基于叶尖定时的风机叶片裂纹故障识别研究

风机叶片作为风电机组的关键部件,其裂纹故障尤为常见。 裂纹的存在会导致叶片或机组出现损坏。 为此,基于叶尖 定时原理和分析方法,提出一种风机叶片裂纹故障的识别方法。 首先,依据叶尖定时原理,分析叶片在载荷作用下裂纹对叶尖 偏移的影响,建立叶尖偏移与叶尖偏移时间之间的数学模型。 其次,通过仿真分析叶片在不同状态下叶尖偏移程度,结合不同 工况参数与叶尖偏移时间之间的数学模型,识别裂纹特征信号。 最后,利用风机模拟试验台实测叶尖信号,结果表明本文所提 的识别方法对裂纹的特征信号的成功提取达到了 92% 以上,并且能够实时完成裂纹信号的提取和分析,说明此方法能够实现 裂纹故障实时识别。     

基于Log-Gabor小波相位一致的掌纹识别算法研究

本文对掌纹识别相关理论和算法进行了研究,提出了一种基于Log-Gabor小波相位一致的掌纹线特征提取方法.掌纹线特征是掌纹识别中最基本、最直观的特征,但由于掌纹线的特殊性和复杂性,如何有效提取掌纹线特征一直是掌纹识别的难点.本文从频率域角度考虑,使用Log-Gabor小波相位一致方法提取掌纹图像线特征,包括掌纹方向相位一致特征和掌纹整体相位一致特征.该方法提取的线特征可有效包含纹线的结构信息、强度信息和宽度信息,且提取的线特征比较稳定.在掌纹整体相位一致特征图像上进一步检测出具体的掌纹线,仅使用掌纹线结构特征来表示和识别掌纹,实验验证了Log-Gabor小波相位一致提取掌纹线特征用于识别的有效性.     

基于加工特征的注塑模加工费估算研究

随着塑料模具的发展,模具报价水平直接关系着模具企业的发展。注塑模加工费用的估算对模具成本评估具有重要意义。为了提高注塑模加工费估算的效率和准确度,提出了基于加工特征的注塑模加工费估算方法,并给出了加工费估算数学模型。在NX7.0平台通过图的特征识别技术,完成主要加工特征的识别和特征参数的提取。建立工艺模板完成特征到工艺的推理,进而确定零件加工方案。结合数据库技术完成了加工工时及加工费用的估算。最后通过一个底壳零件的模具实例对方法进行了验证。     

型腔零件曲面加工特征识别方法

为有效提取模具型腔的曲面加工特征,提出一种新的曲面加工特征识别方法。该方法以过渡特征为加工特征识别的线索,识别模具型腔内的过渡特征,并根据其种子面的类型和个数确定过渡特征的类型;提取不同类型过渡特征的特征边界,将过渡特征的曲面信息转换为加工特征的边界信息;利用特征边界识别型腔内加工特征的拓扑面和特征间的拓扑关系。实验表明,该方法简单、可行,能有效识别模具型腔的曲面加工特征、提取工艺规划所需的集成性信息,促进该类产品的CAD/CAM集成。     

一种复杂背景下运动扩展目标识别算法

目标识别的关键在于提取出目标有别于背景的特有的特征.在复杂背景下,灰度特征、区域特征、形状及纹理特征难以对目标进行有效的识别,而运动特征是明显的有效特征,提出了一种基于稀疏运动场的目标识别方法,首先用改进Barnard算法对相邻两帧图像进行特征点提取,然后在特征空间用块匹配的快速算法估计出稀疏运动场,再进行C均值模糊聚类识别.实验表明这种算法能对复杂背景下高速运动的扩展目标进行快速稳健的识别.     

一种复杂背景下运动扩展目标识别算法

目标识别的关键在于提取出目标有别于背景的特有的特征.在复杂背景下,灰度特征、区域特征、形状及纹理特征难以对目标进行有效的识别,而运动特征是明显的有效特征,提出了一种基于稀疏运动场的目标识别方法,首先用改进Barnard算法对相邻两帧图像进行特征点提取,然后在特征空间用块匹配的快速算法估计出稀疏运动场,再进行C均值模糊聚类识别.实验表明这种算法能对复杂背景下高速运动的扩展目标进行快速稳健的识别.     

基于实体模型的加工特征提取与识别

提出一种加工特征提取与识别的新方法。在生成有向边 -面图的基础上提取特征 ,并识别出凸起 ,凹陷和开特征     

优化数学模型的分析诊断与特征识别

介绍了对优化数学模型进行分析诊断的方法,用它可以分析诊断该模型中存在的各种问题和错误,并能识别优化数学模型的各种特征。     

八种鼠类头骨自动分科识别模型研究

本研究借助计算机技术,初步探讨鼠类头骨自动分科的识别模型。研究选择4个科的8种鼠的成年组作为研究对象,用Bugshape V1.0提取数学形态特征。对这些特征进行比值化处理后分析建模,用于鼠类的分科识别。从交叉判别结果来看,利用数学形态比值化特征建立的模型可以进行很好地识别,但要提高识别率或再增加鼠种时,需要进一步提取新特征并优化。     

基于CAXA实体设计XPr2的制造特征提取

特征识别是实现CAD/CAPP集成的重要技术,本文运用该技术,实现了从CAXA实体设计XPr2的三维实体模型中提取出制造特征,本系统分为三大模块:自动特征识别,交互特征识别,特征识别器,在文中对这些分别作了介绍。     

薄膜涂层弹性模量的表征与评价研究

因薄膜有其极小的厚度和特殊的微结构等特征,它与相同成分的块体材料相比,二者的弹性模量在数值大小、测试设备、表征方法和技术上都有一定的差异。给出了超声波测速法、弯曲法、鼓膜法和纳米压入法等六种适合于表征薄膜弹性模量的方法,研究了它们的原理和理论模型,分析和讨论了它们的特点和适用性,解释了用不同表征方法得出的弹性模量在数值上存在差异的原因。在实际测量中,应结合各表征方法和薄膜自身的特点加以灵活选用。     

面向三维CAD的装配顺序建模系统研究

研究了面向三维CAD模型的装配顺序建模的原理、步骤和方法,先归纳出常见子装配体特征,再根据这些特征进行人机交互分析以划分装配顺序;开发了基于开目三维CAD的几何模型信息和三维CAD装配仿真模块的装配顺序建模系统;给出了使用该系统实现装配顺序规划的实例。     

自然景物中树的远景特征识别的研究

在分析了目前视觉系统中两种实用运动检测方法——帧间差法和背景差法的基础上,指出树的特征识别的重要性.通过分析远景中树的图像特征,提出了远景中树的特征模型建立和相应的匹配算法.本文提及的树特征识别方法可以减少运动检测计算量并有助于对场景的粗略定位,提及的特征提取方法对具有重复纹理特征的结构建模具有普遍意义.最后,试验结果证明了该方法的可靠性和可行性.     

Nonparametric time series modelling for industrial prognostics and health management

Prognostics and health management (PHM) methods aim at detecting the degradation, diagnosing the faults and predicting the time at which a system or a component will no longer perform its desired function. PHM is based on access to a model of a system or a component using one or combination of physical or data-driven models. In physical-based models, one has to gather a lot of knowledge about the desired system and then build an analytical model of the system function of the degradation mechanism that is used as a reference during system operation. On the other hand, data-driven models are based on the exploitation of symptoms or indicators of degradations using statistical or artificial intelligence methods on the monitored system once it is operational and learn the normal behaviour. Trend extraction is one of the methods used to extract important information contained in the sensory signals, which can be used for data-driven models. However, extraction of such information from the collected data in a practical working environment is always a great challenge as sensory signals are usually multidimensional and obscured by noise. Also, the extracted trends should represent the nominal behaviour of the system as well as the health status evolution. This paper presents a method for nonparametric trend modelling from multidimensional sensory data so as to use such trends in machinery health prognostics. The goal of this work is to develop a method that can extract features representing the nominal behaviour of the monitored component, and from these features, smooth trends are extracted to represent the critical component’s health evolution over the time. The proposed method starts by multidimensional feature extraction from machinery sensory signals. Then, unsupervised feature selection on the features’ domain is applied without making any assumptions concerning the number of the extracted features. The selected features can be used to represent the nominal behaviour of the system and hence detect any deviation. Then, empirical mode decomposition algorithm is applied on the projected features with the purpose of following the evolution of data in a compact representation over time. Finally, ridge regression is applied to the extracted trend for modelling and can be used later for the remaining useful life prediction. The method is demonstrated on accelerated degradation data set of bearings acquired from PRONOSTIA experimental platform and another data set downloaded from NASA repository where it is shown to be able to extract signal trends.     

Damage classification and estimation in experimental structures using time series analysis and pattern recognition

Developed for studying long sequences of regularly sampled data, time series analysis methods are being increasingly investigated for the use of Structural Health Monitoring (SHM). In this research, Autoregressive (AR) models were used to fit the acceleration time histories obtained from two experimental structures: a 3-storey bookshelf structure and the ASCE Phase II Experimental SHM Benchmark Structure, in undamaged and limited number of damaged states. The coefficients of the AR models were considered to be damage-sensitive features and used as input into an Artificial Neural Network (ANN). The ANN was trained to classify damage cases or estimate remaining structural stiffness. The results showed that the combination of AR models and ANNs are efficient tools for damage classification and estimation, and perform well using small number of damage-sensitive features and limited sensors.     

基于优化特征的集成变量预测模型及其应用

针对传统基于变量预测模型的模式识别方法在小样本情况下识别精度较低的问题,提出一种基于递归量化特征优化选择的集成变量预测模型(EVPM).该模型采用递归量化分析对被分析信号进行特征提取,在交错式最大权值最小冗余规则下选择权重高且冗余度低的优化特征子集,引入高斯函数、径向基函数、广义回归函数建立特征变量间的复杂非线性关系模...     

MODEL OF OPERATING AND BALANCING ASSEMBLY LINE IN A MASS CUSTOMIZATION ENVIRONMENT

The successful implementation of mass customization lies on reengineering technology andmanagement methods to organize the production. Especially in assembly phase, various product con-figurations, due-time penalties and order-driven strategy challenge the traditional operation and man-agement of assembly lines. The business features and the operation pattern of assembly line based onmass customization are analyzed. And the research emphatically studies various technologic factors toimprove customer satisfaction and their corresponding implement methods in operating assembly line.     

基于ICT图象的实体建模研究

本文研究了基于 Ｉ Ｃ Ｔ 切片图象的实体建模技术，重构了产品的实体模型．针对装配体 Ｉ Ｃ Ｔ 切片图象的特点，提出了切片采样，精确识别轮廓控制点的算法，基于简单环的内外边界识别，实体建模，基于二维图形特征和上下切片特征之间的关系识别三维特征，通过以上方法提高了实体建模的精度。     

产品设计中的油泥模型制作技术研究

阐述了油泥模型在产品设计活动中对于评价设计方案可行性时的重要意义,不同设计阶段各种类别模型形式的使用方法。介绍了油泥材料的使用特性和配制工艺,相应的模型制作工具及其具体使用方法,并详细说明了油泥模型制作工艺过程中骨架、上泥、修形、预埋、精雕、面饰六个重要技术环节的操作方法,归纳出了每个环节中应注意的具体问题和相应解决方案。为学生学习油泥模型制作和研究者研究油泥模型制作工艺提供了有价值的参考材料,为进一步提高模型制作水平做出了大量的积累和有益的尝试。     

A critical study of different dimensionality reduction methods for gear crack degradation assessment under different operating conditions

Gear cracks are some of the most common faults found in industrial machinery. Identification of different gear crack levels is beneficial to assessing gear crack degradation and preventing any unexpected machine breakdowns. In this paper, redundant statistical features are extracted from binary wavelet packet transform at different decomposition levels to describe different gear crack levels. Because the dimensionality of the extracted redundant statistical parameters is high to 620, it is necessary to reduce their dimensionality prior to the use of any statistical model for intelligently identifying different gear crack levels. The major idea of dimensionality reduction is that the extracted redundant statistical features in a high-dimensional space are mapped to a few significant features in a low-dimensional space, where these significant features are used to represent different gear crack levels. As of today, there are many popular linear and non-linear dimensionality reduction methods including principal component analysis, kernel principal components analysis, Isomap, Laplacian Eigenmaps and local linear embedding. Different dimensionality reduction methods have different performances in dimensionality reduction, which can be measured by prediction accuracies of some common statistical models, such as Naive Bayes classifier, linear discriminant analysis, quadratic discriminant analysis, and classification and regression tree. Gear crack level degradation data collected from a machine in a laboratory under different operating conditions including four different motor speeds and three different loads are used to investigate performances of the linear and non-linear dimensionality reduction methods. In our case study, the results show that principal component analysis has the best performance in dimensionality reduction and it results in the highest prediction accuracies in all of the aforementioned statistical models. In other words, the linear dimensionality reduction method is better than all of the non-linear dimensionality reduction methods investigated in this paper.