铂铑热电偶丝在等幅载荷下的疲劳寿命的研究

文章研究了铂铑热电偶丝在等幅载荷下的疲劳寿命并得到了具体的疲劳寿命数据,也发现了影响铂铑偶丝疲劳寿命的因素。结果表明:随着偶丝铑含量的增加,偶丝的疲劳寿命降低;随着偶丝线径的变粗,偶丝的疲劳寿命降低;真空熔炼炉制造的偶丝疲劳寿命优于高频熔炼炉;水冷铜模铸造的偶丝疲劳寿命优于石墨模铸造。     

高压变频器矢量控制技术在煤矿提升机的应用分析

本文将主要对高压变频器矢量控制原理进行详细的介绍,并结合实际的例子对高压变频器矢量控制技术在煤矿提升机中的应用进行详细的分析,希望能为相关的研究提供新的思路。     

交流变频矢量控制技术在高炉探尺中的应用

探尺设备在高炉生产中起着非常重要的作用,探尺测量高炉料面的准确与否,直接影响生产人员对高炉整个生产炉况的监测和分析判断。尽管采用直流电机调速特性稳定,转矩特性好,能很好的实现探尺的应用,调试方便,但是直流电机维护量大,较为困难。而目前交流变频矢量控制技术的成熟应用,使得交流电机的某些特性和直流电机相媲美。     

改善步进电机电流响应速度的抗饱和PI控制

目的为了提高连续式包装机的生产效率,提出一种新型的抗积分饱和PI电流控制器设计方法。方法针对PI控制器积分饱和现象引起的步进电机绕组电流响应过慢问题,根据控制器输入输出状态,确定抗饱和增益值的大小,调整积分作用效果。在控制器输入变化较慢时,充分发挥积分器的作用,减小超调量;在输入变化较快时,使积分器更快地退出饱和,提高系统响应速度。结果实验结果表明,该方法较传统PI控制和反计算固定增益PI控制能更精确地控制绕组电流,有效提高了电流响应速度和减小了超调量。结论该方法使绕组电流更好地跟随参考值变化,改善了电机在不同运行状态下的动态性能。     

基于自适应阈值调整小波去噪法和HT-LMD的电流幅值时滞检测方法

为解决电流幅值时滞效果,避免电流幅值时滞问题对电网运行的质量扰动,保障电网安全可靠运行的问题,基于自适应阈值调整小波去噪法和HT-LMD提出电流幅值时滞检测方法,采用自适应阈值调整小波去噪法去除电流幅值信号中的噪声,根据正交性判据的迭代终止条件,采用HT-LMD分解去噪后的电流幅值信号获取PF分量,通过比较分析PF分量幅值特征、边际谱特征、瞬时频率特征和Hilbert-Huang谱特征,实现电流幅值时滞检测。开展电流幅值时滞检测方法仿真验证,仿真及实验结果表明：随噪声强度变化,该文方法能自适应地更新相关参数,更好地向最优值逼近;除噪效果较为显著,可很好地保存有效信号特征;能有效获取电压暂降LMD分量,具有较好的电流幅值时滞检测效果。     

直流大电流测量质量控制技术的开发与应用

本文介绍了借鉴“计量保证方案”原理并将其简化 ,应用于直流大电流计量检测体系的设计中 ,开发出了一种适合特殊环境下的直流大电流测量管理新体系及其运作机制 ,为测量过程的测量质量控制 ,简化现场量值溯源程序提供了技术支持。     

高应变状态下混凝土的非等应变幅重复受压性能试验研究

为了建立考虑高应变低周疲劳性能的混凝土受压本构规律,利用全数字化电液伺服式试验机及其位移控制加卸载功能,完成了最大应变以不同幅度超过ε₀(ε₀为与峰值应力对应的应变)的混凝土棱柱体的等应变幅多次重复受压试验(循环周数分别从624周到8931周),获得了其独特的低周疲劳特征及加卸载应力-应变规律。     

基于矢量内积法的相位差测量及其应用

使用矢量内积法直接对正弦数字序列进行处理,可以得到两个正弦信号的相位差。在理论推导的基础上,通过仿真实验和在动不平衡检测中相位的测量,验证了本方法的有效性。     

基于傅立叶分析的滑窗迭代无功电流检测方法及应用

本文讨论了基于傅立叶滑窗迭代的无功电流检测方法,并将其应用于高压电机软起动中,实验表明,该检测方法可以实时准确榆测出所需补偿的无功电流分量,使电机起动过程更加安全可靠。     

用等安匝法校准焊接电流监测仪的研究

本文介绍了一种用等安匝法校准基于罗氏线圈采样的焊接电流监测仪的方法.利用等安匝法模拟焊接时的大电流,通过测量流过多匝线圈的相对稳定的小电流的变化值,由此获得对应焊接大电流的标准值,最终扩展了校准焊接电流监测仪的电流范围.     

Robust kernel distance multivariate control chart using support vector principles

It is important to monitor manufacturing processes in order to improve product quality and reduce production cost. Statistical Process Control (SPC) is the most commonly used method for process monitoring, in particular making distinctions between variations attributed to normal process variability to those caused by ‘special causes’. Most SPC and multivariate SPC (MSPC) methods are parametric in that they make assumptions about the distributional properties and autocorrelation structure of in-control process parameters, and, if satisfied, are effective in managing false alarms/-positives and false-negatives. However, when processes do not satisfy these assumptions, the effectiveness of SPC methods is compromised. Several non-parametric control charts based on sequential ranks of data depth measures have been proposed in the literature, but their development and implementation have been rather slow in industrial process control. Several non-parametric control charts based on machine learning principles have also been proposed in the literature to overcome some of these limitations. However, unlike conventional SPC methods, these non-parametric methods require event data from each out-of-control process state for effective model building. The paper presents a new non-parametric multivariate control chart based on kernel distance that overcomes these limitations by employing the notion of one-class classification based on support vector principles. The chart is non-parametric in that it makes no assumptions regarding the data probability density and only requires ‘normal’ or in-control data for effective representation of an in-control process. It does, however, make an explicit provision to incorporate any available data from out-of-control process states. Experimental evaluation on a variety of benchmarking datasets suggests that the proposed chart is effective for process monitoring.     

基于随机脉宽调制技术的感应电机矢量控制系统研究

随机脉宽调制技术可以使逆变器输出电压的谐波成分均匀地分布在一个较宽的频率范围内，达到抑制噪声和机械振动的目的．把随机脉宽调制技术引入感应电机矢量控制系统中，保证了调速系统的高性能，同时也降低了电机的有色噪声和机械振动．     

Finite volume distance field and its application to medial axis transforms

Accurate and efficient computation of the nearest wall distance d (or level set) is important for many areas of computational science/engineering. Differential equation‐based distance/level set algorithms, such as the hyperbolic‐natured Eikonal equation, have demonstrated valuable computational efficiency. Here, in the context, as an ‘auxiliary’ equation to the main flow equations, the Eikonal equation is solved efficiently with two different finite volume approaches (the cell‐vertex and cell‐centered). The application of the distance solution is studied for various geometries. Moreover, a procedure using the differential field to obtain the medial axis transform (MAT) for different geometries is presented. The latter provides a skeleton representation of geometric models that has many useful analysis properties. As an alternative to other methods, the current d‐MAT procedure bypasses difficulties that are usually encountered by pure geometric methods (e.g. the Voronoi approach), especially in three dimensions, and provides better accuracy than pure thinning methods. It is also shown that the d‐MAT approach provides the potential to sculpt/control the MAT form for specialized solution purposes. Copyright © 2009 John Wiley & Sons, Ltd.     

Kernel distance-based robust support vector methods and its application in developing a robust K-chart

Traditional statistical process control (SPC) techniques are not applicable in many process industries due to autocorrelation among data. In addition, most conventional charts are based on the assumption that quality characteristics follow a multivariate normality assumption. Therefore, the reduction in process variability obtained through the use of SPC techniques has not been realized in the industries. However, this may not be reasonable in many real-world problems and its extension poses serious limitations. Hence, it is not only desirable, but also inevitable to have some techniques that can serve the same purpose as SPC control charts used for correlated parameters. In this paper, a robust support vector method drawn from statistical learning theory was applied to develop a multivariate control chart based on kernel distance, which is a measure of the distance between the centre of a class and the sample to be monitored. The proposed robust chart takes advantage of information extracted from in-control preliminary samples. A robust support vector method-based chart aims to solve the over fitting problems when outliers exist in the training data set. The robust support vector method makes the decision function less sensitive towards the noise and outliers. The performance of the robust chart is tested on the problem taken from the literature and the results verify the effectiveness of the chart and validate that the robust chart is better than the conventional charts when the distribution of the quality characteristics is not multivariate normal. Experiments for the problem undertaken confirm the reduction in the number of support vectors and there is significant improvement in performance when compared with the standard support vector methods.     

The vector projection of normalized interferogram differences and its application in phase-shift estimation

From three interferograms, a novel algorithm for extracting phase shifts based on the vector projection of normalized difference maps is presented. In it, subtraction and vector normalization are operated successively to obtain two normalized interferogram differences without the effect of background component. Then, the phase shift can be estimated based on the analysis and calculation of the vector projection. Without any iteration and complex calculation, this algorithm can be implemented for phase-shift range approximately being well distributed from 0 to 2π, when fringe number of interferograms is more than one. It offers a powerful tool for rapid calibration of phase shifts because of its high efficiency and easy implementation. Numerical simulations and experiments are performed to prove its validity.     

组合核函数支持向量机在水中目标识别中的应用

论文研究了支持向量机核函数构成条件以及不同核函数的特性，结合水中目标识别技术特点，提出了一种组合核函数支持向量机的方法。提取了基于小波变换的舰船辐射噪声奇异性、尺度-过零、尺度-能量特征，对水中目标进行了SVM分类识别。研究表明，基于组合核函数的支持向量机分类识别效果优于单独核函数的支持向量机识别效果。     

微细超声工作台的设计与微振幅测量

微细超声工作台是微细超声加工机床与微细超声振动辅助加工机床的关键零部件,其振幅值直接影响加工精度与加工效率。传统的振幅测量方法很难准确的测出微细超声工作台的微振幅。基于精密微三维运动平台的高分辨率,提出了一种恒力控制的微振幅测量方法,成功的测量出能精确到0.1微米的微振幅。本文首先对微细超声工作台中的微细超声振动系统进行了理论设计,然后采用基于恒力控制的超声振幅测量方法,对不同功率作用下的微细超声振动系统的振幅值进行了测量。     

Variable amplitude cyclic deformation and fatigue behaviour of stainless steel 304L including step,periodic, and random loadings

This paper discusses cyclic deformation and fatigue behaviours of stainless steel 304L and aluminium 7075‐T6 under variable amplitude loading using strain‐controlled as well as load‐controlled tests. Load sequence effects were investigated in step tests with high‐low and low‐high sequences. For stainless steel 304L, strong hardening induced by the first step of the H‐L sequence significantly affects the fatigue behaviour, depending on the test control mode used. For periodic overload tests of stainless steel 304L, hardening due to the overloads was progressive throughout life and more significant than in H‐L step tests. For aluminium 7075‐T6, no effect on deformation behaviour was observed due to periodic overloads. However, the direction of the overloads was found to affect fatigue life, as tensile overloads led to longer lives, while compressive overloads led to shorter lives. Deformation and fatigue behaviours under random loading were also studied for the two materials. To correlate a broad range of fatigue life data for a material with strong deformation history effect, such as stainless steel, it is shown that a damage parameter with both stress and strain is required. The Smith‐Watson‐Topper parameter as such a parameter is shown to correlate the data reasonably well under different control modes and loading conditions.     

运动目标低频矢量宽带谱干涉结构及其应用研究

随着安静型技术的飞速进步,舰船和潜艇的辐射噪声总源级越来越低,但其低频成分很难消除。推导了低频矢量声场双曲线方程形式的声场干涉条纹方程,建立了双曲线参数与目标运动参数的关系。分别利用射线理论和波动理论模拟了低频声场,计算了运动目标声压和水平、垂直振速的干涉谱图,采用短时傅里叶变换与霍夫变换结合的方法对干涉结构图进行处理,估计舰船目标的运动参数。仿真结果表明,利用运动目标的低频矢量宽带干涉谱可以有效估计出目标的运动参数,同时,垂直振速的干涉谱图参数估计结果较前两种谱图误差小。     

Adaptive weighted least square support vector machine regression integrated with outlier detection and its application in QSAR

In order to eliminate the influence of unavoidable outliers in training sample on a model's performance, a novel least square support vector machine regression, which combines outlier detection approach and adaptive weight value for the training sample, is proposed and named as adaptive weighted least square support vector machine regression (AWLS-SVM). Firstly, the effective robust 3σ principle is used to detect marked outliers for the training sample. Secondly, based on the training sample without marked outliers, least square support vector machine regression is employed to develop the model and the fitting error of each sample data is obtained. Thirdly, according to the fitting error of each sample data, the initial weight is calculated. The bigger the fitting error of sample data is, the smaller the weight value of the sample data. Thus, the potential outliers, which are not detected by the robust 3σ principle and have bigger fitting errors, have smaller weight values to reduce the influence of the potential outliers on the performance of model. Then, LS-SVM is applied for the weighted sample to develop the model again. Finally, via the proposed weight value iterative method, the weight values of the training sample are converged, and the model with good predicting performance is obtained. To illustrate the performance of AWLS-SVM, simulation experiment is designed to produce the training sample with marked outlier and some non-marked outliers. AWLS-SVM, AWLS-SVM without the robust 3σ principle, LS-SVM with the robust 3σ principle, LS-SVM, and radial basis function network are applied to develop the model based on the designed sample. The results show that the influence of marked and un-marked outliers on the model's performance is eliminated by AWLS-SVM, and that the predicting performance of AWLS-SVM is the best. Furthermore, the AWLS-SVM method was applied to develop the quantitative structure–activity relationships (QSAR) model of HIV-1 protease inhibitors, and the satisfactory result was obtained.