基于小波包和EMD的异步电动机轴承故障诊断方法

针对传统的异步电动机轴承故障诊断方法对于轴承的局部缺陷及早期故障的诊断效果不明显的问题,提出了一种采用小波包理论与EMD相结合的方式提取异步电动机轴承故障特征频率的方法。该方法先采用小波包理论对原始信号进行消噪及频带划分,接着采用EMD对小波分解重构得到的信号进行分解以获得固有内在模函数(IMF),最后将IMF经时频变换得到频谱图,根据故障特征频率得出诊断结果。实验结果证明,该方法可有效地提取出故障特征频率,并方便地判断出故障类型。     

基于改进阈值和小波包的轴承故障诊断方法

为了准确有效地确定滚动轴承的故障部位,提出一种轴承故障诊断的新方法。用改进的小波阈值法对轴承振动信号进行降噪处理,对去噪后的信号进行小波包分解与重构,提取各重构子带内的信号特征作为故障诊断的样本,依据各子带信号的能量分布特征判断轴承的故障部位。在MATLAB环境下对SKF6205-2RS轴承的典型故障进行了仿真研究,结果表明改进的阈值法相比于传统去噪方法有较好的去噪效果,小波包能够准确提取信号的故障特征,能够提高轴承故障检测的准确性和有效性。     

基于小波包分解和PSO-BPNN的滚动轴承故障诊断

针对现有煤矿旋转机械滚动轴承故障诊断方法存在信号有效特征提取不完全、故障诊断精度不高及效率低等问题,提出了一种基于小波包分解和粒子群优化BP神经网络的滚动轴承故障诊断方法。该方法包括信号特征提取和故障类型识别两部分:在信号特征提取部分,对采集的滚动轴承振动信号进行小波包分解,得到各子频带能量及信号总能量,经归一化处理后获得表征滚动轴承状态的特征向量;在故障类型识别部分,通过粒子群优化算法优化BP神经网络的初始权值和阈值,以加速网络收敛速度,避免陷入局部极小值。实验结果表明,该方法提高了滚动轴承故障诊断效率和准确率。     

基于双树复小波包和改进SVM的轴承故障诊断

为了提高滚动轴承内圈、滚动体、外圈等故障诊断效率,提出了将双树复小波包和支持向量机（Support Vector Machine,SVM）结合的故障诊断方法。采用双树复小波包对轴承振动信号分解和重构,提取重构信号中的故障能量特征并构造特征样本作为支持向量机诊断模型的输入。针对支持向量机的参数选取没有固定方法而导致故障诊断的准确性降低的问题,采用人工鱼群算法对支持向量机的惩罚系数和核参数进行寻优。用寻优得到的参数建立支持向量机诊断模型对特征样本进行故障诊断。仿真结果表明提出的方法不仅可以提高降噪效果从而得到滚动轴承故障振动的特征信号,而且能实现更高精度的故障诊断。     

基于EMD多模态特征融合支持向量机的故障诊断

针对非平稳时间序列信号,提出一种基于经验模态分解(EMD)的特征提取和多模态特征融合支持向量机的故障诊断方法,首先对原始信号进行EMD分解,选择能量最大的几个基本模式分量(IMF)并提取其小波包特征,获得对每个IMF独立的特征子集;然后在每个IMF特征子集中训练SVM弱分类器,并根据各特征子集对应的IMF能量权重进行加权融合,获得故障状态的强分类器,将该方法应用于6135型柴油机振动信号故障诊断中,实验结果表明了其可行性和有效性.     

基于孪生支持向量机的齿轮箱故障诊断

针对齿轮箱振动信号非平稳特性以及故障样本数据处理困难的特点,提出了基于小波包分解和孪生支持向量机的故障诊断方法。首先采集信号通过Mallat塔式算法对信号进行小波分解再重构从而获得频带能量谱,然后通过归一化的方法再提取各频带的故障诊断特征向量。并将它送入孪生支持向量机进行训练。实验表明,该方法有效提高了分类精度和鲁棒性,而且具有较高的诊断效率。     

基于小波包和HHT的轴承故障诊断研究

采用一种处理非平稳信号的新方法—希尔伯特-黄变换HHT(Hilbert-Huang Transform)来进行滚动轴承故障特征的提取.将信号先进行小波包降噪处理,然后用HHT进行信号故障特征提取.通过实验仿真和轴承故障诊断实例,对比没有进行小波包降噪而直接进行HHT的结果,证明了此方法在轴承故障诊断中的有效性.     

基于EMD多模态特征融合支持向量机的故障诊断

针对非平稳时间序列信号,提出一种基于经验模态分解(EMD)的特征提取和多模态特征融合支持向量机的故障诊断方法.首先对原始信号进行EMD分解,选择能量最大的几个基本模式分量(IMF)并提取其小波包特征,获得对每个IMF独立的特征子集;然后在每个IMF特征子集中训练SVM 弱分类器,并根据各特征子集对应的IMF能量权重进行加权融合,获得故障状态的强分类器.将该方法应用于6135型柴油机振动信号故障诊断中,实验结果表明了其可行性和有效性.

     

基于LCD和改进SVM的轴承故障诊断方法

针对滚动轴承极易损伤,振动信号表现出非线性、非平稳性等特点,提出一种基于局部特征尺度分解(LCD)和改进支持向量机(SVM)的滚动轴承故障诊断算法。首先对采集到的轴承振动信号进行LCD,分解得到一系列内禀尺度分量(ISC),通过与经验模态分解(EMD)对比研究,证明了LCD方法的优越性;然后计算所有分量的能量熵值,提取出轴承信号的敏感特征集,输入到经过遗传算法(GA)进行参数优选后的SVM识别模型进行轴承状态的诊断识别。实验研究表明,基于LCD和改进SVM的轴承诊断算法能较好地提取出轴承故障特征信息,对4种轴承状态的识别率高达90%,是一种较为有效的轴承故障诊断方法。     

改进粒子滤波在汽轮机故障诊断中的应用

针对汽轮机的振动信号容易受到较为复杂的随机噪声污染,提出了一种改进粒子滤波的振动信号降噪方法;首先建立采集振动信号的数学模型,将其作为粒子滤波的状态方程;然后利用小波分析提取采集振动信号的背景噪声,将其和状态信号一起作为观测信号,得到观测方程,把降噪问题转化成在状态空间模型下的滤波问题;由于采用序贯重要性采样的粒子滤波存在着样本退化问题,在重采样阶段采用了一种权值排序、优胜劣汰的重采样算法,就是对各粒子的归一化权值从小到大的排列顺序,并根据权值方差大小淘汰粒子,从而得到了改进的粒子滤波算法,在一定程度上解决了标准粒子滤波的退化问题;进而运用改进粒子滤波算法对振动信号进行降噪处理,降噪前信号和降噪后信号分别通过小波包分解系数求取频带能量,根据各个频带能量的变化提取故障特征向量浓缩了汽轮机振动故障的全部信息,对提取的故障特征向量应用诊断识别算法进行故障模式识别;通过对比降噪前信号和降噪后信号的故障诊断识别率,证明了改进粒子滤波在汽轮机故障诊断中的应用效果更佳。     

Only connect: teaching, technology and telesis

Abstract This paper describes an approach to the design of interactive multimedia materials being developed in a European Community project. The developmental process is seen as a dialogue between technologists and teachers. This dialogue is often problematic because of the differences in training, experience and culture between them. Conditions needed for fruitful dialogue are described and the generic model for learning design used in the project is explained.     

European Community policy and the market

Abstract This paper starts with some reflections on the policy considerations and priorities which are shaping European Commission (EC) research programmes. Then it attempts to position the current projects which seek to capitalise on information and communications technologies for learning in relation to these priorities and the apparent realities of the marketplace. It concludes that while there are grounds to be optimistic about the contribution EC programmes can make to the efficiency and standard of education and training, they are still too technology driven.     

一种自适应子融合集成多分类器方法

融合集成方法已经广泛应用在模式识别领域,然而一些基分类器实时性能稳定性较差,导致多分类器融合性能差,针对上述问题本文提出了一种新的基于多分类器的子融合集成分类器系统。该方法考虑在度量层融合层次之上通过对各类基多分类器进行动态选择,票数最多的类别作为融合系统中对特征向量识别的类别,构成一种新的自适应子融合集成分类器方法。实验表明,该方法比传统的分类器以及分类融合方法识别准确率明显更高,具有更好的鲁棒性。     

Avoiding semantic and temporal gaps in developing software intensive systems

Development of software intensive systems (systems) in practice involves a series of self-contained phases for the lifecycle of a system. Semantic and temporal gaps, which occur among phases and among developer disciplines within and across phases, hinder the ongoing development of a system because of the interdependencies among phases and among disciplines. Such gaps are magnified among systems that are developed at different times by different development teams, which may limit reuse of artifacts of systems development and interoperability among the systems. This article discusses such gaps and a systems development process for avoiding them.     

Designing economic np control charts: A programmed simulation approach

This paper presents control charts models and the necessary simulation software for the location of economic values of the control parameters. The simulation program is written in FORTRAN, requires only 10K of main storage, and can run on most mini and micro computers. Two models are presented - one describes the process when it is operating at full capacity and the other when the process is operating under capacity. The models allow the product quality to deteriorate to a further level before an existing out-of-control state is detected, and they can also be used in situations where no prior knowledge exists of the out-of-control causes and the resulting proportion defectives.     

The development of robot art

Going through a few examples of robot artists who are recognized worldwide, we try to analyze the deepest meaning of what is called “robot art” and the related art field definition. We also try to highlight its well-marked borders, such as kinetic sculptures, kinetic art, cyber art, and cyberpunk. A brief excursion into the importance of the context, the message, and its semiotics is also provided, case by case, together with a few hints on the history of this discipline in the light of an artistic perspective. Therefore, the aim of this article is to try to summarize the main characteristics that might classify robot art as a unique and innovative discipline, and to track down some of the principles by which a robotic artifact can or cannot be considered an art piece in terms of social, cultural, and strictly artistic interest. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

Explanation and prediction: an architecture for default and abductive reasoning

Although there are many arguments that logic is an appropriate tool for artificial intelligence, there has been a perceived problem with the monotonicity of classical logic. This paper elaborates on the idea that reasoning should be viewed as theory formation where logic tells us the consequences of our assumptions. The two activities of predicting what is expected to be true and explaining observations are considered in a simple theory formation framework. Properties of each activity are discussed, along with a number of proposals as to what should be predicted or accepted as reasonable explanations. An architecture is proposed to combine explanation and prediction into one coherent framework. Algorithms used to implement the system as well as examples from a running implementation are given.     

Three Process Perspectives: Organizations, Teams, and People

This paper provides the author's personal views and perspectives on software process improvement. Starting with his first work on technology assessment in IBM over 20 years ago, Watts Humphrey describes the process improvement work he has been directly involved in. This includes the development of the early process assessment methods, the original design of the CMM, and the introduction of the Personal Software Process (PSP)^SM and Team Software Process (TSP){^SM}. In addition to describing the original motivation for this work, the author also reviews many of the problems he and his associates encountered and why they solved them the way they did. He also comments on the outstanding issues and likely directions for future work. Finally, this work has built on the experiences and contributions of many people. Mr. Humphrey only describes work that he was personally involved in and he names many of the key contributors. However, so many people have been involved in this work that a full list of the important participants would be impractical.     

基于复小波噪声方差显著修正的SAR图像去噪

提出了一种基于复小波域统计建模与噪声方差估计显著性修正相结合的合成孔径雷达(Synthetic Aperture Radar,SAR)图像斑点噪声滤波方法。该方法首先通过对数变换将乘性噪声模型转化为加性噪声模型,然后对变换后的图像进行双树复小波变换(Dualtree Complex Wavelet Transform,DCWT),并对复数小波系数的统计分布进行建模。在此先验分布的基础上,通过运用贝叶斯估计方法从含噪系数中恢复原始系数,达到滤除噪声的目的。实验结果表明该方法在去除噪声的同时保留了图像的细节信息,取得了很好的降噪效果。     

How do children do mathematics with LOGO?

Abstract  This paper considers some results of a study designed to investigate the kinds of mathematical activity undertaken by children (aged between 8 and 11) as they learned to program in LOGO. A model of learning modes is proposed, which attempts to describe the ways in which children used and acquired understanding of the programming/mathematical concepts involved. The remainder of the paper is concerned with discussing the validity and limitations of the model, and its implications for further research and curriculum development.