基于人工免疫算法的轴线直线度误差评定

提出了一种满足最小区域条件的轴线直线度误差评定方法--人工免疫优化算法.介绍了该算法的原理, 根据最小条件建立了轴线直线度误差评定的数学模型及优化目标函数, 给出了该算法的具体实现方法.实例计算结果表明,人工免疫优化算法评定精度优于最小二乘法,能够全局寻优,而且易于实现.     

圆度误差的最小二乘法、最小包容区域法和最优函数法评定精度之比较

目的在于寻找符合最小条件的圆度误差评定方法。首先详细介绍圆度误差评定的最小二乘法、最小包容区域法和最优函数法的算法模型与实现方法;然后,在三坐标测量机上对被测圆进行采样点坐标数据提取,分别用最小二乘法、最小包容区域法和最优函数法对给定圆进行误差评定。结果表明,最小包容区域法评定精度最高,最优函数法评定精度次之,最小二乘法评定精度较低。     

改进区域搜索算法评定圆度误差

针对圆度误差评定方法中传统区域搜索算法存在很多无效搜索点的问题，提出了改进区域搜索算法(IZS),该算法引用阿基米德曲线特性改进搜索区域，简化搜索点数，提高计算效率。给出了圆度误差最小区域，最小外接和最大内切法的数学计算模型，并具体阐述了该算法的实现过程。最后通过实验对比GA,SA和PSO算法，发现IZS算法计算速度更快，精度更高；对比传统搜索算法(RZS、PZS),计算得到的精度相同的条件下(即1.282 6μm),IZS算法仅需要搜索78个点。应用于实践中，将提升回转类零件圆度误差的检测效率。     

基于遗传算法和自适应的平面线轮廓度误差评定方法

 为了解决在测量平面线轮廓度中由于存在被测轮廓与其测量基准间存在位置误差而影响评定精度的问题，提出了一种基于遗传算法和自适应的计算平面线轮廓度误差的新方法。该方法满足最小条件原理，它利用样条插值函数拟合理论轮廓，并在评定过程中能自动地实现被测轮廓与理论轮廓之间的适应性调整，从而能够分离并消除被测轮廓与其测量基准之间的位置误差对轮廓误差评定结果的影响，在遗传优化中获得全局最优解。实例计算验证了这一结果。这种算法简单明确，具有精度高、收敛速度快、易于计算机程序实现、易于推广应用等特点。     

基于改进置换算法的圆参数评定

提出了按最小包容区域法评定圆度误差的改进置换算法,利用拟合精度较高的相对代数距离法设置置换算法的起点,符合最小条件,减少迭代次数,加快计算速度,提高拟合精度.建立了圆参数评定的数学模型,设计了相应的误差评定软件,成功地应用到了微机型万能工具显微镜的测量软件上,并给出一个影像法测量光滑环规直径和圆度误差的实例,将改进置换算法的评定结果与其它评定方法进行了比较.结果表明,改进置换算法具有较高的拟合精度和计算速度.     

形状误差的优化算法

提出了一种应用优化技术计算形状误差值的方法,给出了计算直线度、平面度、圆度、圆柱度的误差值的实例。算法对误差的评定基于“最小区域法”,采用Powell优化方法进行求解。     

微分进化算法在圆度误差评定中的应用

为了精确快速计算圆度误差,提出了基于微分进化智能优化算法的最小区域圆度误差评定方法。介绍了微分进化算法的基本原理及种群初始化、变异、交叉、选择实现步骤,建立了该算法求解最小区域圆度误差的数学模型。为验证算法的有效性,进行了大量实验并与多种算法进行对比,证实了方法的评定结果不仅小于最小二乘法及标准遗传算法评定结果,精度高,而且计算结果稳定,运算速度快。实验表明:微分进化算法用于最小区域圆度误差评定有较强的自适应能力、快速全局收敛性和高稳定性,适于对高精度圆度误差的快速评定。     

基于动态改变权重粒子群算法的球度误差评定

为了实现对球形工件球度误差的精确评定,在4种球度误差评定数学模型的基础上,对文献提供的两组数据采用一种动态改变权重的粒子群算法(PSO)进行计算,这种算法在优化迭代过程中使惯性权重值随粒子的位置和目标函数的性质而更新。与基本PSO算法、最小二乘法、遗传算法和一种改进的PSO算法进行了比较。实验结果显示,相比其他方法,在最小包容区域法模型下使用动态改变权重粒子群算法得到的球度误差最小,第1组数据只需迭代30代左右,约50ms即可收敛,第2组数据收敛也很迅速,且多次实验显示其稳定性很高。因此,所提算法可精确快速地评价球度误差。     

基于分群粒子群算法的平面度误差评定研究

基于分群粒子群算法对平面度误差判定进行了研究。首先建立平面度误差评定数学模型,对平面度误差最小求解转化成对目标函数的非线性最优化问题;接着改进粒子群算法把粒子群一分为二,在不增加粒子个数和粒子维度的情况下,两个粒子群分别用来全局搜索和局部搜索,通过阈值判断早熟现象;最后给出了算法流程。实例验证结果表明:该算法具有较强的优化能力,对测试函数求解的最优解值数据波动性比较小,平面度的公差值为0.0073mm,相比LSM、DM、TPM、PSO、ABC算法公差值平均分别减少了0.0023mm,0.0025mm,0.0027mm,0.0002mm,0.0005mm,评定精度较高。     

进化策略实现圆度误差的统一评定研究

圆度误差的评定有最小区域法、最小外接圆法、最大内接圆法和最小二乘法4种方法,文中提出了将进化策略用于上述多种圆度误差的统一评定.该算法基于实数编码,采用(μ λ)选择策略和高斯变异算子,即父代种群参与竞争,算法简单、鲁棒性强,优化效率高.同时建立了进化策略评定上述圆度误差时目标函数的数学模型.最后,通过不同评价方法对圆度误差进行评定,结果证明该方法不仅能快速收敛到全局最优解,而且计算结果的稳定性好,易于在工程计量中推广使用.     

圆度误差目标函数凸凹性的研究

应用凸函数理论证明了圆度误差最小区域评定法的目标函数是二维欧氏空间R2 中的连续、不可微的凸函数 ,从而证明了目标函数的全局极小值的唯一性 ,并给出了实例     

进口圆度仪智能化改造

以Talyrond73型圆度仪为例，介绍了圆度仪智能化改造的基本原理，圆度测量软件的主要功能以及回度误差评定方法的优化，探讨了保证检测精度的若干措施，并给出了检定结果。     

基于天牛须改进粒子群算法的平面度误差评定研究

基于天牛须改进粒子群算法(BAS-PSO)对平面度误差进行了评定研究.首先,建立基于最小区域的平面度误差评定的数学模型,并将目标函数转化为非线性最优化问题;接着,在粒子群算法(PSO)的基础上,引人局部搜索能力较强的天牛须算法(BAS),加速全局搜索和局部搜索的并行计算,避免算法早熟收敛并陷入局部最优,提高平面度误差评...     

一种基于多步法的高精密主轴回转误差分离算法

论述了多步法回转误差分离算法的技术原理,提出了频域混合补偿法误差分离算法,通过分析采集到的误差数据中主轴回转误差、圆度误差等成分的频域特性,利用傅里叶变换得到误差的谐波分量,通过剔除、补偿等方式分离偏心误差、圆度误差,从而得到主轴回转误差,该算法能很好地解决谐波抑制问题。通过圆度仪在工作现场采集数据进行仿真分析,实验验证该方法的可行性,并用最小二乘法对结果进行评定。三、四、五步法得到的圆度误差评定值与厂家给出的圆度误差40 nm间的差分别为118 nm、113 nm、145 nm,而频域混合补偿法分离出的圆度误差评定值为54 nm,与40 nm仅相差14 nm,可以看出频域混合补偿法对误差的分离效果明显优于多步法的误差分离效果。     

一种改进HVD信号特征提取方法及应用研究

希尔伯特振动分解(HVD)广泛应用于风电机组、齿轮箱等旋转机械的故障诊断,然而,它有2个亟待解决的问题:一是算法的参数需要经验设置或人工试定;二是如何避免模态混叠选择敏感的本征模态函数分量。针对上述2个问题,提出一种优化的HVD改进算法,有效解决了希尔伯特振动分解的参数设置和模态混叠问题。首先用粒子群优化算法(PSO)对HVD算法的2个参数进行优化。其次,提出了一种新的评估指标—最大包络峰度均值作为PSO优化算法的目标函数,并提出采用最大包络峰度自适应地选择敏感的IMF分量。最后,对选定的重构信号进行平方包络谱分析并提取故障特征频率,以识别风电机组设备故障类型。通过模拟信号、实验信号和风电机组应用实例分析,验证了所提改进HVD方法的有效性。     

Optimizing of ANFIS for estimating INS error during GPS outages

Global positioning system (GPS) has been extensively used for land vehicle navigation systems. However, GPS is incapable of providing permanent and reliable navigation solutions in the presence of signal evaporation or blockage. On the other hand, navigation systems, in particular, inertial navigation systems (INSs), have become important components in different military and civil applications due to the recent advent of micro-electro-mechanical systems (MEMS). Both INS and GPS systems are often paired together to provide a reliable navigation solution by integrating the long-term GPS accuracy with the short-term INS accuracy. This article presents an alternative method to integrate GPS and INS systems and provide a robust navigation solution. This alternative approach to Kalman filtering (KF) utilizes artificial intelligence based on adaptive neuro-fuzzy inference system (ANFIS) to fuse data from both systems and estimate position and velocity errors. The KF is usually criticized for working only under predefined models and for its observability problem of hidden state variables, sensor error models, immunity to noise, sensor dependency, and linearization dependency. The training and updating of ANFIS parameters is one of the main problems. Therefore, the challenges encountered implementing an ANFIS module in real time have been overcome using particle swarm optimization (PSO) to optimize the ANFIS learning parameters since PSO involves less complexity and has fast convergence. The proposed alternative method uses GPS with INS data and PSO to update the intelligent PANFIS navigator using GPS/INS error as a fitness function to be minimized. Three methods of optimization have been tested and compared to estimate the INS error. Finally, the performance of the proposed alternative method has been examined using real field test data of MEMS grade INS integrated with GPS for different GPS outage periods. The results obtained outperform KF, particularly during long GPS signal blockage.     

A particle swarm‐optimized support vector machine for reliability prediction

System reliability depends on inherent mechanical and structural aging factors as well as on operational and environmental conditions, which could enhance (or smoothen) such factors. In practice, the involved dependences may burden the modeling of the reliability behavior over time, in which traditional stochastic modeling approaches may likely fail. Empirical prediction methods, such as support vector machines (SVMs), become a valid alternative whenever reliable time series data are available. However, the prediction performance of SVMs depends on the setting of a number of parameters that influence the effectiveness of the training stage during which the SVMs are constructed based on the available data set. The problem of choosing the most suitable values for the SVM parameters can be framed in terms of an optimization problem aimed at minimizing a prediction error. In this work, this problem is solved by particle swarm optimization (PSO), a probabilistic approach based on an analogy with the collective motion of biological organisms. SVM in liaison with PSO is then applied to tackle reliability prediction problems based on time series data of engineered components. Comparisons of the obtained results with those given by other time series techniques indicate that the PSO + SVM model is able to provide reliability predictions with comparable or great accuracy. Copyright © 2011 John Wiley & Sons, Ltd.     

Minimum Zone Evaluation for Roundness Error Based on Geometric Approximating Searching Algorithm

Considering the characteristics of roundness error, a new method for roundness error evaluation in the rectangular coordinates, named as Geometric Approximation Searching Algorithm, is presented in this paper. The principle and steps of the algorithm are described in detail. The mathematical formulas and program flowchart are given. At first the algorithm allocates a square and predetermines the ideal centre point as the initial reference point. The radius value of all the measured points are calculated by each corresponding vertexes of the square respectively. After each vertex of the square and the initial reference point are used as the ideal centre point to calculate the roundness error, the minimum difference of the radius is obtained. The judgment and arranged square can be done repeatedly. Finally, the roundness error value of the minimum zone circle is determined. The experimental results show that the roundness error can be evaluated effectively and accurately by using this algorithm.