Process capability analyses based on fuzzy measurements and fuzzy control charts

Process performance can be analyzed by using process capability indices (PCIs), which are summary statistics to depict the process location and dispersion successfully. Although they are very usable statistics, they have some limitations which prevent a deep and flexible analysis because of the crisp measurements and specification limits (SLs). If the specification limits or measurements are expressed by linguistic variables, traditional PCIs cause some misleading results. In this paper, the fuzzy set theory is used to add more information and flexibility to process capability analyses (PCA). For this aim, linguistic definition of the quality characteristic measurements are converted to fuzzy numbers and fuzzy PCIs are produced based on these measurements and fuzzy specification limits (SLs). Also fuzzy control charts are derived for fuzzy measurements of the related quality characteristic. They are used to increase the accuracy of PCA by determining whether or not the process is in statistical control. The fuzzy formulation of the indices C_p and C_pk, which are the most used two traditional PCIs, are produced when SLs and measurements are both triangular (TFN) and trapezoidal fuzzy numbers (TrFN). The proposed methodologies are applied in a piston manufacturer in Konya’s Industrial Area, Turkey.     

Unnatural pattern recognition on control charts using correlation analysis techniques

This paper presents analysis and development of a pattern recognition system for identifying unnatural patterns on quality control charts. The system is based on correlation analysis, where a set of optimal matched filters are generated. To illustrate the design methodology and operation of the system, a set of commonly encountered patterns is utilized, such as the trend, the systematic, and the cyclic patterns. A training algorithm that minimizes the probabilities of Type I and Type II errors is presented. To evaluate the system performance, a testing algorithm as well as a set of newly-defined performance measures are introduced. The obtained results have shown the effectiveness of correlation analysis for control chart pattern recognition.     

New enhanced performance robust control design scheme for grid-connected VSI

In this paper, a new method for intelligent robust control design is presented that achieves the best possible convergence rate of the system, utilizing the knowledge on the range of uncertain parameter. Thus resulting in enhanced stability and performance. The proposed method is applied to the grid-connected voltage source inverter (VSI) system with uncertainties in grid-impedance. Simulation and experimental results illustrate the efficacy of the proposed scheme. Comparison with existing methods shows that the proposed scheme can provide better reference tracking, stability for a wider uncertainty range, and improved transient and steady-state performance with low implementation cost.     

Detection of chatter vibration in a drilling process using multivariate control charts

Time series analysis and multivariate control charts are used to devise a real-time monitoring strategy in a drilling process. The process is used to produce holes with high length-to-diameter ratio, good surface finish and straightness. It is subject to dynamic disturbances that are classified as either chatter vibration or spiralling. A new nonparametric control chart for multivariate processes is proposed. It is used to detect chatter vibration which is dominated by single frequencies. The results showed that the proposed monitoring strategy can detect chatter vibration and that some alarm signals are related to changing physical conditions of the process.     

Integrating economics into quality control charts

When control of a manufacturing process is needed, the common tool is Statistical Quality control (SQC). In the past, however, economic factors were the results after employing the SQC charts. Design of control charts refers to the specification of the sample size, the sampling frequency and the control limits for the chart. The authors have tested a model that uses economics as an integral part for the design of an X-bar control chart.

Douglas C. Montgomery developed a computer program for the optimal economic design of an X-bar control chart. The program is based on the cost model proposed by A.J. Duncan.

Montgomery's program was modified to select the optimal design parameters from a table of parameter values. Subroutines were developed to enable the user to enter the number of subgroups and the data points for each subgroup. The economically designed control chart is plotted using standard Graphical Kernel System (GKS) subroutines.     

Overload control using rate control throttle: selecting token bankcapacity for robustness to arrival rates

The design of rate-control throttles, which are used for overload control of computer and communication networks is discussed. An analysis of the rate-control throttle is presented. It is found that if the token bank capacity is 10 or more and if jobs arrive as a Poisson process, then for many practical applications in digital switching systems and telecommunication networks the control settings can be set independently of the arrival rate and need only be adjusted for changes in the desired departure rate. If the arrival of jobs is bursty (coefficient of variation of interarrival times is greater than 1), then the robustness declines but may remain quite good, depending on parameter values. The rate-control throttle with any token bank capacity is significantly more robust to arrival rates than either a percent-blocking throttle or a call-gapping throttle     

Q control charts for negative binomial distribution

As we known, Q charts are SPC charts for approximate normal distribution. This paper proposes new kinds of Q charts for negative binomial random variable. First, define two transformations of approximate standard normal distribution for a negative binomial parameter p which it is known or unknown. Then, Q charts are constructed by transformed data, these Q charts are useful for monitoring process in real time from the beginning of sampling, especially, for p unknown in advance. In order to compare the goodness of the Q charts with conventional Shewhart charts with 3-sigma limits, an example is given. The results have shown that these Q charts are more efficient than classical Shewhart charts. Finally, some review are given.     

Beta control charts for monitoring fraction data

p-Charts and np-Charts are commonly used in monitoring variables of the fraction type and these charts assume that the monitored variables are binomially distributed. In this paper we propose a new control chart called Beta Charts, for monitoring fraction data (p). The Beta Chart presents the control limits based on the Beta probability distribution. It was applied for monitoring the variables in three real studies, and it was compared to the control limits with three schemes. The comparative analysis showed that: (i) Beta approximation to the Binomial distribution was more appropriate with values confined in the [0, 1]-interval; and (ii) the charts proposed were more sensitive to the average run length (ARL), in both in-control and out-of-control processes monitoring. The Beta Charts outperform the control charts analyzed for monitoring fraction data.     

Robust control charts for time series data

This article presents a control chart for time series data, based on the one-step-ahead forecast errors of the Holt–Winters forecasting method. We use robust techniques to prevent that outliers affect the estimation of the control limits of the chart. Moreover, robustness is important to maintain the reliability of the control chart after the occurrence of alarm observations. The properties of the new control chart are examined in a simulation study and on a real data example.     

Fuzzy logic based assignable cause diagnosis using control chart patterns

Control chart patterns, besides determining the presence of assignable causes, also provide hints on the nature of assignable cause(s) present. Relating the patterns exhibited on the control chart to assignable causes is an ambiguous and vague task especially when multiple patterns co-exist. In this work, a rule based fuzzy inference system is developed for control chart to prioritize the control chart causes based on the accumulated evidence. When a process goes out of control, search for assignable causes can be assisted by the priorities assigned to the causes. For an in-control process, developing patterns can be tracked and preventive action can be taken to prevent the process from going out of control.     

利用JFreeChart在JSP中制作图表

图表的应用不但可以使数据的显示更加清晰、直观,而且可大大增强Web页面的功能和显示效果。针对目前实际应用中经常需要通过数据库中的数据生成相应的图表这一强大需求,介绍了JFreeChart中通过其提供的数据集合对象在JSP页面中生成图表的方法,并给出了生成饼图和柱状图的具体方法和代码。     

CUSUM control charts for monitoring optimal portfolio weights

A portfolio investor requires statistical tools for the timely detection of changes in the optimal portfolio composition. Several multivariate cumulative sum (CUSUM) control charts are proposed for the purpose of monitoring optimal portfolio weights. The ability of the CUSUM schemes to detect important types of changes in the optimal portfolio weights is analyzed in an extensive Monte Carlo simulation study. The empirical application of control charts shows that the proposed methodology can provide a significant reduction of the portfolio volatility.     

Optimization designs of the combined Shewhart-CUSUM control charts

This article presents the optimization design of the combined Shewhart chart and CUSUM chart ( chart in short) used in Statistical Process Control (SPC). While the optimization design effectively improves the overall performance of the chart over the entire process shift range, it does not increase difficulties in understanding and implementing this combined chart. A new feature pertaining to an additional charting parameter w (the exponential of the sample mean shift) is also investigated, with the hope of further enhancing the detection effectiveness of the chart. Moreover, this article provides the SPC practitioners with a design table to facilitate the designs of the charts. From this design table, the users can directly find the optimal values of the charting parameters, according to the design specifications. The design table makes the design of an chart as simple as the design of the simplest chart. In general, this article will help to enhance the detection effectiveness of the chart, and facilitate and promote its applications in SPC.     

Comparison of control charts for low defective rate

It is well known that the conventional p control chart constructed by the normal approximation for the binomial distribution suffers a serious inaccuracy in the monitor process when the true rate of nonconforming items is small. A similar problem also arises in the binomial confidence interval estimation. Adjusted confidence intervals are established in the literature to improve the coverage probability when the binomial proportion is small. In this paper, a new p control chart based on an adjusted confidence interval is established, which can substantially improve the existing control charts when the nonconforming rate is small.     

New EWMA control charts for monitoring process dispersion

There exist two EWMA-type dispersion charts for monitoring dispersion increases in the literature. One resets the EWMA statistic to zero whenever it is below zero. The other one truncates negative normalized observations to zero in the EWMA statistic. This paper proposes two one-sided EWMA charts for detecting dispersion increases and decreases, respectively, and one two-sided EWMA chart for monitoring dispersion increases or decreases simultaneously. Simulation studies show that the proposed upper-sided EWMA chart performs better than the two existing counterparts for detecting increases in dispersion, and that the proposed lower-sided EWMA chart significantly outperforms the two lower-sided EWMA charts developed similar to their two existing upper-sided EWMA charts for detecting decreases in dispersion. Moreover, the proposed two-sided EWMA chart provides much better sensitivity than the two two-sided EWMA charts generalized from the two existing upper-sided EWMA charts for detecting overall changes in dispersion.     

Principal component analysis-based control charts for multivariate nonnormal distributions

Multivariate control charts have been widely used in many industries to monitor and diagnose processes characterized by a large number of quality characteristics. Usually, these characteristics are highly correlated with each other. The direct use of conventional multivariate control charts for situations with highly correlated characteristics may lead to increased rates of false alarms. Principal component analysis (PCA) control charts have been widely used to address problems posed by such high correlations by transforming the set of correlated variables to an uncorrelated set of variables and then identifying the PCs with highest contribution which then allows one to reduce dimensionality. However, an assumption that the data are normally distributed underlies the construction of the control limits of traditional PCA control charts. This assumption has limited the use of PCA control charts in nonnormal situations found in many modern systems. This study presents the development of nonparametric PCA control charts that do not require any distributional assumptions for their construction. We propose to use nonparametric techniques, kernel density estimation, and bootstrapping to establish the control limits of these charts. A simulation study was conducted to evaluate the performance of the proposed charts and compare them with traditional PCA control charts. The comparative performance in terms of average run length showed that the proposed nonparametric PCA control charts performed better than the parametric PCA control charts in nonnormal situations.     

Applying a coupled expert system to quality control charts

Quality control charts are a common tool used for control of a manufacturing process. Expert systems can serve as a valuable tool to automate the different phases in the use of quality control charts. The authors have developed a coupled expert system to select a proper control chart, optimize the design of the selected control chart, analyze the chart to identify an out-of-control process and suggest the reason(s) for an out-of-control process. The intent of this paper is to discuss the main features of this coupled expert system.

An expert system shell called PCPLUS helps a user select the most appropriate control chart and identify the reason(s) for a process to be out-of-control. FORTRAN was used to implement the modules for design, plotting, and analysis of the selected control chart.     

Pattern recognition of control charts using artificial neural networks—analyzing the effect of the training parameters

In this paper, we have utilized artificial neural networks (ANN) for pattern recognition of the most common patterns which occur in quality control charts. After detecting such patterns, it is possible to relate these patterns to their causes. This could find extreme importance for on-line quality monitoring and on-line trouble shooting. It could be possible to detect problems before they become serious and the operator has to shut the line down or the process may produce defective parts. In this work, we have attempted to explore the effect of the training parameters on the performance of the neural network. The training parameters are important because they emphasis the required performance and the accuracy required from the neural network. A resolution IV fractional factorial experiment is utilized to explore a portion of the range of selected parameters to obtain better performance of the neural network. The results showed that many parameters usually assigned by experience such as minimum shift, shift range, population size and shift percentage, have significant effect on the performance of the ANN, while others such as network size and window size do not have major significance on the performance of the net.