A New Method of Wind Turbine Bearing Fault Diagnosis Based on Multi-Masking Empirical Mode Decomposition and Fuzzy C-Means Clustering

Based on Multi-Masking Empirical Mode Decomposition (MMEMD) and fuzzy c-means (FCM) clustering, a new method of wind turbine bearing fault diagnosis FCM-MMEMD is proposed, which can determine the fault accurately and timely. First, FCM clustering is employed to classify the data into different clusters, which helps to estimate whether there is a fault and how many fault types there are. If fault signals exist, the fault vibration signals are then demodulated and decomposed into different frequency bands by MMEMD in order to be analyzed further. In order to overcome the mode mixing defect of empirical mode decomposition (EMD), a novel method called MMEMD is proposed. It is an improvement to masking empirical mode decomposition (MEMD). By adding multi-masking signals to the signals to be decomposed in different levels, it can restrain low-frequency components from mixing in highfrequency components effectively in the sifting process and then suppress the mode mixing. It has the advantages of easy implementation and strong ability of suppressing modal mixing. The fault type is determined by Hilbert envelope finally. The results of simulation signal decomposition showed the high performance of MMEMD. Experiments of bearing fault diagnosis in wind turbine bearing fault diagnosis proved the validity and high accuracy of the new method.

Gene Coding Sequence Identification Using Kernel Fuzzy C-Mean Clustering and Takagi-Sugeno Fuzzy Model

Sequence analysis technology under big data provides unprecedented opportunities for modern life science. A novel gene coding sequence identification method is proposed in this paper. Firstly, an improved short-time Fourier transform algorithm based on Morlet wavelet is applied to extract the power spectrum of DNA sequence. Then, threshold value determination method based on kernel fuzzy C-mean clustering is used to combine Signal to Noise Ratio (SNR) data of exon and intron into a sequence, classify the sequence into two types, calculate the weighted sum of two SNR clustering centers obtained and the discrimination threshold value. Finally, exon interval endpoint identification algorithm based on Takagi-Sugeno fuzzy identification model is presented to train Takagi-Sugeno model, optimize model parameters with Levenberg-Marquardt least square method, complete model and determine fuzzy rule. To verify the effectiveness of the proposed method, example tests are conducted on typical gene sequence sample data.

Research and Implementation of the Enterprise Evaluation Based on a Fusion Clustering Model of AHP-FCM

Traditional clustering method is easy to slow convergence speed because of high data dimension and setting random initial clustering center. To improve these problems, a novel method combining subtractive clustering with fuzzy C-means( FCM)clustering will be advanced. In the method, the initial cluster number and cluster center can be obtained using subtractive clustering. On this basis,clustering result will be further optimized with FCM. In addition,the data dimension will be reduced through the analytic hierarchy process( AHP) before clustering calculating.In order to verify the effectiveness of fusion algorithm,an example about enterprise credit evaluation will be carried out. The results show that the fusion clustering algorithm is suitable for classifying high-dimension data,and the algorithm also does well in running up processing speed and improving visibility of result. So the method is suitable to promote the use.

Kernel method-based fuzzy clustering algorithm

The fuzzy C-means clustering algorithm(FCM) to the fuzzy kernel C-means clustering algorithm(FKCM) to effectively perform cluster analysis on the diversiform structures are extended, such as non-hyperspherical data, data with noise, data with mixture of heterogeneous cluster prototypes, asymmetric data, etc. Based on the Mercer kernel, FKCM clustering algorithm is derived from FCM algorithm united with kernel method. The results of experiments with the synthetic and real data show that the FKCM clustering algorithm is universality and can effectively unsupervised analyze datasets with variform structures in contrast to FCM algorithm. It is can be imagined that kernel-based clustering algorithm is one of important research direction of fuzzy clustering analysis.

Fault Pattern Recognition based on Kernel Method and Fuzzy C-means

A method about fault identification is proposed to solve the relationship among fault features of large rotating machinery, which is extremely complicated and nonlinear. This paper studies the rotor test-rig and the clustering of data sets and fault pattern recognitions. The present method firstly maps the data from their original space to a high dimensional Kernel space which makes the highly nonlinear data in low-dimensional space become linearly separable in Kernel space. It highlights the differences among the features of the data set. Then fuzzy C-means （FCM） is conducted in the Kernel space. Each data is assigned to the nearest class by computing the distance to the clustering center. Finally, test set is used to judge the results. The convergence rate and clustering accuracy are better than traditional FCM. The study shows that the method is effective for the accuracy of pattern recognition on rotating machinery.

Abnormal State Detection of OLTC Based on Improved Fuzzy C-means Clustering

An accurate extraction of vibration signal characteristics of an on-load tap changer(OLTC)during contact switching can effectively help detect its abnormal state.Therefore,an improved fuzzy C-means clustering method for abnormal state detection of the OLTC contact is proposed.First,the wavelet packet and singular spectrum analysis are used to denoise the vibration signal generated by the moving and static contacts of the OLTC.Then,the Hilbert-Huang transform that is optimized by the ensemble empirical mode decomposition(EEMD)is used to decompose the vibration signal and extract the boundary spectrum features.Finally,the gray wolf algorithm-based fuzzy C-means clustering is used to denoise the signal and determine the abnormal states of the OLTC contact.An analysis of the experimental data shows that the proposed secondary denoising method has a better denoising effect compared to the single denoising method.The EEMD can improve the modal aliasing effect,and the improved fuzzy C-means clustering can effectively identify the abnormal state of the OLTC contacts.The analysis results of field measured data further verify the effectiveness of the proposed method and provide a reference for the abnormal state detection of the OLTC.

Analysis of Selecting Gated Community as Opening Its Micro-Inter-Road Network

The opening of gated community to expand the micro-road network in the urban traffic system is a hot topic on the urban congestion. To satisfy the demand of opening early choosing case, this paper proposed a comprehensive selecting framework on qualified communities and its appropriate opening time. Firstly, the static influential factors on internal road structure, boundary road structure and traffic flow are qualitatively analyzed. Then, an evaluation opening state index system based on describing accurately traffic flow state is obtained, which takes the opening factors into account at the boundary road network. In this structure, the modified fuzzy C-means (FCM) method calculates the fuzzy entropy weight and range of each opening states index. Finally, the simulation results show that the proposed method is capable of selecting qualified community and the optimum opening time.

Fuzzy C-Means Algorithm Based on Density Canopy and Manifold Learning

Fuzzy C-Means(FCM)is an effective and widely used clustering algorithm,but there are still some problems.considering the number of clusters must be determined manually,the local optimal solutions is easily influenced by the random selection of initial cluster centers,and the performance of Euclid distance in complex high-dimensional data is poor.To solve the above problems,the improved FCM clustering algorithm based on density Canopy and Manifold learning(DM-FCM)is proposed.First,a density Canopy algorithm based on improved local density is proposed to automatically deter-mine the number of clusters and initial cluster centers,which improves the self-adaptability and stability of the algorithm.Then,considering that high-dimensional data often present a nonlinear structure,the manifold learning method is applied to construct a manifold spatial structure,which preserves the global geometric properties of complex high-dimensional data and improves the clustering effect of the algorithm on complex high-dimensional datasets.Fowlkes-Mallows Index(FMI),the weighted average of homogeneity and completeness(V-measure),Adjusted Mutual Information(AMI),and Adjusted Rand Index(ARI)are used as performance measures of clustering algorithms.The experimental results show that the manifold learning method is the superior distance measure,and the algorithm improves the clustering accuracy and performs superiorly in the clustering of low-dimensional and complex high-dimensional data.

APPLICATION OF FUZZY CLUSTERING TECHNIQUE FOR ANALYSIS OF NORTH INDIAN OCEAN TROPICAL CYCLONE TRACKS

A fuzzy, c-means(FCM) clustering technique is explored to investigate the track of tropical cyclones over the North Indian Ocean(NIO) for the period(1976-2014). A total of fi ve clusters is objectively identifi ed based on partition index,partition coeffi cient, Dunn Index and separation index. The results obtained during analysis emphasized that each cluster has the unique features in terms of their genesis location, landfall, travel duration, trajectory, seasonality, accumulated cyclone energy and Intensity. Analysis of large scale environmental parameters, constructed preceding day of genesis show some of these parameters to be potential precursors to TC formation for almost all the clusters, most prominently, mid-tropospheric humidity, zonal wind,vorticity and outgoing long wave radiation of the main developing regions. The individual clusters have the several distinct features in their seasonal cycles.The cluster C5 shows distinct bimodal distributions where as other clusters are formed throughout the year. ENSO infl uenced the cyclone frequency in two of the fi ve clusters. The MJO is found to play an important role in the genesis of the cyclone. The post monsoon season cyclone frequency is more in MJO phase 2, 3 and 4. The technique(FCM) can be used as a guideline in terms of the probable affected zone of TC Tracks by the operational forecasters.

一种基于三角模糊数多指标信息的FCM聚类算法

针对一类具有不确定性三角模糊数多指标信息的聚类分析问题,基于传统的数值信息FCM聚类算法,提出一种新的聚类分析算法.首先描述了具有三角模糊数多指标信息的聚类分析问题,提出并证明了基于三角模糊数多指标信息的关于最优划分和最优聚类中心确定的两个定理;然后根据这两个定理,进一步给出了基于三角模糊数信息的FCM聚类算法的迭代步骤;最后通过一个算例说明了该聚类算法的具体应用.

一种基于区间数多指标信息的FCM聚类算法

针对一类具有不确定性区间数多指标信息的聚类分析问题,基于传统的数值信息FCM(fuzzyc＿means)聚类算法,提出了一种新的聚类分析算法.首先描述了具有区间数多指标信息的聚类分析问题,其次提出并证明了基于区间数多指标信息的关于最优划分和最优聚类中心确定的两个定理.然后根据提出的两个定理,进一步给出了基于区间数信息的FCM聚类算法的迭代步骤.最后,通过一个算例说明了给出的聚类算法.

一种基于区间数多指标信息的FCM聚类算法

针对一类具有不确定性区间数多指标信息的聚类分析问题,依据传统的基于数值信息的FCM聚类算法的思路,提出了一种新的聚类分析算法。文章首先描述了具有区间数多指标信息的聚类分析问题;其次给出了基于区间数多指标信息的关于最优划分和最优聚类中心确定的两个定理;然后给出了基于区间数多指标信息的FCM聚类算法的计算步骤。该算法的特点是聚类中心的表现形式为精确的数值,给出的两个定理说明了该聚类算法的收敛性。最后,通过给出一个算例说明了本文给出的聚类算法。

基于模糊连接度的FCM分割方法在医学图像分析中的应用

图像分割的一个重要应用领域是医学图像的分割。我们针对医学图像的模糊特点和实际应用的要求 ,结合模糊连接度阈值分割和模糊C均值聚类两种分割方法的优点 ,提出一种新的交互式医学图像分割方法。首先计算整幅图像的模糊连接度 ,通过阈值分割提取出感兴趣的对象 ,并将模糊连接度作为图像的冗余特征 ;然后在由冗余特征和原图像特征构成的二维聚类空间中 ,利用模糊C -均值聚类方法优化上一步骤的分割结果 ,提高分割准确度。我们以CT和MR图像为实验对象进行了验证 ,实验结果表明这是一个有效的方法。

大学生身体素质数据的FCM算法聚类及MATLAB实现

大学生身体素质的准确分类,直接关系到大学体育分组教学和选才评价的合理性、有效性。传统的模糊聚类分析法有传递闭包法、编网法等.编网法虽然直观,但必须画图,不适合编程应用;传递闭包法需要计算相似矩阵的传递闭包,其计算量随分类对象数目的增加而呈指数规律增加,不宜应用推广。为此,引入FCM算法,采用身体质量指数、肺活量、耐力素质、柔韧力量素质和速度灵巧素质等5个聚类特征量,对大学生身体素质进行模糊聚类分析,利用Xie-Beni有效性指标确定最佳的分类方式,并利用MATLAB软件编程辅助计算.实践证明,该方法操作简便,科学有效,便于应用推广。

模糊C-均值(FCM)聚类算法的改进

针对目前模糊C-均值聚类算法不适用于有噪声和样本不均衡等问题,借助改进算法AFCM和WFCM的思想,提出另一种新的聚类算法。它是AFCM和WAFCM结合的一种算法,但有着更好的健壮性和聚类效果。

基于类内类间距离量级平衡的FCM聚类算法设计

类内距离和类间距离数值量级差异性导致两类距离无法直接融合,进而影响了FCM聚类模型设计。首先,本文全面回顾了经典和改进型的FCM聚类模型,构建了类内距离和类间距离迹的关系模型,分别从类内类间距离的变化不一致性和量级差异性两个方面分析了现有FCM聚类模型的不足;其次,运用高斯核距离替代传统的欧式距离来表征类内类间距离,基于最小化类内紧凑度与类间分离度差的思想,设计了类内类间距离平衡方法,提出了一种改进的FCM聚类目标函数与算法;最后,运用算例说明了本方法的有效性和优越性。

基于K-means算法和FCM算法的聚类研究

采用K-means算法和FCM算法实现对47个城市竞争力的聚类分析,选择较为简便的聚类有效性函数用于聚类结果的检验,得到了两种有效的聚类算法的实现方式,并验证该方法的合理性。

基于FCM聚类的奖学金评定方法

传统的奖学金评定方法是按照学生总成绩的高低作为评定依据,把一个多因数的问题简单化,使它成为一个单一的问题来处理,这种方法显然不合理。针对这个问题,采用基于模糊划分的模糊C-均值方法,对学生进行奖学金评定,为评审人员提供了一种比较科学、公正的评审方法。

改进FCM模糊聚类算法对主轴箱温度测点优化分析

轮槽铣床主轴箱热变形数据样本量繁杂,利用传统的FCM模糊聚类算法对主轴箱温度测点进行分析时,需根据情况自行设置分类数,由于经验不足会使分析结果出现偏差,导致分析失效。基于以上情况,提出采用改进的FCM模糊聚类算法对主轴箱测点进行优化分析,其原理为依据主轴箱温度及热变形量,增设聚类数c的自适应目标函数,并建立了改进的FCM模糊聚类算法可靠性分析模型,基于该模型分析得到了多元回归关键测点热误差分析数据。结果显示:采用FCM聚类算法对轮槽铣床主轴箱预先布置的温度测点进行分组优化,使主轴箱的关键测温点由21个缩减至6个,且分析结果准确度较高。该方法为机床温度测点优化分析提出了新的思维路径,具有较好的应用前景。

基于进化FCM和S2FCM算法的滚动轴承故障诊断研究

在对滚动轴承故障数据进行的分类实验中应用FCM算法,就其不足进行分析。为了克服离群点的影响,提出相对模糊指数概念,并且构建了隶属度归一化修正因子。然后结合S2FCM算法提出一种无监督的FCM融合算法。分别用两个实验平台的数据对该融合算法与传统算法的有效性进行仿真实验。对比不同算法的实验结果说明,FCM融合算法可以有效的提高价值函数的收敛速度,同时聚类结果的准确率也明显优于传统算法。