FCM属性约简方法在汽轮机故障诊断中的应用

文章通过汽轮机故障样本数据的空间分布状态分析,基于模糊c均值(FCM)聚类方法实现故障征兆参数属性的离散化处理;采用粗糙集(RS)属性约简方法对原始特征向量进行优化,去除冗余特征,提取特征向量的本质信息;基于约简后的特征向量建立支持向量机(SVM)故障诊断模型.实验结果表明,该文所提出的方法不仅可以提高故障诊断的准确性...     

EEMD排列熵与PCA-GK的滚动轴承聚类故障诊断

针对滚动轴承故障诊断中,用振动信号的总体经验模式分解(EEMD)方法分解后的熵特征向量维数高,且样本熵(SE)计算效率差等问题,提出了一种基于EEMD排列熵(PE)的主成分分析(PCA)-GK滚动轴承聚类故障诊断组合方法。首先,使用EEMD方法将信号分解为若干个固有模态函数(IMFs),使用PE/SE计算其IMFs熵值;然后,使用PCA对熵特征向量进行可视化降维,并作为模糊C均值(FCM)与GK聚类算法的输入,实现对滚动轴承的故障诊断。利用分类系数和平均模糊熵,对聚类结果进行了评价与对比,实验结果表明:本文模型(EEMD-PE-PCA-GK)的聚类效果比其他3种模型(EEMD-SE-PCA-FCM、EEMD-SE-PCA-GK和EEMDPE-PCA-FCM)更好,且PE比SE的计算效率更快。     

基于SS/OSF实现高维稀疏数据对象的聚类

为了解决传统聚类方法处理高维稀疏数据对象时聚类结果不理想的问题,提出了SS/OSF聚类方法.该方法基于对象组相似度(SS)和对象组特征向量(OSF),并借助对象组特征向量的可加性实现. 采用本方法得到高维稀疏数据对象的聚类结果后,可以根据聚类结果中各个对象集合的上确界和下确界为新对象进行对象组分类. 实验表明,与传统K-means聚类方法相比,随着数据对象数目的增加,该方法无论是在运行时间上,还是在聚类结果的准确度方面都有明显的改进.     

基于变分模态分解和改进模糊支持向量机的汽轮机故障诊断方法

针对现有方法在汽轮机故障诊断中存在影响诊断结果的样本、诊断准确率不高等缺陷,提出了基于变分模态分解(VMD)和改进的模糊支持向量机(FSVM)相结合的故障诊断模型;采用VMD对信号进行分解,并计算出各模态分量的样本熵值作为特征值,构造特征向量;通过核模糊C均值(KFCM)聚类算法计算出不同特征向量的模糊隶属度值,并将其引入支持向量机(SVM)中形成FSVM故障诊断识别模型,同时采用粒子群算法(PSO)优化FSVM中的参数;通过本特利RK4型转子试验台模拟汽轮机故障,将得到的数据输入诊断模型中进行实验。结果表明,在保证诊断模型性能的前提下,与传统SVM相比,基于VMD和改进的FSVM方法可以有效提高汽轮机故障诊断的准确率,而且诊断时间更短。     

基于ITD和模糊聚类的齿轮箱故障诊断方法

为提高齿轮箱故障诊断的准确性与效率,针对其振动信号非线性和非平稳性的特点,提出将固有时间尺度分解(ITD)和模糊聚类(FCM)相结合的齿轮箱故障诊断方法。首先对齿轮箱振动信号进行固有时间尺度分解,提取包含主要故障信息的前4个固有旋转分量(PRC),求取PRC的特征能量作为故障特征向量。然后利用模糊C-均值聚类算法对齿轮箱故障进行识别与诊断,并将该方法应用到现场齿轮箱的诊断中。结果表明,诊断结果与实际情况完全相符,该方法比经验模式分解与模糊聚类相结合的方法具有更高的计算速度和精度,为齿轮箱故障诊断提供了一种新的有效方法。     

融合Nystr(o)m方法的谱聚类算法(NSDcut)的图像分割

为了克服谱聚类算法SDcut在计算相似度矩阵和拉普拉斯矩阵的特征值和特征向量时,比较耗时的缺点,提出了融合Nystr(o)m方法的SDcut算法NSDcut,并应用于图像分割.该算法采用Nystr(o)m方法构建相似度矩阵和计算相似度矩阵的特征向量,并用这些特征向量通过矩阵运算降低了SDcut算法中的相似度矩阵和拉普拉斯矩阵的阶,从而降低SDcut算法的时间复杂度.实验结果表明:NSDcut算法提高了SDcut算法的运行速度,同时也具有SDcut算法的聚类性能.     

一种基于改进辛几何模态分解的复合故障诊断方法

针对辛几何模态分解方法分析结果的不确定性,提出一种改进的辛几何模态分解方法.首先对原时间序列进行相空间变换,获得轨迹矩阵;然后通过辛几何相似变换求得特征值和对应的特征向量,并通过对角平均得到一系列的初始辛几何分量;最后采用层次聚类方法对初始辛几何分量进行自适应重组,进而得到最终的聚类辛几何分量.实验结果表明:改进的辛几何模态分解方法可以有效地对旋转机械复合故障信号进行特征提取,提高故障诊断的准确性.     

基于余弦定理和K-means的植物叶片识别方法

为了提高植物叶片识别准确率,提出一种基于余弦定理和K-means的识别方法.该方法首先通过提取叶片的Hu不变矩和形状特征得到叶片的综合特征向量,然后使用K均值聚类(Kmeans)对各类叶片训练样本的特征向量集合进行聚类以获得聚类中心特征向量,紧接着使用余弦定理计算目标叶片和训练样本的相似度并排序.仿真实验表明:在Flavia植物叶片数据库中进行测试,该文方法以96.03%的概率在前5位发现目标,优于KNN、BP神经网络方法,因此,该方法具有一定的实用价值.     

基于声学特征的埋地管道堵塞故障的聚类识别方法

针对在实际工程应用中埋地排水管道不同程度的堵塞难以检测的问题,提出了一种基于声学特征的管道堵塞故障类型的聚类识别方法.首先对管道中采集的声学信号进行声压级变换,以增加不同故障类型之间的区分度,其次对声压级信号进行总体平均经验模态分解(Ensemble Empirical Mode Decomposition,EEMD),利用皮尔逊相关系数选取前4个IMF分量并提取中心频率和能量占比作为聚类特征,之后采用主成分分析法(Principal Component Analysis,PCA)对特征向量进行降维,最后通过FCM((Fuzzy C-means))和GK(GustafsonKessel),GG(Gath-Geva)3种聚类学习方法对特征向量进行堵塞故障的聚类和识别.实验结果表明,该方法能有效识别排水管道内不同程度的堵塞故障,具有一定的工程实用价值.     

一种混合智能故障诊断方法及柴油机故障诊断

提出了一种模糊聚类、粗糙集理论与神经网络集成的混合智能故障诊断方法。引入聚类有效性函数和点分布密度函数。对模糊c-均值聚类算法进行改进,形成了自适应模糊聚类算法并依据该算法将连续的故障特征值离散化。应用粗糙集理论处理离散化的故障诊断数据。采用基于信息熵的方法,约简冗余的故障特征。依据约简结果构建神经网络,采用遗传算法优化网络的权值和阈值。将该方法用于柴油机气门故障诊断,并与普通神经网络进行对比。结果表明,该方法提高了故障诊断的正确率。     

Characterization of Ordovician carbonate reservoirs, southeastern Saskatchewan, Canada

The discovery of the prolific Ordovician Red River reservoirs in 1995 in southeastern Saskatchewan was the catalyst for extensive exploration activity which resulted in the discovery of more than 15 new Red River pools. The best yields of Red River production to date have been from dolomite reservoirs. Understanding the processes of dolomitization is, therefore, crucial for the prediction of the connectivity, spatial distribution and heterogeneity of dolomite reservoirs.The Red River reservoirs in the Midale area consist of 3～4 thin dolomitized zones, with a total thickness of about 20 m, which occur at the top of the Yeoman Formation. Two types of replacement dolomite were recognized in the Red River reservoir: dolomitized burrow infills and dolomitized host matrix. The spatial distribution of dolomite suggests that burrowing organisms played an important role in facilitating the fluid flow in the backfilled sediments. This resulted in penecontemporaneous dolomitization of burrow infills by normal seawater. The dolomite in the host matrix is interpreted as having occurred at shallow burial by evaporitic seawater during precipitation of Lake Almar anhydrite that immediately overlies the Yeoman Formation. However, the low δ18O values of dolomited burrow infills (-5.9‰～ -7.8‰, PDB) and matrix dolomites (-6.6‰～ -8.1‰, avg. -7.4‰ PDB) compared to the estimated values for the late Ordovician marine dolomite could be attributed to modification and alteration of dolomite at higher temperatures during deeper burial, which could also be responsible for its 87Sr/86Sr ratios (0.7084～0.7088) that are higher than suggested for the late Ordovician seawaters (0.7078～0.7080). The trace amounts of saddle dolomite cement in the Red River carbonates are probably related to "cannibalization" of earlier replacement dolomite during the chemical compaction.     

An Index mechanism for multi-scale view in spatial databases

There are numerous geometric objects stored in the spatial databases. An importance function in a spatial database is that users can browse the geometric objects as a map efficiently. Thus the spatial database should display the geometric objects users concern about swiftly onto the display window. This process includes two operations:retrieve data from database and then draw them onto screen. Accordingly, to improve the efficiency, we should try to reduce time of both retrieving object and displaying them. The former can be achieved with the aid of spatial index such as R-tree, the latter require to simplify the objects. Simplification means that objects are shown with sufficient but not with unnecessary detail which depend on the scale of browse. So the major problem is how to retrieve data at different detail level efficiently. This paper introduces the implementation of a multi-scale index in the spatial database SISP (Spatial Information Shared Platform) which is generalized from R-tree. The difference between the generalization and the R-tree lies on two facets: One is that every node and geometric object in the generalization is assigned with a importance value which denote the importance of them, and every vertex in the objects are assigned with a importance value,too. The importance value can be use to decide which data should be retrieve from disk in a query. The other difference is that geometric objects in the generalization are divided into one or more sub-blocks, and vertexes are total ordered by their importance value. With the help of the generalized R-tree, one can easily retrieve data at different detail levels.Some experiments are performed on real-life data to evaluate the performance of solutions that separately use normal spatial index and multi-scale spatial index. The results show that the solution using multi-scale index in SISP is satisfying.     

A computer generator for randomly layered structures

AcomputergeneratorforrandomlylayeredstructuresYUJia shun1,2,HEZhen hua2(1.TheInstituteofGeologicalandNuclearSciences,NewZealand;2.StateKeyLaboratoryofOilandGasReservoirGeologyandExploitation,ChengduUniversityofTechnology,China)Abstract:Analgorithmisintrod…     

海南岛地体及其毗邻陆缘晚中生代—新生代古地磁研究和构造演化

本文叙述了对海南岛及其毗邻大陆边缘白垩纪到第四纪地层岩石进行古地磁研究的全部工作过程。通过分析岩石中剩余磁矢量的磁偏角及磁倾角的变化,提出海南岛白垩纪以来经历的构造演化模式如下:早期伴随顺时针旋转而向南迁移,后期伴随逆时针转动并向北运移。联系该地区及邻区的地质、地球物理资料,对海南岛上述的构造地体运动提出以下认识:北部湾内早期有一拉张作用,主要是该作用使湾内地壳显著伸长减薄,形成北部湾盆地。从而导致了海南岛的早期构造运动,而海南岛后期的构造运动则主要是受南海海底扩张的影响。海南地体运动规律的阐明对于了解北部湾油气盆地的形成演化有重要的理论和实际意义。     

Exciton Seebeck in Molecular Systems

Various applications relevant to the exciton dynamics,such as the organic solar cell,the large-area organic light-emitting diodes and the thermoelectricity,are operating under temperature gradient.The potential abnormal behavior of the exicton dynamics driven by the temperature difference may affect the efficiency and performance of the corresponding devices.In the above situations,the exciton dynamics under temperature difference is mixed with     

Achieving Unusual States of Matter Under High Pressure

The elongation method,originally proposed by Imamura was further developed for many years in our group.As a method towards O(N)with high efficiency and high accuracy for any dimensional systems.This treatment designed for one-dimensional(ID)polymers is now available for three-dimensional(3D)systems,but geometry optimization is now possible only for 1D-systems.As an approach toward post-Hartree-Fock,it was also extended to     

《Chinese Science Bulletin》SUBJECT INDEX TO VOLUME 59(2014)

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An on-line scaling method for improving scalability of a database cluster

The explosive growth of the Internet and database applications has driven database to be more scalable and available, and able to support on-line scaling without interrupting service. To support more client's queries without downtime and degrading the response time, more nodes have to be scaled up while the database is running. This paper presents the overview of scalable and available database that satisfies the above characteristics. And we propose a novel on-line scaling method. Our method improves the existing on-line scaling method for fast response time and higher throughputs. Our proposed method reduces unnecessary network use, i.e. , we decrease the number of data copy by reusing the backup data. Also, our on-line scaling operation can be processed parallel by selecting adequate nodes as new node. Our performance study shows that our method results in significant reduction in data copy time.     

An improved R-tree based on childnode''s probability

R-Tree is a good structure for spatial searching. But in this indexing structure,either the sequence of nodes in the same level or sequence of traveling these nodes when queries are made is random. Since the possibility that the object appears in different MBR which have the same parents node is different, if we make the subnode who has the most possibility be traveled first, the time cost will be decreased in most of the cases. In some case, the possibility of a point belong to a rectangle will shows direct proportion with the size of the rectangle. But this conclusion is based on an assumption that the objects are symmetrically distributing in the area and this assumption is not always coming into existence. Now we found a more direct parameter to scale the possibility and made a little change on the structure of R-tree, to increase the possibility of founding the satisfying answer in the front sub trees. We names this structure probability based arranged R-tree (PBAR-tree).     

Integrating GIS Web services based on mediating architecture

The geographic information service is enabled by the advancements in general Web service technology and the focused efforts of the OGC in defining XML-based Web GIS service. Based on these models, this paper addresses the issue of services chaining,the process of combining or pipelining results from several interoperable GIS Web Services to create a customized solution. This paper presents a mediated chaining architecture in which a specific service takes responsibility for performing the process that describes a service chain. We designed the Spatial Information Process Language (SIPL) for dynamic modeling and describing the service chain, also a prototype of the Spatial Information Process Execution Engine (SIPEE) is implemented for executing processes written in SIPL. Discussion of measures to improve the functionality and performance of such system will be included.