Fault Diagnosis Model Based on Fuzzy Support Vector Machine Combined with Weighted Fuzzy Clustering

A fault diagnosis model is proposed based on fuzzy support vector machine (FSVM) combined with fuzzy clustering (FC).Considering the relationship between the sample point and non-self class,FC algorithm is applied to generate fuzzy memberships.In the algorithm,sample weights based on a distribution density function of data point and genetic algorithm (GA) are introduced to enhance the performance of FC.Then a multi-class FSVM with radial basis function kernel is established according to directed acyclic graph algorithm,the penalty factor and kernel parameter of which are optimized by GA.Finally,the model is executed for multi-class fault diagnosis of rolling element bearings.The results show that the presented model achieves high performances both in identifying fault types and fault degrees.The performance comparisons of the presented model with SVM and distance-based FSVM for noisy case demonstrate the capacity of dealing with noise and generalization.

Turbopump Condition Monitoring Using Incremental Clustering and One-class Support Vector Machine

Turbopump condition monitoring is a significant approach to ensure the safety of liquid rocket engine （LRE）.Because of lack of fault samples,a monitoring system cannot be trained on all possible condition patterns.Thus it is important to differentiate abnormal or unknown patterns from normal pattern with novelty detection methods.One-class support vector machine （OCSVM） that has been commonly used for novelty detection cannot deal well with large scale samples.In order to model the normal pattern of the turbopump with OCSVM and so as to monitor the condition of the turbopump,a monitoring method that integrates OCSVM with incremental clustering is presented.In this method,the incremental clustering is used for sample reduction by extracting representative vectors from a large training set.The representative vectors are supposed to distribute uniformly in the object region and fulfill the region.And training OCSVM on these representative vectors yields a novelty detector.By applying this method to the analysis of the turbopump＇s historical test data,it shows that the incremental clustering algorithm can extract 91 representative points from more than 36 000 training vectors,and the OCSVM detector trained on these 91 representative points can recognize spikes in vibration signals caused by different abnormal events such as vane shedding,rub-impact and sensor faults.This monitoring method does not need fault samples during training as classical recognition methods.The method resolves the learning problem of large samples and is an alternative method for condition monitoring of the LRE turbopump.

A Hierarchical Clustering and Fixed-Layer Local Learning Based Support Vector Machine Algorithm for Large Scale Classification Problems

It is a challenging topic to develop an efficient algorithm for large scale classification problems in many applications of machine learning. In this paper, a hierarchical clustering and fixed- layer local learning （HCFLL） based support vector machine（SVM） algorithm is proposed to deal with this problem. Firstly, HCFLL hierarchically dusters a given dataset into a modified clustering feature tree based on the ideas of unsupervised clustering and supervised clustering. Then it locally trains SVM on each labeled subtree at a fixed-layer of the tree. The experimental results show that compared with the existing popular algorithms such as core vector machine and decision.tree support vector machine, HCFLL can significantly improve the training and testing speeds with comparable testing accuracy.

Short-Term Wind Power Prediction Using Fuzzy Clustering and Support Vector Regression

A sustainable production of electricity is essential for low carbon green growth in South Korea. The generation of wind power as renewable energy has been rapidly growing around the world. Undoubtedly, wind energy is unlimited in potential. However due to its own intermittency and volatility, there are difficulties in the effective harvesting of wind energy and the integration of wind power into the current electric power grid. To cope with this, many works have been done for wind speed and power forecasting. In this paper, an SVR （support vector regression） using FCM （Fuzzy C-Means） is proposed for wind speed forecasting. This paper describes the design of an FCM based SVR to increase the prediction accuracy. Proposed model was compared with ordinary SVR model using balanced and unbalanced test data. Also, multi-step ahead forecasting result was compared. Kernel parameters in SVR are adaptively determined in order to improve forecasting accuracy. An illustrative example is given by using real-world wind farm dataset. According to the experimental results, it is shown that the proposed method provides better forecasts of wind power.

A Kernel Clustering Algorithm for Fast Training of Support Vector Machines

A new algorithm named kernel bisecting k-means and sample removal(KBK-SR) is proposed as sampling preprocessing for support vector machine(SVM) training to improve the efficiency.The proposed algorithm tends to quickly produce balanced clusters of similar sizes in the kernel feature space,which makes it efficient and effective for reducing training samples.Theoretical analysis and experimental results on three UCI real data benchmarks both show that,with very short sampling time,the proposed algorithm dramatically accelerates SVM sampling and training while maintaining high test accuracy.

A Fast Algorithm for Support Vector Clustering

Support Vector Clustering (SVC) is a kernel-based unsupervised learning clustering method. The main drawback of SVC is its high computational complexity in getting the adjacency matrix describing the connectivity for each pairs of points. Based on the proximity graph model [3], the Euclidean distance in Hilbert space is calculated using a Gaussian kernel, which is the right criterion to generate a minimum spanning tree using Kruskal's algorithm. Then the connectivity estimation is lowered by only checking the linkages between the edges that construct the main stem of the MST (Minimum Spanning Tree), in which the non-compatibility degree is originally defined to support the edge selection during linkage estimations. This new approach is experimentally analyzed. The results show that the revised algorithm has a better performance than the proximity graph model with faster speed, optimized clustering quality and strong ability to noise suppression, which makes SVC scalable to large data sets.

基于ERF和BO-SVC的交流接触器触头故障识别方法

针对交流接触器各状态样本不均衡导致故障状态识别精度低和特征冗余度高的问题,文中提出一种基于嵌入式随机森林(embedded random forest,ERF)和贝叶斯优化非线性支持向量机(Bayesian optimization-support vector classification,BO-SVC)的复合识别方法。首先,通过交流接触器全寿命试验平台提取接触器状态特征,并针对各状态样本间不均衡导致识别精度低现象,提出一种基于权重法的样本均衡处理策略。然后,使用ERF对均衡后样本进行特征选择和降维,提取最能表征触头状态变化规律的最优特征。最后,将最优特征输入到BO-SVC识别模型,与另外2种代表性模型作为对比,以精确率、召回率和F1-分数3个指标对各模型性能进行评估。在3个指标上,文中方法的结果分别达到95.22%、98.91%和97.01%,均高于对比模型。以F1-分数为指标,在4组样本上对各模型性能进行测试,结果表明文中方法的F1-分数平均高出对比模型0.56%和27.28%,验证文中研究有效解决了交流接触器特征冗余和故障识别精度低的问题。

基于SVC的电动汽车集群并网鲁棒优化调度模型

针对电动汽车(electric vehicle,EV)入网时长和荷电状态(state of charge,SOC)的不确定性,提出基于支持向量聚类(support vector clustering,SVC)的电动汽车集群并网鲁棒优化调度模型。以EV的充放电功率作为决策变量,用户最小充电成本为目标函数,建立集群EV调度模型。利用EV历史充电数据,以包含所有样本数据的最小超球体作为不确定集形状,将广义直方图交叉核作为核函数,计算EV入网时间和充电时长参数的不确定集,建立基于SVC的集群EV鲁棒优化调度模型。算例分析结果表明,所提方法能更准确地描述EV充电的不确定性参数,所提模型在保证经济性的同时能迅速响应分时电价,具有较好的实用性。

基于VMD-MD-Clustering方法的航班延误等级分类

针对航班数量逐年增加导致的航班延误日益频繁问题,研究对航班延误等级分类的方法,从而为制定针对性措施,降低航班延误造成的损失提供理论基础。从时间、空间和效率3个方面确定航班延误时间、航班飞行时间、延误影响人数和航程这4个数值属性指标,以及过站是否经停、飞机载客量2个类属性指标,共计6个评估指标构建航班延误等级分类模型。提出了1种基于变分模态分解(VMD)、马氏深度(MD)函数和K-means数据聚类(Clustering)的航班延误等级分类方法(以下简称V-M-C方法)。V-M-C方法将非正态、非平稳的多维航班延误数据视作含噪声的信号序列进行处理,通过VMD降噪获得正态、稳定的多维信号数据;利用MD函数进行降维处理得到一维的稳定信号数据;使用K-means方法对得到的一维数据进行聚类,对航班延误等级分类。为确定航班延误等级分类精确性,采用带惩罚权重的支持向量机(SVM)对分类结果进行分析,可以在一定程度上提高V-M-C方法的普适性。以某大型枢纽机场某月的航班运行数据为例,只使用K-means算法的航班延误等级分类精度为81.9%,而V-M-C方法对航班延误等级分类精度可提升至95.41%。实验结果表明,V-M-C方法的分类准确率更高,能够帮助机场根据相应延误等级制定预案,保障航班整体运行正点率。

Method of Modulation Recognition Based on Combination Algorithm of K-Means Clustering and Grading Training SVM

For the existing support vector machine, when recognizing more questions, the shortcomings of high computational complexity and low recognition rate under the low SNR are emerged. The characteristic parameter of the signal is extracted and optimized by using a clustering algorithm, support vector machine is trained by grading algorithm so as to enhance the rate of convergence, improve the performance of recognition under the low SNR and realize modulation recognition of the signal based on the modulation system of the constellation diagram in this paper. Simulation results show that the average recognition rate based on this algorithm is enhanced over 30% compared with methods that adopting clustering algorithm or support vector machine respectively under the low SNR. The average recognition rate can reach 90% when the SNR is 5 dB, and the method is easy to be achieved so that it has broad application prospect in the modulating recognition.

Discussion About Nonlinear Time Series Prediction Using Least Squares Support Vector Machine

The least squares support vector machine (LS-SVM) is used to study the nonlinear time series prediction. First, the parameter gamma and multi-step prediction capabilities of the LS-SVM network are discussed. Then we employ clustering method in the model to prune the number of the support values.. The learning rate and the capabilities of filtering noise for LS-SVM are all greatly improved.

A NOVEL SVM ENSEMBLE APPROACH USING CLUSTERING ANALYSIS

A novel Support Vector Machine(SVM) ensemble approach using clustering analysis is proposed. Firstly,the positive and negative training examples are clustered through subtractive clus-tering algorithm respectively. Then some representative examples are chosen from each of them to construct SVM components. At last,the outputs of the individual classifiers are fused through ma-jority voting method to obtain the final decision. Comparisons of performance between the proposed method and other popular ensemble approaches,such as Bagging,Adaboost and k.-fold cross valida-tion,are carried out on synthetic and UCI datasets. The experimental results show that our method has higher classification accuracy since the example distribution information is considered during en-semble through clustering analysis. It further indicates that our method needs a much smaller size of training subsets than Bagging and Adaboost to obtain satisfactory classification accuracy.

Instance reduction for supervised learning using input-output clustering method

A method that applies clustering technique to reduce the number of samples of large data sets using input-output clustering is proposed.The proposed method clusters the output data into groups and clusters the input data in accordance with the groups of output data.Then,a set of prototypes are selected from the clustered input data.The inessential data can be ultimately discarded from the data set.The proposed method can reduce the effect from outliers because only the prototypes are used.This method is applied to reduce the data set in regression problems.Two standard synthetic data sets and three standard real-world data sets are used for evaluation.The root-mean-square errors are compared from support vector regression models trained with the original data sets and the corresponding instance-reduced data sets.From the experiments,the proposed method provides good results on the reduction and the reconstruction of the standard synthetic and real-world data sets.The numbers of instances of the synthetic data sets are decreased by 25%-69%.The reduction rates for the real-world data sets of the automobile miles per gallon and the 1990 census in CA are 46% and 57%,respectively.The reduction rate of 96% is very good for the electrocardiogram(ECG) data set because of the redundant and periodic nature of ECG signals.For all of the data sets,the regression results are similar to those from the corresponding original data sets.Therefore,the regression performance of the proposed method is good while only a fraction of the data is needed in the training process.

Comparison of Supervised Clustering Methods for the Analysis of DNA Microarray Expression Data

Several typical supervised clustering methods such as Gaussian mixture model-based supervised clustering （GMM）, k- nearest-neighbor （KNN）, binary support vector machines （SVMs） and multiclass support vector machines （MC-SVMs） were employed to classify the computer simulation data and two real microarray expression datasets. False positive, false negative, true positive, true negative, clustering accuracy and Matthews＇ correlation coefficient （MCC） were compared among these methods. The results are as follows： （1） In classifying thousands of gene expression data, the performances of two GMM methods have the maximal clustering accuracy and the least overall FP＋FN error numbers on the basis of the assumption that the whole set of microarray data are a finite mixture of multivariate Gaussian distributions. Furthermore, when the number of training sample is very small, the clustering accuracy of GMM-Ⅱ method has superiority over GMM- Ⅰ method. （2） In general, the superior classification performance of the MC-SVMs are more robust and more practical, which are less sensitive to the curse of dimensionality, and not only next to GMM method in clustering accuracy to thousands of gene expression data, but also more robust to a small number of high-dimensional gene expression samples than other techniques. （3） Of the MC-SVMs, OVO and DAGSVM perform better on the large sample sizes, whereas five MC-SVMs methods have very similar performance on moderate sample sizes. In other cases, OVR, WW and CS yield better results when sample sizes are small. So, it is recommended that at least two candidate methods, choosing on the basis of the real data features and experimental conditions, should be performed and compared to obtain better clustering result.

Dual membership SVM method based on spectral clustering

A new fuzzy support vector machine algorithm with dual membership values based on spectral clustering method is pro- posed to overcome the shortcoming of the normal support vector machine algorithm, which divides the training datasets into two absolutely exclusive classes in the binary classification, ignoring the possibility of ＂overlapping＂ region between the two training classes. The proposed method handles sample ＂overlap＂ effi- ciently with spectral clustering, overcoming the disadvantages of over-fitting well, and improving the data mining efficiency greatly. Simulation provides clear evidences to the new method.

基于SWDAE-SVC的矿用齿轮箱自监督故障诊断方法

针对矿用齿轮箱振动数据易受噪声污染且故障类别标注困难问题,提出了一种基于栈式小波降噪自编码器(SWDAE)和支持向量聚类(SVC)的自监督故障诊断方法。首先,将小波映射函数引入栈式降噪自编码器(SDAE)模型,以实现强噪声下矿用齿轮箱的敏感故障特征提取。然后,利用所得高层抽象特征构建SVC模型,以实现无标签信息下的矿用齿轮箱故障诊断。实验结果表明,所提SWDAE-SVC方法具有优异的故障诊断性能。

改进黑猩猩算法的光伏发电功率短期预测

针对晴空、非晴空条件下光伏出力预测精度不高等问题,提出一种改进K均值(K-means++)算法和黑猩猩优化算法CHOA(chimpanzee optimization algorithm)相结合,优化最小二乘支持向量机LSSVM(least squares support vector machine)的模型,进行光伏功率预测。首先,利用密度聚类和混合评价函数改进K-means++对原始数据进行自适应类别划分。其次,通过相关性分析和随机森林特征提取构建模型的输入特征集。最后,根据特征集建立基于DK-PCHOA-LSSVM的短期光伏发电预测模型。结合实际算例,结果表明:该模型在恶劣天气下预测精度明显优于其他模型,验证了其有效性和优越性。

基于空间投影和聚类划分的SVR加速算法

数据不仅能产生价值,还对统计学的科学发展提供了动力。随着科技的飞速发展,海量数据得以涌现,但大规模的数据会导致很多传统处理方法很难满足各领域对数据分析的需求。面对海量数据时代学习算法的低效性,分治法通常被认为是解决这一问题最直接、最广泛使用的策略。SVR是一种强大的回归算法,在模式识别和数据挖掘等领域有广泛应用。然而在处理大规模数据时,SVR训练效率低。为此,该文利用分治思想提出一种基于空间投影和聚类划分的SVR加速算法(PKM-SVR)。利用投影向量将数据投影到二维空间;利用聚类方法将数据空间划分为k个互不相交的区域;在每个区域上训练SVR模型;利用每个区域的SVR模型预测落入同一区域的待识别样本。在标准数据集上与传统的数据划分方法进行对比实验,实验结果表明该算法训练速度较快,并表现出更好的预测性能。

基于机器学习组合模型的远程塔台管制员情景意识水平

有效识别远程塔台管制员情景意识水平(situation awareness,SA)的主要影响因素,能够更好地为远程塔台管制设计和使用提供参考依据。首先对管制员分别在传统塔台和远程塔台环境下进行数据采集试验,分析两种环境下主、客观指标的差异性。其次,采用假设检验的方法来验证眼动指标作为评价远程塔台管制员SA的可行性。接着基于敏感眼动指标采用K-means聚类和支持向量机(support vector machine,SVM)组合模型分析方法对远程塔台管制员SA水平进行分类识别。结果表明:采用Poly核函数进行模型训练,对SA识别的准确率达到了99.72%。研究结果证实了K-means聚类与支持向量机模型组合模型可作为分析远程塔台管制员SA的有效方法。

切入场景下基于碰撞风险聚类的改进车速预测方法

切入工况的高精度车速预测是保证自动驾驶切入安全的关键依据。为提高自动驾驶汽车切入工况安全,开展了基于车车耦合风险聚类的切入场景自车速度高精度预测方法的研究。首先,依据实验所得自然驾驶数据进行车辆切入切出片段提取,使用K-means方法依据碰撞风险与加速度关联特征进行聚类分析。其次,基于支持向量机(SVM)模型,对切入切出工况车车交互状态进行在线识别,对切入危险工况进行实时预测。最后,提出基于自回归综合移动平均(ARIMA)模型的改进车速预测方法,结合在线识别结果进行车速在线优化。仿真结果表明,所提出的基于碰撞风险聚类的改进ARIMA车速预测方法对提高切入安全效果明显,较传统的预测方法车辆的碰撞风险降低了10%~20%。研究结果表明,ARIMA模型的改进车速预测方法对提高自动驾驶车切入安全具有重要的研究意义。