基于改进判别字典学习的故障诊断方法

近年来,基于稀疏表示的分类技术在模式识别中取得一定的成功。该框架中,字典的学习和分类器的训练通常是两个独立的模块,降低了方法的识别精度。针对以上问题,提出了一种特征提取和模式识别相融合的改进判别字典学习模型,将重构误差项、稀疏编码判别项及分类误差项进行了整合,并用K奇异值分解算法对目标函数进行优化,实现了字典和分类器的同步学习。该方法先对原始信号进行经验模态分解,并从分解的本征模态函数中提取时、频特征,形成故障样本;然后将训练样本输入改进模型用K奇异值分解优化;最后用习得字典及分类器权重对测试样本进行识别。实验结果表明：该算法不但适用于小样本故障问题,而且鲁棒性和分类性能都明显高于其它算法。         

A hybrid SVM and supervised learning approach to fuzzy min‐max hyperbox classifiers

Abstract

In this paper, a fuzzy min‐max hyperbox classifier is designed to solve M‐class classification problems using a hybrid SVM and supervised learning approach. In order to solve a classification problem, a set of training patterns is gathered from a considered classification problem. However, the training set may include several noisy patterns. In order to delete the noisy patterns from the training set, the support vector machine is applied to find the noisy patterns so that the remaining training patterns can describe the behavior of the considered classification system well. Subsequently, a supervised learning method is proposed to generate fuzzy min‐max hyperboxes for the remaining training patterns so that the generated fuzzy min‐max hyperbox classifier has good generalization performance. Finally, the Iris data set is considered to demonstrate the good performance of the proposed approach for solving this classification problem.     

Fault Diagnosis for Non-linear System Based On Adaptive Fuzzy System

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Fuzzy cognitive map based approach for determining the risk of ischemic stroke

Stroke is the third major cause of mortality in the world. The diagnosis of stroke is a very complex issue considering controllable and uncontrollable factors. These factors include age, sex, blood pressure, diabetes, obesity, heart disease, smoking, and so on, having a considerable influence on the diagnosis of stroke. Hence, designing an intelligent system leading to immediate and effective treatment is essential. In this study, the soft computing method known as fuzzy cognitive mapping was proposed for diagnosis of the risk of ischemic stroke. Non‐linear Hebbian learning method was used for fuzzy cognitive maps training. In the proposed method, the risk rate for each person was determined based on the opinions of the neurologists. The accuracy of the proposed model was tested using 10‐fold cross‐validation, for 110 real cases, and the results were compared with those of support vector machine and K ‐nearest neighbours. The proposed system showed a superior performance with a total accuracy of (93.6 ± 4.5)%. The data used in this study is available by emailing the first author for academic and non‐commercial purposes.Inspec keywords: patient diagnosis, fuzzy logic, diseases, medical computing, cognition, learning (artificial intelligence), fuzzy set theory, Hebbian learning, neural nets, support vector machinesOther keywords: ischemic stroke, controllable factors, uncontrollable factors, blood pressure, heart disease, intelligent system, immediate treatment, soft computing method, fuzzy cognitive mapping, nonlinear Hebbian learning method, fuzzy cognitive maps training, risk rate     

Hybrid control of vibrations of a smart von Kármán plate

The suppression of large vibrations of a smart thin elastic rectangular von Kármán’s plate is considered. The plate is subjected to external disturbances and generalized control forces produced by electromechanical feedback. The considered nonlinear initial-boundary value problem is spatially discretized by means of the time spectral method. The implicit Newmark-β iterative method is employed for the time integration of the obtained system of nonlinear equations of motion. Nonlinear controllers are designed, based on a fuzzy inference system. Two numerical algorithms involving a general control of displacement/velocity and a direct control of the Fourier coefficients are proposed. The techniques have been implemented within MATLAB environment with the use of the fuzzy logic toolbox. Numerical examples are presented.     

模糊PID迭代学习气压模拟系统控制算法研究

高速列车车内气压变化影响乘客的舒适性,基于综合舒适度模拟试验台,抽象出气压模拟系统以模拟车内典型气压变化工况。针对气压模拟系统可重复性模拟车内典型气压变化及其难以精确建立数学模型等特点,建立该系统的质量-压力转换数值模型,并根据系统特点设计模糊PID迭代学习控制算法对系统的压力控制进行研究。仿真结果表明:在收敛速度方面,该算法较模糊迭代控制提升34.48%,较传统PID迭代控制提升44.12%;在控制精度方面,该算法较模糊迭代控制提升20.14%,较传统PID迭代控制提升21.00%。综上所述,该算法能够有效提高控制系统的控制精度和收敛速度,改善系统的动态性能。     

Efficient computation of order and mode of corner singularities in 3D‐elasticity

A general numerical procedure is presented for the efficient computation of corner singularities, which appear in the case of non‐smooth domains in three‐dimensional linear elasticity. For obtaining the order and mode of singularity, a neighbourhood of the singular point is considered with only local boundary conditions. The weak formulation of the problem is approximated by a Galerkin–Petrov finite element method. A quadratic eigenvalue problem ( P +λ Q +λ² R ) u = 0 is obtained, with explicitly analytically defined matrices P , Q , R . Moreover, the three matrices are found to have optimal structure, so that P , R are symmetric and Q is skew symmetric, which can serve as an advantage in the following solution process. On this foundation a powerful iterative solution technique based on the Arnoldi method is submitted. For not too large systems this technique needs only one direct factorization of the banded matrix P for finding all eigenvalues in the interval ?e(λ)∈(?0.5,1.0) (no eigenpairs can be ‘lost’) as well as the corresponding eigenvectors, which is a great improvement in comparison with the normally used determinant method. For large systems a variant of the algorithm with an incomplete factorization of P is implemented to avoid the appearance of too much fill‐in. To illustrate the effectiveness of the present method several new numerical results are presented. In general, they show the dependence of the singular exponent on different geometrical parameters and the material properties. Copyright © 2001 John Wiley & Sons, Ltd.     

发电侧市场力的模糊综合评判

为了对发电侧市场力进行测度,应用模糊数学综合评判法和增强学习算法,建立了发电侧市场力模糊评估模型,对发电侧市场力进行了模糊综合评判.评估的结果说明了根据模糊综合评判模型,可以把模糊性很强的集中度等级指标定量化,为发电侧市场力的评判提供依据.并以某市发电市场为例,通过对其进行模糊评估分析,得出该市场集中度等极指标.     

Detection of pneumonia in chest X‐ray images by using 2D discrete wavelet feature extraction with random forest

Pneumonia is one of the most common and fatal diseases in the world. Early diagnosis and treatment are important factors in reducing mortality caused by the aforementioned disease. One of the most important and common techniques to diagnose pneumonia disease is the X‐ray images. By evaluating these images, various machine‐learning methods are used for accuracy in diagnosis. The presented study in this article utilizes machine‐learning techniques to evaluate these X‐ray images. The diagnosis of pediatric pneumonia is classified with a proposed machine learning method by using the chest X‐ray images. The proposed system firstly utilizes a two‐dimensional discrete wavelet transform to extract features from images. The features obtained from the wavelet method are labeled as normal and pneumonia and applied to the classifier for classification. Besides, Random Forest algorithm is used for the classification technique of 5856 X‐ray images. A 10‐fold cross‐validation method is used to evaluate the success of the proposed method and to ensure that the system avoided overfitting. By using various machine learning algorithms, simulation results reveal that the Random Forest method is proposed and it gives successful results. Results also show that, at the end of the training and validation process, the proposed method achieves higher success with an accuracy of 97.11%.     

大型旋转机械故障诊断专家系统ETHYLENE的理论研究

本文在模糊产生式规则的基础上引入了加权模糊连接算子的概念,并研究了征兆的类型。在此基础上,为旋转机械模糊诊断知识提出了一个灵活的知识表达方法——广义模糊产生式规则。在征兆类型的基础上引入了征兆认可因子的概念.利用这一概念来描述模糊蕴涵关系的语义,基于征兆认可因子提出了一个模糊推理算法。文中还给出了由基于这一广义模糊产生式规则和模糊推理算法建立的透平压缩机组故障诊断专家系统ETHYLENE得到的两个诊断结果。     

基于IITD模糊熵与随机森林的滚动轴承故障诊断方法

针对滚动轴承故障微弱振动信号特征提取后难以识别的问题,提出基于改进的固有时间尺度分解(IITD)和模糊熵(FE)输入随机森林(RF)模式识别的滚动轴承故障诊断方法.首先,利用轴承试验台采集正常、滚动体故障、内圈故障、外圈故障等4种状态下轴承的振动信号;通过IITD分解将采集到的振动信号分解成一组固有旋转分量(PRC),...     

Constructing a new fuzzy classifier based on hierarchical fuzzy entropy

Abstract

In an earlier work, Lee et al. (Lee et al., 2001) presented a simple and fast fuzzy classifier that employed fuzzy entropy to evaluate pattern distribution information in a pattern space. In this paper, we extend his work to propose a new fuzzy classifier based on hierarchical fuzzy entropy (FC‐HFE). We retained the main parts of the original structure and modified some methods (e.g., methods for deciding the number of intervals in each dimension and for assigning class labels). In addition, the hierarchical fuzzy entropy is proposed for partitioning the decision region. The proposed FC‐HFE improves classification accuracy and overcomes some of the drawbacks in the Lee et al method (Lee et al., 2001). The simulation results show that the classification rate of the proposed FC‐HFE is better than earlier methods.     

多维数据雷达图和模糊推理的分类器研究

提出了一种新颖的基于多维数据雷达图表示原理结合模糊推理规则的自动分类器设计方法.该方法首先采用多元分析中的雷达图表示多维数据,建立已知类模板,然后应用模糊推理方法识别未知类的雷达图形,应用模板匹配法确定其归属,从而完成自动分类.基于混合油品132个测试数据的实验表明,此分类器可以将全部合理混合油品做出正确分类,且具有良好的分类效果和分类精度.     

Gain ratio based fuzzy weighted association rule mining classifier for medical diagnostic interface

The health care environment still needs knowledge based discovery for handling wealth of data. Extraction of the potential causes of the diseases is the most important factor for medical data mining. Fuzzy association rule mining is well-performed better than traditional classifiers but it suffers from the exponential growth of the rules produced. In the past, we have proposed an information gain based fuzzy association rule mining algorithm for extracting both association rules and member-ship functions of medical data to reduce the rules. It used a ranking based weight value to identify the potential attribute. When we take a large number of distinct values, the computation of information gain value is not feasible. In this paper, an enhanced approach, called gain ratio based fuzzy weighted association rule mining, is thus proposed for distinct diseases and also increase the learning time of the previous one. Experimental results show that there is a marginal improvement in the attribute selection process and also improvement in the classifier accuracy. The system has been implemented in Java platform and verified by using benchmark data from the UCI machine learning repository.     

基于EEMD模糊熵和GK聚类的信号特征提取方法及应用

提出了一种基于集合经验模态分解模糊熵和GK聚类相结合的方法,应用于滚动轴承的故障诊断中。首先,利用EEMD方法将故障信号分解成多个本征模态分量来消除模态混叠影响;其次,通过相关性对IMF分量进行筛选,并求取其模糊熵作为特征向量进行GK聚类分析进行模式识别。在实验分析中,通过模糊熵、样本熵、近似熵3种特征参数的对比,和GK聚类与FCM聚类的对比,证明了该方法的有效性和优越性。     

An intelligent mining system for diagnosing medical images using combined texture‐histogram features

The aim of this article is to design an expert system for medical image diagnosis. We propose a method based on association rule mining combined with classification technique to enhance the diagnosis of medical images. This system classifies the images into two categories namely benign and malignant. In the proposed work, association rules are extracted for the selected features using an algorithm called AprioriTidImage, which is an improved version of Apriori algorithm. Then, a new associative classifier CLASS_Hiconst ( CL assifier based on ASS ociation rules with Hi gh Con fidence and S uppor t ) is modeled and used to diagnose the medical images. The performance of our approach is compared with two different classifiers Fuzzy‐SVM and multilayer back propagation neural network (MLPNN) in terms of classifier efficiency with sensitivity, specificity, accuracy, positive predictive value, and negative predictive value. The experimental result shows 96% accuracy, 97% sensitivity, and 96% specificity and proves that association rule based classifier is a powerful tool in assisting the diagnosing process. © 2013 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 23, 194–203, 2013     

基于模糊K-均值算法的模糊分类器设计

基于模糊K-均值算法的模糊分类器,就是把目前比较常用的模糊K-均值算法的聚类方法,再一次与模糊分类规则提取相结合而得到的一种分类器。它是一种很有效的模糊分类器,训练样本能正确的分类。在这种方法中,首先用模糊K-均值算法按剖分和覆盖的原则把训练样本分成群,并且每一群的中心和半径都被计算出来。然后,设计一个用模糊规则来表示分类的模糊系统。这样就有效地构建了一个能对训练样本比较准确分类的模糊分类器。用这种方法设计的分类器不需要预定义参数、训练时间较短、方法简单     

模糊迭代学习方法及高层建筑结构地震响应控制的数值模拟

结合自校正控制、模糊逻辑和迭代学习控制的基本思想,提出采用自整定模糊控制确定迭代学习律的方法,提高了迭代学习控制的鲁棒性。选取建筑结构振动控制Benchmark第二阶段的地震作用Benchmark模型作为研究对象,进行模糊迭代学习控制地震响应仿真计算,结果表明该方法能够对Benchmark模型的地震响应进行有效控制,而且具有学习控制律简单实用、跟踪精度高、鲁棒性强等优点。     

基于一类超球面支持向量机的机械故障诊断研究

针对机械故障诊断中故障类样本不易获取以及样本分布不均的问题,提出了基于一类超球面支持向量机(SVM)的故障诊断方法,该方法只需要对正常类样本进行训练.试验分析了异常类样本缺失对一类超球面支持向量机性能的影响,并提出模型参数优化选择方法,以提高分类模型的推广能力.分析了不同训练结果的分类能力,并对一类超球面支持向量机与一类超平面支持向量机的分类结果进行比较,验证了前者的正确性和有效性.     

Soft fault detection and isolation in analog circuits: some resultsand a comparison between a fuzzy approach and radial basis functionnetworks

This paper provides a comparison between two techniques for soft fault diagnosis in analog electronic circuits. Both techniques are based on the simulation before test approach: a "fault dictionary" is a priori generated by collecting, signatures of different fault conditions. Classifiers, trained by the examples contained in the fault dictionary, are then configured to classify the measured circuit responses. The suggested classifiers have similar structures. The first is based on a fuzzy system, obtained by processing fault dictionary data for automatic generation of IF-THEN rules, and the second classifier is based on a radial basis function neural network. The two classifiers are used to detect and isolate faults both at the subsystem and component levels. The experimental results point out that both classifiers provide low classification errors in the presence of noise and nonfaulty components tolerance effects. The fuzzy approach provides better results due to an efficient generation method for the IF-THEN rules that allows adding IF parts in the input space regions where ambiguity occurs