Control chart pattern recognition using an optimized neural network and efficient features

Automatic recognition of abnormal patterns in control charts has seen increasing demands nowadays in manufacturing processes. This study investigates the design of an accurate system for control chart pattern (CCP) recognition from two aspects. First, an efficient system is introduced that includes two main modules: the feature extraction module and the classifier module. The feature extraction module uses the entropies of the wavelet packets. These are applied for the first time in this area. In the classifier module several neural networks, such as the multilayer perceptron and radial basis function, are investigated. Using an experimental study, we choose the best classifier in order to recognize the CCPs. Second, we propose a hybrid heuristic recognition system based on particle swarm optimization to improve the generalization performance of the classifier. The results obtained clearly confirm that further improvements in terms of recognition accuracy can be achieved by the proposed recognition system.     

A novel approach for recognition of control chart patterns: Type-2 fuzzy clustering optimized support vector machine

Unnatural patterns in the control charts can be associated with a specific set of assignable causes for process variation. Hence, pattern recognition is very useful in identifying the process problems. In this study, a multiclass SVM (SVM) based classifier is proposed because of the promising generalization capability of support vector machines. In the proposed method type-2 fuzzy c-means (T2FCM) clustering algorithm is used to make a SVM system more effective. The fuzzy support vector machine classifier suggested in this paper is composed of three main sub-networks: fuzzy classifier sub-network, SVM sub-network and optimization sub-network. In SVM training, the hyper-parameters plays a very important role in its recognition accuracy. Therefore, cuckoo optimization algorithm (COA) is proposed for selecting appropriate parameters of the classifier. Simulation results showed that the proposed system has very high recognition accuracy.     

Super-parameter selection for Gaussian-Kernel SVM based on outlier-resisting

The learning ability and generalizing performance of the support vector machine (SVM) mainly relies on the reasonable selection of super-parameters. When the scale of the training sample set is large and the parameter space is huge, the existing popular super-parameter selection methods are impractical due to high computational complexity. In this paper, a novel super-parameter selection method for SVM with a Gaussian kernel is proposed, which can be divided into the following two stages. The first one is choosing the kernel parameter to ensure a sufficiently large number of potential support vectors retained in the training sample set. The second one is screening out outliers from the training sample set by assigning a special value to the penalty factor, and training out the optimal penalty factor from the remained training sample set without outliers. The whole process of super-parameter selection only needs two train-validate cycles. Therefore, the computational complexity of our method is low. The comparative experimental results concerning 8 benchmark datasets show that our method possesses high classification accuracy and desirable training time.     

基于优化支持向量机的轴承故障诊断方法研究

以滚动轴承在正常、内圈故障、外圈故障和滚动体故障四种工况下的振动信号为研究对象,采用小波包变换的方法提取信号的能量熵,构成振动信号的特征向量。在此基础上采用支持向量机进行故障模式识别,建立支持向量机模型需要选择适当的核函数及相关参数,使用径向基核函数,需要设置的参数为核函数的宽度和误差惩罚系数,分别结合传统的网格搜索,遗传算法,粒子群算法优化支持向量机参数以提升分类性能。试验结果表明,采用优化后的支持向量机进行故障诊断可以大大提高诊断精度。     

Temperature drift modeling and compensation of RLG based on PSO tuning SVM

Precision and generalization ability are the two main requirements for modeling the temperature drift of a Ring Laser Gyroscope (RLG). Traditional methods such as the least square fitting and artificial neural network cannot achieve the optimal performance for both aspects. To solve this problem, a novel modeling method based on particle swarm optimization (PSO) tuning support vector machine (SVM) with multiple temperature variables input is proposed. First, the temperature drift data for modeling is preprocessed by adaptive forward linear prediction (FLP) filter. Then, the SVM method is employed to construct the drift model and guarantee the generalization ability. And the PSO algorithm is used to tune the parameters of SVM and improve the precision of established model. The results of experiment validate the superiority of the proposed method in both aspects. The method has been practically applied to a high precision RLG position and orientation system.     

基于支持向量机的半导体生产线动态调度方法

为了快速合理地选择调度策略,研究了一种半导体生产线动态调度策略选择方法。该方法以历史数据为基础,选取支持向量机为数据挖掘工具,采用二进制粒子群优化算法对生产属性(特征)子集进行寻优,获得基于支持向量机的动态调度策略分类模型。对于任意给定的生产状态,通过该模型,能实时地获取当前生产状态下近似最优的调度策略。在调度策略评价中,选用了基于功效函数与熵权法的多目标评价方法,以扩展该方法的应用范围。在某实际硅片生产线上验证了所提动态调度方法的有效性。     

基于可用度的预防维修和均值控制图联合优化模型

针对高可用度要求的生产系统(即可修复系统,以下简称系统),提出一种以单位时间成本为约束,可用度最大化为目标的预防维修和均值控制图联合优化模型。首先,考虑到系统运行存在受控、失控两种状态以及均值控制图两类错误(漏报警和误报警)发生的概率,在完美维修假设前提下,构建了4种系统运行(更新)情景(S1~S4)。然后,建立了每种更新情景的可用度和经济模型,并在此基础上建立了系统的可用度目标函数和经济约束函数,以实现单位时间成本约束条件下预防维修和均值控制图联合优化的系统可用度最大化目标。针对所建立的联合优化模型,通过实例对比验证了模型的有效性,并利用遗传算法对决策变量进行优化,实例优化结果表明,该模型能够有效提高系统可用度,并能降低系统成本。最后,通过正交试验、回归分析等方法对模型进行了参数的灵敏度分析。     

Support vector machine based data processing algorithm for wear degree classification of slurry pump systems

Classification is a useful tool in identifying fault patterns. Generally, a good classification implementation is closely related to the effectiveness of data used. The word “effectiveness” implies that the data should be clean and the features indicating fault patterns should be properly selected. Unfortunately, data cleaning is not often implemented in reported work of fault pattern classifications. In this paper, a data processing algorithm is developed to achieve the effectiveness, which includes data cleaning followed by feature selection. A data cleaning algorithm is developed based on support vector machine and random sub-sampling validation. Candidate outliers are selected based on fraction values provided by the proposed data cleaning algorithm and final outliers are determined based on their removal impacts on classification performance. The feature selection algorithm adopts the classical sequential backward feature selection. The performance of the data cleaning algorithm is tested using three benchmark datasets. The tests show good capability of the data cleaning algorithm in identifying outliers for all datasets. The proposed data processing algorithm is adopted in the classification of the wear degree of pump impellers in a slurry pump system. The results show good effectiveness of sequentially using data cleaning and feature selection in addressing fault pattern classification problems.     

A novel sense-through-foliage target recognition system based on sparse representation and improved particle swarm optimization-based support vector machine

A new recognition system of improved particle swarm optimization-based support vector machine (SVM) combined with sparse representation-based feature extraction is proposed for recognize targets obscured by foliage. Real data sets of four kinds of samples are acquired using a bistatic ultra-wideband (UWB) radar system. Sparse representation (SR) theory is applied to analyzing the components of received target echo signals and sparse coefficients are used to describe target features, the dimension of the sparse coefficients is reduced using principal component analysis (PCA). Support vector machine is a powerful tool for solving the recognition problem with small sampling, nonlinearity and high dimension. Improved particle swarm optimization (IPSO) is developed in this study to determine the optimal parameters for SVM with the highest accuracy and generalization ability. The experiment results indicate that the method of feature extraction using SR can effectively represent the original data better. The recognition result of the proposed method is also compared with SVM, k-nearest neighbor (KNN) and BP neural network (BPNN). The effectiveness of the proposed approach is verified by experiments taken in the forest environment. Our findings show that the proposed method combined with bistatic UWB radar technology provides a good access to achieve the aim of automatic sense through foliage target recognition.     

采用自适应变异粒子群优化SVM的行为识别

为了提高对视频序列中人体行为的识别能力,建立了基于局部特征的动作识别框架。通过时空特征提取及编码和SVM分类器参数优化两部分对该框架所涉及算法进行了研究。首先,采用Harris3D检测器获取时空兴趣点(STIP),以方向梯度直方图(HOG)和光流方向直方图(HOF)对STIP进行描述,并引入Fisher向量实现对特征描述子的编码;由于固定参数下SVM动作分类模型存在泛化能力不足的问题,将粒子群算法应用于各动作分类器参数寻优过程中,针对种群多样性逐代变化的特点,构建粒子聚集度模型,并利用其动态调节各代粒子的变异概率;最后,利用KTH和HMDB51数据集对所提方法进行验证。结果表明,所提自适应变异粒子群算法(AMPSO)能够有效避免种群陷入局部最优,具备较强的全局寻优能力;在KTH和HMDB51数据集上的识别准确率分别为87.50%和26.41%,优于其余2种识别方法。实验证明,AMPSO算法收敛性能良好且整体识别框架具有较高的实用性和准确性。     

A new sense-through-foliage target recognition method based on hybrid particle swarm optimization-based wavelet twin support vector machine

In order to improve the accuracy of sense-through-foliage target recognition, a new recognition method based on sparse representation-based adaptive feature extraction and hybrid particle swarm optimization (HPSO)-optimized wavelet twin support vector machine (WTSVM) is proposed in this paper. First, an adaptive feature extraction approach based on sparse representation is applied to extract the target features from the measured radar echo waveforms, the target feature set is constructed by sparse coefficients that contain most target information. Then, a new recognition method based optimized WTSVM is developed to perform target recognition. Twin SVM (TSVM) is a powerful tool in the field of machine learning, but the kernel and parameters selection problem still affects the performance of TSVM directly. A novel HPSO is developed in this study to determine the optimal parameters for WTSVM with the highest accuracy and generalization ability. As a hybridization strategy, local search is integrated in the PSO algorithm to further refine the performance of individuals and accelerate their convergence toward the global optimality. Finally, the performance of the proposed method is verified by experiments taken in the forest, and the results conform the improved accuracy of target recognition.     

基于Minitab的质量控制图在工件制造中的应用

介绍利用-x-R控制图对工件生产过程进行质量控制,并用Minitab14作为开发工具对质量数据进行统计处理,通过所得的质量控制图分析产品的质量变化.     

PSO优化SVM在木材干燥建模中的应用

在木材干燥计算机控制过程中,基准模型化是此控制过程的必要环节。为提高此建模预测精度,针对SVM木材干燥基准模型的参数进行研究。利用粒子群优化算法中的粒子位置和速度优化此模型参数,并对木材含水率进行预测。仿真实验表明,PSO算法在优化SVM木材干燥基准模型参数方面表现出良好的性能,预测结果具有很高的精度,此模型具有较好的泛化能力和预测能力。     

A new approach for rule extraction of expert system based on SVM

Based on the SVM’s excellent generalization performance, a new approach is proposed to extract knowledge rules from Support Vector Clustering (SVC). In this method, the first step is to choose the features of the sample data by using Genetic Algorithm for improving the comprehensibility of the knowledge rules. Then the SVC algorithm is adopted to obtain the Clustering Distribution Matrix of the sample data whose features have been chosen. Finally, hyper-rectangle rules are constructed using the Clustering Distribution Matrix. To make the rules more concise, and easier to explain, hyper-rectangle rules are simplified further by using rules combinations, dimension reduction and interval extension. In addition, the SMOTE (Synthetic Minority Over-sampling Technique) algorithm is adopted to resample fault samples in order to solve the serious imbalance problem of samples. The UCI datasets are used to validate the new method proposed in this paper, the results compared with other rules extraction methods show that the new approach is more effective. The new method is used to extract knowledge rules for aero-engine oil monitoring expert system, and the results show that the new method can effectively extract knowledge rules for expert system, and break through the bottleneck in expert system knowledge dynamic acquisition.     

A hybrid fault diagnosis methodology with support vector machine and improved particle swarm optimization for nuclear power plants

The safety and public health during nuclear power plant operation can be enhanced by accurately recognizing and diagnosing potential problems when a malfunction occurs. However, there are still obvious technological gaps in fault diagnosis applications, mainly because adopting a single fault diagnosis method may reduce fault diagnosis accuracy. In addition, some of the proposed solutions rely heavily on fault examples, which cannot fully cover future possible fault modes in nuclear plant operation. This paper presents the results of a research in hybrid fault diagnosis techniques that utilizes support vector machine (SVM) and improved particle swarm optimization (PSO) to perform further diagnosis on the basis of qualitative reasoning by knowledge-based preliminary diagnosis and sample data provided by an on-line simulation model. Further, SVM has relatively good classification ability with small samples compared to other machine learning methodologies. However, there are some challenges in the selection of hyper-parameters in SVM that warrants the adoption of intelligent optimization algorithms. Hence, the major contribution of this paper is to propose a hybrid fault diagnosis method with a comprehensive and reasonable design. Also, improved PSO combined with a variety of search strategies are achieved and compared with other current optimization algorithms. Simulation tests are used to verify the accuracy and interpretability of research findings presented in this paper, which would be beneficial for intelligent execution of nuclear power plant operation.     

基于变分模态分解和PSO-SVM的起重机齿轮箱故障诊断

起重机齿轮箱的振动信号具有信噪比低、非线性的特点,需要一定的专业知识和经验才能实现故障诊断.为了实现起重机齿轮箱的智能故障诊断,提出了一种基于变分模态分解(Variation?al modal decomposition,VMD)改进小波降噪和粒子群算法(Particle swarm optimization,PSO)...     

基于PSO-SVM的关键生物参数软测量方法研究

微生物发酵过程中一些关键生物参数难以实时在线测量,严重影响发酵的优化控制。为解决关键生物参数的测量难题,采用了一种基于PSO-SVM的软测量方法。该方法利用粒子群优化(PSO)算法优化选择支持向量机(SVM)的最佳参数,并建立了基于PSO-SVM的软测量模型。利用赖氨酸发酵的数据对模型进行仿真验证,结果表明该模型具有很好的学习精度和泛化能力。另外在建模耗时上,PSO-SVM算法所用时间远少于标准SVM算法所用时间。     

基于量子粒子群优化Volterra时域核辨识的隐Markov模型识别方法

将量子粒子群优化算法引入Voherra级数模型的非线性辨识中,并结合隐Markov模型(hidden Markov model,HMM),提出了一种基于量子粒子群优化的Voherra时域核特征提取的HMM识别方法,在提出的方法中,利用量子粒子群优化算法辨识得到的前三阶Volterra时域核作为故障特征,输入到各种状态的HMM中,其中,输出概率最大的HMM对应的状态即为设备的当前运行状态.提出的方法克服了传统的基于Volterra模型系统的机械故障诊断要求目标函数连续可导、容易陷入局部最小以及抗干扰能力差等缺陷.最后,将提出的方法应用到旋转机械故障诊断中.实验结果验证了该方法的有效性.     

Automatic recognition of machining features from a solid model using the 2D feature pattern

Feature recognition systems are now widely identified as a cornerstone for conceiving an automated process planning system. Various techniques have been reported in the literature, but a few of them acquired a status of generic methodology. A flexible and robust approach is demanded for recognising a wide variety of features, e.g., non-interacting, interacting circular and slanting features. This research aims to exploit the concept of the ray - firing technique, in which a 2D surface pattern for each feature is generated and information is extracted from these patterns to correlate it with the corresponding machining features. The system first defines a virtual surface and then probing rays are dropped from each point of this surface to the 2.5D features of the B-rep solid model. According to the length of rays between the bottom face of the 2.5D machining features and the virtual surface, 2D feature patterns are formed for each machining feature. Finally, features are recognised using an algorithm described in this article. Different types of examples have been considered to demonstrate the effectiveness of the proposed approach.     

一种基于粒子群算法的二自由度PID控制器优化设计

粒子群算法在解决复杂问题的优化设计上具有算法简单,计算速度快的优点,将粒子群算法引入到二自由度PID控制器的参数整定中,用于改进二自由度PID控制器参数整定的效率并进行了仿真实验,仿真结果表明,系统同时具有了最优的目标值跟踪特性和干扰抑制特性.