A demerit control chart with linguistic weights

A classical demerit control chart is used to monitor counts of several different categories of defects simultaneously in a complex product. The traditional recommendation is to plot the demerit statistic, a weighted sum of the number of defects of each category, on a control chart. Such approach assumed that the severe degree of the same category is equally treated and a crisp weight is assigned subjectively. Furthermore, the assignment of an actual and crisp weight to each category is somewhat difficult for process and quality engineers. A linguistic variable to represent the importance and severity is more suitable. Thus, on the basis of the fuzzy set theory, the fuzzy demerit control chart which uses linguistic weights to represent the severe degree of each category is proposed. The procedure of constructing the proposed chart is described in five steps. In addition, a fuzzy ranking method using -cuts is adopted to generate the crisp statistic and control limits in coordination with the custom of classical control charts. A guideline is suggested for deciding the values of and the width of control limits. By a numerical example, the results show that such approach can provide more realistic modeling to monitor the number of demerits per inspection unit and identify the process variation.This revised version was published in June 2005 with corrected page numbers.     

Recognition of control chart patterns using improved selection of features

Recognition of various control chart patterns (CCPs) can significantly reduce the diagnostic search process. Feature-based approaches can facilitate efficient pattern recognition. The full potentiality of feature-based approaches can be achieved by using the optimal set of features. In this paper, a set of seven most useful features is selected using a classification and regression tree (CART)-based systematic approach for feature selection. Based on these features, eight most commonly observed CCPs are recognized using heuristic and artificial neural network (ANN) techniques. Extensive performance evaluation of the two types of recognizers reveals that both these recognizers result in higher recognition accuracy than the earlier reported feature-based recognizers. In this work, various features are extracted from the control chart plot of actual process data in such a way that their values become independent of the process mean and standard deviation. Thus, the developed feature-based CCP recognizers can be applicable to any general process.     

Mixture control chart patterns recognition using independent component analysis and support vector machine

Effective recognition of control chart patterns (CCPs) is an important issue since abnormal patterns exhibited in control charts can be associated with certain assignable causes which affect the process. Most of the existing studies assume that the observed process data which needs to be recognized are basic types of abnormal CCPs. However, in practical situations, the observed process data could be mixture patterns, which consist of two basic CCPs combined together. In this study, a hybrid scheme using independent component analysis (ICA) and support vector machine (SVM) is proposed for CCPs recognition. The proposed hybrid ICA-SVM scheme initially applies an ICA to the mixture patterns in order to generate independent components (ICs). The hidden basic patterns of the mixture patterns can be discovered in these ICs. The ICs can then serve as the input variables of the SVM for building a CCP recognition model. Experimental results revealed that the proposed scheme is able to effectively recognize mixture control chart patterns and outperform the single SVM models, which did not use an ICA as a preprocessor.     

A fuzzy reasoning based diagnosis system for X control charts

This paper describes a new diagnosis system, which is based on fuzzy reasoning to monitor the performance of a discrete manufacturing process and to justify the possible causes. The diagnosis system consists chiefly of a knowledge bank and a reasoning mechanism. The knowledge bank provides knowledge of the membership functions of unnatural symptoms that are described by Nelson's rules on X control charts and knowledge of cause-symptom relations. We develop an approach called maximal similarity method (MSM) for knowledge acquisition to construct the fuzzy cause-symptom relation matrix. Through the knowledge bank, the diagnosis system can first determine the degrees of an observation fitting each unnatural symptom. Then, using the fuzzy cause-symptom relation matrix, we can diagnose the causes of process instability. In conclusion we provide a numerical example to illustrate the system.     

Feature-based recognition of control chart patterns

Control charts primarily in the form of chart are widely used to identify the situations when control actions will be needed for manufacturing systems. Various types of patterns are observed in control charts. Identification of these control chart patterns (CCPs) can provide clues to potential quality problems in the manufacturing process. Each type of control chart pattern has its own geometric shape and various related features can represent this shape. Feature-based approaches can facilitate efficient pattern recognition since extracted shape features represent the main characteristics of the patterns in a condensed form. In this paper, a set of eight new features, extraction of which does not call for utilizing the experience and skill of the user in any form, is presented. Two feature-based approaches using heuristics and artificial neural network (ANN) are developed, which are capable of recognizing eight most commonly observed CCPs including stratification and systematic patterns. Relative performances of the feature-based heuristic and feature-based ANN recognizers are extensively studied using synthetic pattern data. The feature-based ANN recognizer results in better recognition performance and generalization compared to the feature-based heuristic recognizer.     

基于支持向量机的控制图模式识别

为了提高控制图模式识别效果，提出混合核函数支持向量机的模式识别方法。在模型构造中采用一对一多类分类支持向量机，并利用遗传算法优化混合核函数支持向量机参数。仿真和应用结果表明，混合核函数支持向量机对各种模式控制图的总体识别率，I型错判均优于单独核函数、概率神经网络和小波概率神经网络，且具有良好的泛化能力，适合生产现场实时在线工序质量控制。     

Recognition of concurrent control chart patterns using wavelet transform decomposition and multiclass support vector machines

Statistical process control charts have been widely utilized for monitoring process variation in many applications. Nonrandom patterns exhibited by control charts imply certain potential assignable causes that may deteriorate the process performance. Though some effective approaches to recognition of control chart patterns (CCPs) have been developed, most of them only focus on recognition and analysis of single patterns. A hybrid approach by integrating wavelet transform and improved particle swarm optimization-based support vector machine (P-SVM) for on-line recognition of concurrent CCPs is developed in this paper. A statistical correlation coefficient is used to determine whether the input pattern is a single or concurrent CCP. Based on wavelet transform, a raw concurrent pattern signal is decomposed into two basic pattern signals, which can be recognized by multiclass SVMs. The performance of the hybrid approach is evaluated by simulation experiments, and numerical and graphical results are provided to demonstrate that the proposed approach can perform effectively and efficiently in on-line CCP recognition task.     

基于融合特征与支持向量机的控制图模式识别

为了提高控制图模式识别的精度, 将控制图模式的原始特征与形状特征相融合得到分类特征, 并采用支持向量机进行模式分类的控制图模式识别。融合所得特征既保持了控制图模式的原始特征所蕴涵的模式全局特性信息, 又通过引入形状特征对部分易混淆模式的局部几何特性进行强化, 使不同模式间的区分度得到有效提高; 而以支持向量机作为模式分类器保证方法在高维度特征和小样本条件下也能获得较好的识别性能。仿真实验结果表明所提方法的识别精度相比其他几种基于形状特征的控制图模式识别方法有明显提高。     

Multi-criteria design of an control chart

Control chart designs are widely studied because control charts are not only costly used but also play an important role in improving firms' quality and productivity. Design of control charts refers to the selection of parameters, including sample size, control-limit width, and sampling frequency. In this paper, a possible combination of design parameters is considered as a decision-making unit; it is characterized by three attributes: hourly expected cost, average run length of process being controlled, and detection power of the chart designed with the selected parameters. Accordingly, optimal design of control charts can be formulated as a multiple criteria decision-making (MCDM) problem. To solve the MCDM problem, a solution procedure on the basis of data envelopment analysis is proposed. Finally, an industrial application is presented to illustrate the solution procedure. Also, adjustment to control chart design parameters is suggested when there are process improvements or process deteriorations.     

Identification of control chart patterns using wavelet filtering and robust fuzzy clustering

This paper proposes a hybrid framework composed of filtering module and clustering module to identify six common types of control chart patterns, including natural pattern, cyclic pattern, upward shift, downward shift, upward trend, and downward trend. In particular, a multi-scale wavelet filter is designed for denoising and its performance is compared to single-scale filters, including mean filter and exponentially weighted moving average (EWMA) filter. Moreover, three fuzzy clustering algorithms, based on fuzzy c means (FCM), entropy fuzzy c means (EFCM) and kernel fuzzy c means (KFCM), are adopted to compare their performance of pattern classification. Experimental results demonstrate that the excellent performance of EFCM and KFCM against outliers, especially in the case of high noise level embedded in the input data. Therefore, a hybrid framework combining wavelet filter with robust fuzzy clustering is suggested and proposed in this paper. Compared to neural network based approaches, the proposed method provides a promising way for the on-line recognition of control chart patterns because of its efficient computation and robustness against outliers.     

An economically designed Sequential Probability Ratio Test control chart for short-run production

This paper proposes an economic model for the design of an SPRT (Sequential Probability Ratio Test) chart for monitoring the process mean in short-run production. The model expresses the short-run cost per unit of operating the SPRT chart as a function of the cost parameters associated with the operation. A simple algorithm capable of optimizing the charting parameters is also proposed. The model can be used to quantify cost reductions achievable by substituting a traditional control policy by SPRT control. Numerical examples illustrate the effectiveness of the proposed procedure. It is shown that the resulting cost reduction can range from modest to substantial as the out-of-control probability of the process increases.     

Effective recognition of control chart patterns in autocorrelated data using a support vector machine based approach

The effective recognition of unnatural control chart patterns (CCPs) is a critical issue in statistical process control, as unnatural CCPs can be associated with specific assignable causes adversely affecting the process. Machine learning techniques, such as artificial neural networks (ANNs), have been widely used in the research field of CCP recognition. However, ANN approaches can easily overfit the training data, producing models that can suffer from the difficulty of generalization. This causes a pattern misclassification problem when the training examples contain a high level of background noise (common cause variation). Support vector machines (SVMs) embody the structural risk minimization, which has been shown to be superior to the traditional empirical risk minimization principle employed by ANNs. This research presents a SVM-based CCP recognition model for the on-line real-time recognition of seven typical types of unnatural CCP, assuming that the process observations are AR(1) correlated over time. Empirical comparisons indicate that the proposed SVM-based model achieves better performance in both recognition accuracy and recognition speed than the model based on a learning vector quantization network. Furthermore, the proposed model is more robust toward background noise in the process data than the model based on a back propagation network. These results show the great potential of SVM methods for on-line CCP recognition.     

基于Labview开发统计过程控制软件

统计过程控制是质量管理的重要内容,介绍了SPC的原理,并用Labview软件实现了SPC软件.这种开发方法应用了Labview软件提供的统计过程控制模块,方便了程序的开发,很容易被质量人员掌握,设备改动或软件应用于别的设备时,只需简单地修改参数,大大方便了统计过程控制的普及,提高企业的质量水平.     

小批量多元控制图建模方法研究

在分析研究现有小批量及多元控制图相关理论的基础上,基于Kalman滤波原理,提出一种综合解决小批量多元过程控制的建模方法。仿真实验及应用实例表明,该建模方法能够充分利用已经取得的数据,动态建立控制模型,从而解决小批量生产过程中建模数据不足的问题。     

Fuzzy logic coupling and synchronised control of multiple independent servo-drives

A method for achieving the co-ordination and synchronisation of multiple independent servo-drives using fuzzy logic is described. The co-ordination and synchronised control of motion of multiple axes is a challenging problem, since the performance of each individual axis can be influenced by many factors. To overcome these problems, a fuzzy coupling co-ordinator design is proposed, that enables linguistic inference outputs to be added to each individual control loop in the multi-axis scheme. A detailed analysis of the fuzzy algorithm is presented. Due to its simple structure, and efficient operation the proposed scheme can address many of the problems arising in multiple-axis motion co-ordination.     

Fuzzy Logic Control ASIC Chip

A fuzzy logic control VLSI chip,F100,for industry process real-time control has been designed and fabricated with 0.8μm CMOS technology.The chip has the features of simplicity,felexibility and generality.This paper presents the Fuzzy control inrerence method of the chip,its VLSI implementation,and testing esign consideration.     

A hybrid approach for identification of concurrent control chart patterns

Control chart patterns (CCPs) are widely used to identify the potential process problems in modern manufacturing industries. The earliest statistical techniques, including chart and R chart, are respectively used for monitoring process mean and process variance. Recently, pattern recognition techniques based on artificial neural network (ANN) are very popular to be applied to recognize unnatural CCPs. However, most of them are limited to recognize simple CCPs arising from single type of unnatural variation. In other words, they are incapable to handle the problem of concurrent CCPs where two types of unnatural variation exist together within the manufacturing process. To facilitate the research gap, this paper presents a hybrid approach based on independent component analysis (ICA) and decision tree (DT) to identify concurrent CCPs. Without loss of generality, six types of concurrent CCPs are used to validate the proposed method. Experimental results show that the proposed approach is very successful to handle most of the concurrent CCPs. The proposed method has two limitations in real application: it needs at least two concurrent CCPs to reconstruct their source patterns and it may be incapable to handle the concurrent pattern incurred by two correlated process (“upward trend” and “upward shift”).     

Economic design of autoregressive moving average control chart using genetic algorithms

When designing control charts, it is usually assumed that the observations from the process at different time points are independent. However, this assumption may not be true for some production processes, e.g., the continuous chemical processes. The presence of autocorrelation in the process data can result in significant effect on the statistical performance of control charts. Jiang, Tsui, and Woodall (2000) developed a control chart, called the autoregressive moving average (ARMA) control chart, which has been shown suitable for monitoring a series of autocorrelated data. In the present paper, we develop the economic design of ARMA control chart to determine the optimal values of the test and chart parameters of the chart such that the expected total cost per hour is minimized. An illustrative example is provided and the genetic algorithm is applied to obtain the optimal solution of the economic design. A sensitivity analysis shows that the expected total cost associated with the control chart operation is positively affected by the occurrence frequency of the assignable cause, the time required to discover the assignable cause or to correct the process, and the quality cost per hour while producing in control or out of control, and is negatively influenced by the shift magnitude in process mean.     

自适应EWMA控制图统计经济设计

针对自适应指数加权移动平均(AEWMA) 控制图统计经济设计问题, 给出AEWMA控制图统计经济设计模型, 提出一种在偏移区间上对AEWMA控制图进行设计的多目标优化方法. 针对不同的偏移区间优化了AEWMA控制图, 并将AEWMA控制图统计经济性能与指数加权移动平均(EWMA) 控制图相比较. 结果表明, 所提出方法优化设计的AEWMA控制图仍具有克服EWMA控制图的惯性问题的统计特性, AEWMA控制图的经济性能也优于EWMA控制图.

     

基于CIMS 环境下质量控制图的研究

计算机辅助质量（CAQ）是计算集成制造系统（CIMS）不可分割的重要组成部分，而质量控制图是其中的关键技术。分析了传统控制图应用于CIM系统存在的缺陷和不足，提出了适于小批量生产的均值（x^-）控制图，使其在产品批量较少或生产加工的初期阶段等环境下得到很好的应用，而且随着检测数据的增加，其控制界限与传统方法得到的控制界限相一致，同样适合于大批量的生产环境。