列嵌入维数的平均最近邻域发散度方法，并根据从时间序列中计算分形维数的方法，计算了一组不同平衡度汽车传动轴振动信号分形维数。计算表明，汽车传动轴振动信号具有分形特征；在时间序列具有较大噪声时，平均最近邻域发散度法能很好地确定其嵌入维数，具有良好的收敛性；在其他条件不变时，传动轴的不平衡度与其振动信号的分形维数值呈线性关系，当时间序列样本点达到一定数目时，计算结果稳定性好。     

非线性裂纹转子的分形分析

首先介绍了分形的基本概念,给出了机械振动信号主要应用的分析方法。然后研究了非线性裂纹转子的弯曲振动特性,给出了进入倍周期分叉后裂纹转子的时域特性和频域特性。最后对裂纹转子特性进行分形分析,发现并定义了裂纹转子在进入分叉以前轴心轨迹随裂纹角变化的分形特性,并发现裂纹转子在进入混沌前与进入混沌以后具有相似的分形特性;发现了裂纹转子带噪声信号具有两个分维数（二重分形）的复杂特性。     

Diesel Engine Valve Clearance Fault Diagnosis Based on Features Extraction Techniques and FastICA-SVM

Numerous vibration-based techniques are rarely used in diesel engines fault diagnosis in a direct way, due to the surface vibration signals of diesel engines with the complex non-stationary and nonlinear time-varying features. To investigate the fault diagnosis of diesel engines,fractal correlation dimension, wavelet energy and entropy as features reflecting the diesel engine fault fractal and energy characteristics are extracted from the decomposed signals through analyzing vibration acceleration signals derived from the cylinder head in seven different states of valve train. An intelligent fault detector FastICA-SVM is applied for diesel engine fault diagnosis and classification.The results demonstrate that FastICA-SVM achieves higher classification accuracy and makes better generalization performance in small samples recognition. Besides,the fractal correlation dimension and wavelet energy and entropy as the special features of diesel engine vibration signal are considered as input vectors of classifier Fast ICASVM and could produce the excellent classification results.The proposed methodology improves the accuracy of feature extraction and the fault diagnosis of diesel engines.     

复杂机械结构非线性运行特征分析

提出一种适用于工程实测ICP振动加速度计信号的积分方法,该方法可由振动加速度信号得到较精确的振动速度和位移信号,实现了基于振动信号的相空间分析,有效识别了复杂机械结构的非线性时域运行特征.为了解复杂机械结构的非线性频域运行特征,还提出一种基于神经网络辨识模型的频域分析方法,该方法可用于大型振动筛的非线性运行特征分析.     

基于分形理论的机械故障诊断的应用研究

将分形的有关理论与机械故障诊断联系起来,论述了分形维数的基本概念,介绍了网格维数及其求取方法。通过对模拟信号、齿轮振动信号进行分形诊断表明,分形网格维数诊断可以模糊诊断出到底是哪一部分最有可能发生故障,证实了分形网格维数的普遍性和通用性。分形网格维数诊断方法能有效地诊断齿轮局部故障,具有广泛的应用前景。     

分形特征在湿式离合器磨合状态预测中的运用

针对湿式离合器试验过程中磨合状态的预测问题,提出利用摩擦转矩信号分形特征来预测和评价磨合状态的新方法.首先,分别阐述了摩擦转矩信号盒子维数和关联维数的计算方法;然后,在湿式离合器试验台上提取了磨合试验的摩擦转矩信号,并对信号进行了自相似性分析;最后,应用盒子维数和关联维数分别对摩擦转矩信号进行了分形研究.分析结果表明:盒子维数和关联维数都能定量描述试验过程中摩擦转矩信号的分形特征,随着磨合次数的增加,摩擦转矩信号的盒子维数和关联维数的波动范围都逐渐减小,并且最终收敛于较小的波动范围.因此,利用试验中摩擦转矩信号的分形特征能够在不开箱条件下定量地预测和评价湿式离合器的磨合状态.     

机构综合的牛顿混沌迭代方法研究

工程中的许多问题都可以转化为非线性方程组的求解问题,牛顿迭代法是重要的一维及多维的迭代技术,其迭代本身对初始点非常敏感,该敏感区是牛顿迭代法所构成的非线性离散动力系统Julia集,提出了用排斥二周期点寻找牛顿迭代函数的Julia点的求解方法,利用非线性离散系统在其Juilia集出现混沌分形现象的特点,首次提出了基于混沌的牛顿迭代的非线性方程组求解新方法。对平面曲柄一滑块机构综合进行了研究,算例表明该方法的正确性与有效性。     

The Applications of Flexible Manufacturing Technologies in Business Process Reengineering

Through radical redesign of business processes and systems, policies, and organizational structures, the business process reengineering (BPR) effort was initiated in the manufacturing industry to seek performance breakthroughs. This paper describes a novel approach to the BPR, which applies flexible manufacturing systems (FMSs) design and analysis technologies, such as simulation, multicriteria decision support, and artificial intelligence (AI). These technologies are integrated to design and analyze specific FMS models related to the proposed technical and managerial changes in an industrial case. First, the literature is reviewed to obtain an understanding of the BPR concept and the role of FMS design and analysis in BPR. Second, a decision-making support system is developed to illustrate how the FMS design and analysis would affect BPR. Finally, a summary of the integrated approach practice in industry and conclusions are presented. The paper shows that the key to a successful BPR approach is the identification and analysis of specific proposed models. It also demonstrates that the integrated approach enables engineers to improve the efficiency of BPR.     

长度分形的风力机叶片在线检测与故障诊断

应用有限元分析方法,建立300w小型风力发电机组的动力学模型,通过模态分析及动力学仿真,提取风力机叶片的固有频率、振型及动力响应.应用非线性动力系统长度分形维理论,计算叶片正常运行和故障运行的动态特性数据及其长度分形维数,并作为叶片在线检测与故障识别的特征量.     

核函数主元分析及其在齿轮故障诊断中的应用

提出了基于核函数主元分析的齿轮故障诊断方法。该方法通过计算齿轮振动信号原始特征空间的内积核函数来实现原始特征空间到高维特征空间的非线性映射。通过对高维特征数据作主元分析,得到原始特征的非线性主元,以所选的非线性主元作为特征子空间对齿轮工作状态进行分类识别。用齿轮在正常状态、裂纹状态和断齿状态下的试验数据对该方法进行了检验,比较了主元分析与核函数主元分析的分类效果。结果表明,核函数主元分析能有效的检测裂纹故障的出现,正确区分不同的故障模式,更适于提取故障信号的非线性特征。     

基于分形和小波包理论的滚动轴承故障诊断

为了提高滚动轴承故障分形诊断的准确性,利用仿真信号对不同数据长度和不同信噪比下信号的盒维数和关联维数的差异进行对比,发现两种分形维对不同信号具有不同适应性;利用基于小波包分解能量图的特征信号强化技术,突出含噪轴承振动信号的故障信息特征,并对消噪前后振动信号盒维数进行计算和对比。分析结果表明,分形盒维数比关联维数更适用于分析含噪较重的信号;滚动轴承故障振动信号盒维数小于正常信号盒维数;相比原始信号,经小波包提取后不同类型故障振动信号的盒维数区分更为明显,诊断结果更加准确直观。     

基于混沌的全局优化新方法

牛顿优化技术是重要的一维及多维优化迭代技术,其迭代本身对初始点非常敏感,该敏感区是牛顿优化技术所构成的非线性离散动力系统Julia集,提出一种寻找牛顿优化迭代函数的Julia点的求解方法,利用非线性离散动力系统在其Julia集出现混沌分形现象的特点,提出一种基于牛顿优化技术的全局优化新方法,数值试验表明了该方法的有效性和正确性。