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.

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.

Global Optimization Method Using SLE and Adaptive RBF Based on Fuzzy Clustering

High fidelity analysis models,which are beneficial to improving the design quality,have been more and more widely utilized in the modern engineering design optimization problems.However,the high fidelity analysis models are so computationally expensive that the time required in design optimization is usually unacceptable.In order to improve the efficiency of optimization involving high fidelity analysis models,the optimization efficiency can be upgraded through applying surrogates to approximate the computationally expensive models,which can greately reduce the computation time.An efficient heuristic global optimization method using adaptive radial basis function（RBF） based on fuzzy clustering（ARFC） is proposed.In this method,a novel algorithm of maximin Latin hypercube design using successive local enumeration（SLE） is employed to obtain sample points with good performance in both space-filling and projective uniformity properties,which does a great deal of good to metamodels accuracy.RBF method is adopted for constructing the metamodels,and with the increasing the number of sample points the approximation accuracy of RBF is gradually enhanced.The fuzzy c-means clustering method is applied to identify the reduced attractive regions in the original design space.The numerical benchmark examples are used for validating the performance of ARFC.The results demonstrates that for most application examples the global optima are effectively obtained and comparison with adaptive response surface method（ARSM） proves that the proposed method can intuitively capture promising design regions and can efficiently identify the global or near-global design optimum.This method improves the efficiency and global convergence of the optimization problems,and gives a new optimization strategy for engineering design optimization problems involving computationally expensive models.

农业机器人采摘目标识别技术研究——基于FCM模糊聚类算法

介绍了FCM(Fuzzy C-Means)模糊聚类算法的原理,采用权重分配的方法对该算法进行了改进,通过建立模糊的相似矩阵,对目标对象的特征聚类图进行分析,并引入隶属度矩阵对FCM算法进行优化,以加快算法的迭代速度。实验结果表明:农业机器人采用该方法对农作物轮廓分割识别度较高,算法计算效率较快,验证了其可靠性,该方法可用于目标农作物的分割和目标识别。

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.

AN ANALYSIS OF THE APPLICABILITY OF FUZZY CLUSTERING IN ESTABLISHING AN INDEX FOR THE EVALUATION OF METEOROLOGICAL SERVICE SATISFACTION

An evaluation index is a prerequisite for the scientific evaluation of a public meteorological service.This paper aims to explore a technical method for determining and screening evaluation indicators.Based on public satisfaction survey data obtained in Wafangdian,China in 2010,this study investigates the suitability of fuzzy clustering analysis method in establishing an evaluation index.Through quantitative analysis of multilayer fuzzy clustering of various evaluation indicators,correlation analysis indicates that if the results of clustering were identical for two evaluation indicators in the same sub-evaluation layer,then one indicator could be removed,or the two indicators merged.For evaluation indicators in different sub-evaluation layers,although clustering reveals attribute correlations,these indicators may not be substituted for one another.Analysis of the applicability of the fuzzy clustering method shows that it plays a certain role in the establishment and correction of an evaluation index.

Water quality assessment for Ulansuhai Lake using fuzzy clustering and pattern recognition

Water quality assessment of lakes is important to determine functional zones of water use.Considering the fuzziness during the partitioning process for lake water quality in an arid area,a multiplex model of fuzzy clustering with pattern recognition was developed by integrating transitive closure method,ISODATA algorithm in fuzzy clustering and fuzzy pattern recognition.The model was applied to partition the Ulansuhai Lake,a typical shallow lake in arid climate zone in the west part of Inner Mongolia,China and grade the condition of water quality divisions.The results showed that the partition well matched the real conditions of the lake,and the method has been proved accurate in the application.

A New Algorithm for Black-start Zone Partitioning Based on Fuzzy Clustering Analysis

On the process of power system black start after an accident, it can help to optimize the resources allocation and accelerate the recovery process that decomposing the power system into several independent partitions for parallel recovery. On the basis of adequate consideration of fuzziness of black-start zone partitioning, a new algorithm based on fuzzy clustering analysis is presented. Characteristic indexes are extracted fully and accurately. The raw data matrix is made up of the electrical distance between every nodes and blackstart resources. Closure transfer method is utilized to get the dynamic clustering. The availability and feasibility of the proposed algorithm are verified on the New-England 39 bus system at last.

基于FCM聚类的模糊综合评价方法

针对综合评价过程中隶属函数建立存在主观性和随机性以及部分系统缺乏指标阈值的问题,引入模糊聚类的思想,建立基于FCM理论的评价模型。当指标阈值存在时,通过阈值确定FCM的最佳聚类中心,得到隶属度矩阵;不存在时,通过AP聚类确定FCM的初始聚类中心,改善传统算法对聚类中心初值选取的随机性;再利用改进的FCM算法对指标数据进行分级评价,得到隶属度矩阵并建立指标阈值,最后进行综合评价分析;并将该模型应用于四川某水域的水质评价中。结果表明,该模型评价结果处于单因子评价和传统模糊综合评价结果之间,其相关系数均在0.7以上,说明该模型结果具有合理性,并且能克服因单因子评价模型仅强调最坏指标和传统模糊综合评价中人为选择隶属函数而导致评价结果具有片面性和主观性的不足。

Improvements to the fuzzy mathematics comprehensive quantitative method for evaluating fault sealing

Fuzzy mathematics is an important means to quantitatively evaluate the properties of fault sealing in petroleum reservoirs.To accurately study fault sealing,the comprehensive quantitative evaluation method of fuzzy mathematics is improved based on a previous study.First,the single-factor membership degree is determined using the dynamic clustering method,then a single-factor evaluation matrix is constructed using a continuous grading function,and finally,the probability distribution of the evaluation grade in a fuzzy evaluation matrix is analyzed.In this study,taking the F1 fault located in the northeastern Chepaizi Bulge as an example,the sealing properties of faults in different strata are quantitatively evaluated using both an improved and an un-improved comprehensive fuzzy mathematics quantitative evaluation method.Based on current oil and gas distribution,it is found that our evaluation results before and after improvement are significantly different.For faults in＂best＂and＂poorest＂intervals,our evaluation results are consistent with oil and gas distribution.However,for the faults in＂good＂or＂poor＂intervals,our evaluation is not completelyconsistent with oil and gas distribution.The improved evaluation results reflect the overall and local sealing properties of target zones and embody the nonuniformity of fault sealing,indicating the improved method is more suitable for evaluating fault sealing under complicated conditions.

基于FCM-LSTM的滚动轴承多阶段寿命预测

针对滚动轴承逐渐呈现多阶段退化的退化特性,文中提出基于模糊C均值聚类(Fuzzy C-Means Clustering, FCM)和长短时记忆神经网络(Long Short-Term Memory, LSTM)的滚动轴承多阶段寿命预测方法。该方法的步骤是:使用小波包分解提取时频域特征,构建滚动轴承的健康指标;采用FCM将滚动轴承的退化过程分为多个阶段;使用LSTM对其在不同阶段的使用寿命进行预测,其预测结果可用于维修决策的制订与执行;利用开源试验数据集验证了该方法的合理性,表明了分阶段的寿命预测能有效提高预测精度。

An Airborne Radar Clutter Tracking Algorithm Based on Multifractal and Fuzzy C-Mean Cluster

For an airborne Iookdown radar, clutter power often changes dynamically about 80 dB with wide distributions as the platform moves. Therefore, clutter tracking techniques are required to guide the selection of const false alarm rate （CFAR） schemes. In this work, clutter tracking is done in image domain and an algorithm combining multifractal and fuzzy C-mean （FCM） cluster is proposed. The clutter with large dynamic distributions in power density is converted to steady distributions of multifractal exponents by the multifractal transformation with the optimum moment. Then, later, the main lobe and side lobe are tracked from the multifractal exponents by FCM clustering method.

Kernel Generalized Noise Clustering Algorithm

To deal with the nonlinear separable problem, the generalized noise clustering （GNC） algorithm is extended to a kernel generalized noise clustering （KGNC） model. Different from the fuzzy c-means （FCM） model and the GNC model which are based on Euclidean distance, the presented model is based on kernel-induced distance by using kernel method. By kernel method the input data are nonlinearly and implicitly mapped into a high-dimensional feature space, where the nonlinear pattern appears linear and the GNC algorithm is performed. It is unnecessary to calculate in high-dimensional feature space because the kernel function can do it just in input space. The effectiveness of the proposed algorithm is verified by experiments on three data sets. It is concluded that the KGNC algorithm has better clustering accuracy than FCM and GNC in clustering data sets containing noisy data.

基于FCM聚类约束的直流电阻率法与地震走时成像法二维联合反演

通过引入模糊均值聚类(FCM)模型约束函数对电阻率与速度进行约束,开展二维直流电阻率法与地震初至波走时成像法联合反演研究.在地下浅层结构勘探中,通常低电阻率的地质体具有低速特征,较高电阻率的地质体表现为较高的地震波速度.直流电阻率法因为低电阻率区域吸引电流而对其敏感,地震走时成像法因为射线集中在高波速区而对高速体敏感,因此,两者联合成像能够大幅度提高反演效果.合成数据反演表明,直流电阻率法和地震初至波走时联合反演对于两类地质体的分辨能力均有提升,能够优势互补.尤其是引入FCM模型约束进行联合反演,根据已知物性进行监督学习,进一步提高了反演质量,改善了成像模型的分辨率.

基于FCM聚类模型约束的二维初至旅行时反演

最小结构模型约束正则化二维地震初至旅行时反演中存在模型边界刻画不清的问题,尤其是地质体内射线分布稀疏的情况下,反演效果不理想。为此,引入模糊C均值(FCM)聚类模型约束函数,旨在提高反演结果对模型边界的成像精度。该约束项将先验信息作为参考聚类中心,在迭代过程通过反复修改聚类中心及每个网格单元对聚类中心的隶属度,实现对速度的自动分类。在此基础上,采用以模型灵敏度信息为依据的多重网格反演策略,以提高反演的稳定性及效果;应用简单模型讨论了FCM聚类模型约束权重、先验信息引导项权重等参数选取方案;对比无监督学习与先验信息监督学习的反演效果,后者改善了反演速度模型边界刻画模糊现象,有效提高了反演结果的分辨率;最后,通过实测数据反演,验证该方法在实际应用中的实用性和有效性。

基于FCM算法的多属性分析技术在河道砂体精细刻画中的应用——以西湖凹陷T气田为例

西湖凹陷T气田经过十多年的勘探与开发,亟需在主力层花港组内寻找潜力目标。该区为浅水三角洲沉积体系,岩性组合在空间上变化快,为了精确识别河道砂体及其边界,在海上少井条件下利用三维地震资料识别并刻画河道砂体。在等时地层划分的基础上,对目的层段进行岩石物理性质分析,通过地震沉积学的技术方法结合岩芯及测井等资料,对沉积微相做出初步判断,在此基础上提取6类48种地震属性,与砂厚及各属性之间进行相关性分析,对地震属性进行优选,将优选出的3种反映地质体边界、岩性较好的地震属性采用基于模糊C-均值(FCM)算法的多属性聚类分析,以达到数据降维、减少冗余的效果,研究分流河道沉积体系的整体展布规律。再进行多属性RGB融合显示,增强河道砂体边界的刻画,结合构造特征以及预测的砂体厚度综合分析,提出有利目标区,为后续油田滚动开发及井位部署提供依据。

面向超宽带室内定位的FCM-SSGP方法

受室内墙壁、玻璃、木门等障碍物影响,UWB室内定位中UWB信号的传播环境变为非视距环境,在该环境下定位将极大降低定位精度。现有抑制NLOS误差的方法由于复杂度较大导致定位时间过长,结合模糊C均值(FCM)聚类及稀疏谱高斯过程回归(SSGP)方法,提出一种FCM-SSGP定位方法。对接收到的信道冲击响应信号提取特征,利用FCM聚类识别NLOS信号,并根据NLOS信号传播环境的恶劣程度将NLOS信号划分为轻度NLOS信号和一般NLOS信号。使用SSGP方法分别得到2个不同信道条件下的NLOS误差,将SSGP方法得到的测距误差与FCM聚类得到的隶属度相结合作为权值,以抑制NLOS误差。实验结果表明,FCM-SSGP方法能有效降低不同障碍物带来的NLOS误差,定位误差为21.01 cm,与LS-SVM及SPGP方法相比,其定位误差均值分别提升了8.23 cm和6.73 cm,定位所需时间相比LSTM方法缩短了9.35倍,在保证高定位精度的同时降低了计算复杂度。

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.

基于自注意力机制的FCM++及其在学生评价中的应用

利用传统的专家赋权等方式对学生进行评价时,结果往往缺乏准确性。提出一种基于自注意力机制的模糊C均值聚类(FCM)算法,以注意力作为初始聚类中心的选择依据,通过引入注意力机制增强数据之间的关联性,并通过模糊C均值聚类的隶属度思想增强评价的客观性和准确性。实验结果表明,在学生评价问题中,相较于传统模糊聚类算法,提出的引入自注意力机制的FCM++算法在簇间密度和簇内方差等指标上表现更优;相较于基于粒子群的模糊聚类算法,DB指数降低了19%,Dunn指数提高了26%。