Recognition and quality assessment of data charts in mixed-mode documents

Data charts can be used to effectively compress large amounts of complex information and can convey information in an efficient and succinct manner. It is now easier to create data charts by using a variety of automated software systems. These data charts are routinely inserted in text documents and are widely disseminated over many different media. This study addresses the problem of finding goodness of data charts in mixed-mode documents. The quality of the graphics can be used to assist the document development process as well as to serve as an additional criterion for search engines like Google and Yahoo. The quality measures are motivated by principles of visual learning and are based on research in educational psychology and cognitive theories and use attributes of both the graphic and its textual context. We have implemented the approach and evaluated its effectiveness using a set of documents compiled from the Web. Results of a human study shows that the proposed quality measures have a high correlation with the quality ratings of the users for each of the five classes of data charts studied in this research.     

A neural network approach to characterize pattern parameters in process control charts

Abnormal patterns on manufacturing process control charts can reveal potential quality problems due to assignable causes at an early stage, helping to prevent defects and improve quality performance. In recent years, neural networks have been applied to the pattern recognition task for control charts. The emphasis has been on pattern detection and identification rather than more detailed pattern parameter information, such as shift magnitude, trend slope, etc., which is vital for effective assignable cause analysis. Moreover, the identification of concurrent patterns (where two or more patterns exist together) which are commonly encountered in practical manufacturing processes has not been reported. This paper proposes a neural network-based approach to recognize typical abnormal patterns and in addition to accurately identify key parameters of the specific patterns involved. Both single and concurrent patterns can be characterized using this approach. A sequential pattern analysis (SPA) design was adopted to tackle complexity and prevent interference between pattern categories. The performance of the model has been evaluated using a simulation approach, and numerical and graphical results are presented which demonstrate that the approach performs effectively in control chart pattern recognition and accurately identifies the key parameters of the recognized pattern(s) in both single and concurrent pattern circumstances.     

On-line analysis of out-of-control signals in multivariate manufacturing processes using a hybrid learning-based model

Advanced automatic data acquisition is now widely adopted in manufacturing industries and it is common to monitor several correlated quality variables simultaneously. Most of multivariate quality control charts are effective in detecting out-of-control signals based upon an overall statistics in multivariate manufacturing processes. The main problem of such charts is that they can detect an out-of-control event but do not directly determine which variable or group of variables has caused the out-of-control signal and what is the magnitude of out of control. This study presents a hybrid learning-based model for on-line analysis of out-of-control signals in multivariate manufacturing processes. This model consists of two modules. In the first module using a support vector machine-classifier, type of unnatural pattern can be recognized. Then by using three neural networks for shift mean, trend and cycle it can be recognized magnitude of mean shift, slope of trend and cycle amplitude for each variable simultaneously in the second module. The performance of the proposed approach has been evaluated using two examples. The output generated by trained hybrid model is strongly correlated with the corresponding actual target value for each quality characteristic. The main contributions of this work are recognizing the type of unnatural pattern and classification major parameters for shift, trend and cycle and for each variable simultaneously by proposed hybrid model.     

A power function model for determining sample sizes for the operations of multivariate control charts

Using the hypothesis-testing approach, we develop a model for determining sample sizes for the operation of multivariate control charts. A simple solution procedure that can be processed on any personal or small computer is also developed. The effect of correlation between pairs of variables on the performance of the model is studied. The performances of multivariate and univariate control charts are compared under the model. Before the development of the model, a brief review of multivariate test of hypothesis and multivariate control charts was done. The model is recommended for any quality control engineer who may like to specify a desired level of protection against inferior quality.     

A new approach to detecting the process changes for multistage systems

The study aims to develop a new control chart model suitable for monitoring the process quality of multistage manufacturing systems.Considering both the auto-correlated process outputs and the correlation occurring between neighboring stages in a multistage manufacturing system, we first propose a new multiple linear regression model to describe their relationship. Then, the multistage residual EWMA and CUSUM control charts are used to monitor the overall process quality of multistage systems. Moreover, an overall run length (ORL) concept is adopted to compare the detecting performance for various multistage residual control charts. Finally, a numerical example with oxide thickness measurements of a three-stage silicon wafer manufacturing process is given to demonstrate the usefulness of our proposed multistage residual control charts in the Phase II monitoring. A computerized algorithm can also be written based on our proposed scheme for the multistage residual EWMA/CUSUM control charts and it may be further converted to an expert and intelligent system. Hopefully, the results of this study can provide a better alternative for detecting process change and serve as a useful guideline for quality practitioners when monitoring and controlling the process quality of multistage systems with auto-correlated data.     

A neural network-based procedure for the monitoring of exponential mean

Control charts are widely used for both manufacturing and service industries. Cumulative sum (CUSUM) charts are known to be very sensitive in detecting small shifts in the mean. In this paper, we propose a neural network as an alternative approach to CUSUM charts when monitoring exponential mean. The performance of neural network was evaluated by estimating the average run lengths (ARLs) using simulation. The results obtained with simulated data suggest that control scheme based on neural network is significantly more sensitive to process shifts than CUSUM charts. This research also examines the feasibility of using CUSUM chart and neural network together in detecting process mean shifts. The results indicate that using the two methods in combination is more effective than using the methods separately.     

Simple linear profiles monitoring in the presence of within profile autocorrelation

Quality of some processes or products can be characterized effectively by a function referred to as profile. Many studies have been done by researchers on the monitoring of simple linear profiles when the observations within each profile are uncorrelated. However, due to spatial autocorrelation or time collapse, this assumption is violated and leads to poor performance of the proposed control charts. In this paper, we consider a simple linear profile and assume that there is a first order autoregressive model between observations in each profile. Here, we specifically focus on phase II monitoring of simple linear regression. The effect of autocorrelation within the profiles is investigated on the estimate of regression parameters as well as the performance of control charts when the autocorrelation is overlooked. In addition, as a remedial measure, transformation of Y-values is used to eliminate the effect of autocorrelation. Four methods are discussed to monitor simple linear profiles and their performances are evaluated using average run length criterion. Finally, a case study in agriculture field is investigated.     

A new auxiliary information based cumulative sum median control chart for location monitoring

Control charts are commonly used tools in statistical process control for the detection of shifts in process parameters. Shewhart-type charts are efficient for large shift values, whereas cumulative sum (CUSUM) charts are effective in detecting medium and small shifts. Control chart use commonly assumes that data are free of outliers and parameters are known or correctly estimated based on an in-control process. In practice, these assumptions are not often true because some processes occasionally have outliers. Monitoring the location parameter is usually based on mean charts, which are seriously affected by violations of these assumptions. In this paper we propose several CUSUM median control charts based on auxiliary variables, and offer comparisons with their corresponding mean control charts. To monitor the location parameter, we examined the performance of mean and median control charts in the presence and absence of outliers. Both symmetric and non-symmetric processes were studied to examine the properties of the proposed control charts to monitor the location parameter using CUSUM control charts. We used different run length measures to study in-control and out-of-control performances of CUSUM charts. Results revealed that our proposed control charts perform much better than the traditional charts in the presence of outliers. A real application of our study was provided using data on concrete compressive strength as it relates to the quality of cement manufacturing.

     

The design of a visual display for the presentation of statistical quality control information to operators on the plant floor

We applied human-centered design methodologies to enhance the presentation of product quality information to operators on a manufacturing plant floor. First, an initial visual display concept that integrated a pictorial representation of a product with standard graphical and tabular information about the product's quality was refined through iterative design and testing. A preliminary study was then conducted to determine the specific features of such a display (termed a pictorial control chart) from among eight candidate detail designs. Finally, a formal study was conducted to compare the performance of operators using this refined pictorial control chart design with their performance using a conventional control chart. Operators completed a quality control task in significantly less time using the pictorial control chart. There were no significant differences in the number of errors committed with the two charts. Subjective measures showed a significant preference for the pictorial control chart. Actual or potential applications of this research include the development of quality control tools that are useful to and usable by operators on the manufacturing plant floor.     

Statistically appraising process quality of affinity isolation experiments

Quality affinity isolation experiments are necessary to identify valid protein-protein interactions. Biological error, processing error, and random variability can reduce the quality of an experiment, and thus hinder the identification of protein interaction pairs. Appraising affinity isolation assay quality is essential to inferring protein associations. An important step of the assay is the mass spectrometric identification of proteins. To evaluate this step, a known mixture of proteins is processed through a mass spectrometer as a quality control mixture. If the mass spectrometer yields unexpected results, the process is currently qualitatively evaluated, tuned, and reset. Statistical quality control (SQC) procedures, including the use of cumulative sum, the individual measurement, and moving range charts are implemented to analyze the stability of the mass spectrometric analysis. The SQC measures presented can assist in establishing preliminary control limits to identify an out-of-control process and investigate assignable causes for shifts in the process mean in real time.     

Optimum control limits for employing statistical process control in software process

There is increasing interest in using control charts for monitoring and improving software processes, particularly quality control processes like reviews and testing. In a control chart, control limits are established for attributes and, if any point falls outside the limits, it is assumed to be due to special causes that need to be identified and eliminated. If the control limits are too tight, they may raise too many "false alarms" and, if they are too wide, they may miss special situations. Optimal control limits will try to minimize the cost of these errors. In this paper, we develop a cost model for employing control charts for software processes using optimum control limits which can be determined. Our applications of the model suggest that, for quality control processes like inspection, optimum control limits may be tighter than those commonly used in manufacturing. We have also implemented this model as a Web service that can be used for determining optimum control limits.     

应用统计过程控制监控软件测试过程

统计过程控制(SPC)是通过使用控制图来制定过程决策和预测过程行为的一种质量控制方法.SPC的方法用于软件过程,可以通过描述过程行为来监控过程的稳定性.讨论了将SPC应用于软件测试过程,针对测试过程中所度量的不同分布形式的数据而采用不同计算方式应用SPC的控制图,然后根据控制图判断测试过程是否稳定,并分析可能存在的可归属原因.     

Key human factors and their effects on human centered assembly performance

Today in the increasingly competitive market, consumers prefer to have a great variety of products to choose from; this preference is often coupled with demands for a relatively smaller lot size, shorter lead time, higher quality and lower cost. Consequently, manufacturing companies are being forced to consistently increase flexibility and responsiveness of their production systems in order to accommodate changes of the fluctuating market. Among various forms of production systems, human-centred manufacturing systems can offer such a capability in dealing with product variations and production volumes as human workers can always adapt themselves to perform multiple tasks after a learning process. However, human performance can also be unpredictable and it may alter due to varying psychological and physiological states, which are often overlooked by researchers when designing, implementing or evaluating a manufacturing system. This paper presents a study aiming to address these issues by exploring human factors and their interactions that may affect human performance on human-centred assembly systems. The study was carried out based on a literature review and an industrial survey. Critical system performance indicators, which are affected by human factors, were evaluated and the most significant human factors were identified using the fuzzy extent analysis method. The research findings show that experience is the most significant human factor that affects individual human performance, compared to age and general cognitive abilities in human-centred assembly. By contrast, both human reaction time and job satisfaction have the least effect on human performance. The significance of ageing on human performance was also studied and it was concluded that average assembly time of human workers rises by average 1% per year after the age of 38 years old.     

Trend Autoregressive Model Exact Run Length Evaluation on a Two-Sided Extended EWMA Chart

The Extended Exponentially Weighted Moving Average (extended EWMA) control chart is one of the control charts and can be used to quickly detect a small shift. The performance of control charts can be evaluated with the average run length (ARL). Due to the deriving explicit formulas for the ARL on a two-sided extended EWMA control chart for trend autoregressive or trend AR(p) model has not been reported previously. The aim of this study is to derive the explicit formulas for the ARL on a two-sided extended EWMA control chart for the trend AR(p) model as well as the trend AR(1) and trend AR(2) models with exponential white noise. The analytical solution accuracy was obtained with the extended EWMA control chart and was compared to the numerical integral equation (NIE) method. The results show that the ARL obtained by the explicit formula and the NIE method is hardly different, but the explicit formula can help decrease the computational (CPU) time. Furthermore, this is also expanded to comparative performance with the Exponentially Weighted Moving Average (EWMA) control chart. The performance of the extended EWMA control chart is better than the EWMA control chart for all situations, both the trend AR(1) and trend AR(2) models. Finally, the analytical solution of ARL is applied to real-world data in the health field, such as COVID-19 data in the United Kingdom and Sweden, to demonstrate the efficacy of the proposed method.     

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.     

Economic design of time-between-events control chart system

This article presents the economic design of the control chart system consisting of several individual control charts based on time-between-events (TBE) data for monitoring multistage manufacturing processes. The design algorithm considers all the TBE charts within a system in an integrative and optimal manner. Numerical studies show that the proposed design algorithm improves the performance characteristics (in terms of profit) considerably. The proposed control chart system is easy to understand and operate, and thus the floor operators can utilize and understand it as easily as for the traditional system.     

Variable control scheme in the cascade processes

The article considers the variables process control scheme for cascade processes. We construct variable sample sizes and sampling intervals (VSSI) control charts to effectively monitor the input variable and the output variable produced by a cascade process. The performance of the proposed VSSI control charts is measured by the adjusted average time to signal derived by a Markov chain approach. An example of the metallic film thickness of the computer connectors system shows the application and the performance of the proposed VSSI control charts in detecting shifts in means of the cascade process. Furthermore, the performance of the proposed VSSI control charts and the fixed sample sizes and sampling intervals control charts are compared by numerical analysis results. These demonstrate that the former is much faster in detecting small and medium shifts. The optimum VSSI control charts are also proposed using optimization technique when quality engineers cannot specify the values of the variable sample sizes and sampling intervals. It has been found that the optimum VSSI control charts work and are thus suggested whenever quality engineers cannot specify the values of variable sample sizes and sampling intervals. Furthermore, the impacts of misusing Shewhart charts to monitoring the process means on the cascade process are also investigated.     

Development of fuzzy process control charts and fuzzy unnatural pattern analyses

Many problems in scientific investigation generate nonprecise data incorporating nonstatistical uncertainty. A nonprecise observation of a quantitative variable can be described by a special type of membership function defined on the set of all real numbers called a fuzzy number or a fuzzy interval. A methodology for constructing control charts is proposed when the quality characteristics are vague, uncertain, incomplete or linguistically defined. Fuzzy set theory is an inevitable tool for fuzzy control charts as well as other applications subjected to uncertainty in any form. The vagueness can be handled by transforming incomplete or nonprecise quantities to their representative scalar values such as fuzzy mode, fuzzy midrange, fuzzy median, or fuzzy average. Then crisp methods may be applied to those representative values for control chart decisions as “in control” or “out of control”. Transforming the vague data by using one of the transformation methods may result in biased decisions since the information given by the vague data is lost by the transformation. Such data needs to be investigated as fuzzy sets without transformation, and the decisions based on the vague data should not be concluded with an exact decision. A “direct fuzzy approach (DFA)” to fuzzy control charts for attributes under vague data is proposed without using any transformation method. Then, the unnatural patterns for the proposed fuzzy control charts are defined using the probabilities of fuzzy events.     

Uncovering the requirements of cognitive work

OBJECTIVE: In this article, the author provides an overview of cognitive analysis methods and how they can be used to inform system analysis and design. BACKGROUND: Human factors has seen a shift toward modeling and support of cognitively intensive work (e.g., military command and control, medical planning and decision making, supervisory control of automated systems). Cognitive task analysis and cognitive work analysis methods extend traditional task analysis techniques to uncover the knowledge and thought processes that underlie performance in cognitively complex settings. METHODS: The author reviews the multidisciplinary roots of cognitive analysis and the variety of cognitive task analysis and cognitive work analysis methods that have emerged. RESULTS: Cognitive analysis methods have been used successfully to guide system design, as well as development of function allocation, team structure, and training, so as to enhance performance and reduce the potential for error. CONCLUSIONS: A comprehensive characterization of cognitive work requires two mutually informing analyses: (a) examination of domain characteristics and constraints that define cognitive requirements and challenges and (b) examination of practitioner knowledge and strategies that underlie both expert and error-vulnerable performance. A variety of specific methods can be adapted to achieve these aims within the pragmatic constraints of particular projects. APPLICATION: Cognitive analysis methods can be used effectively to anticipate cognitive performance problems and specify ways to improve individual and team cognitive performance (be it through new forms of training, user interfaces, or decision aids).     

Fault detection and diagnosis using statistical control charts and artificial neural networks

In order to operate a successful plant or process, continuous improvement must be made in the areas of safety, quality and reliability. Central to this continuous improvement is the early or proactive detection and correct diagnosis of process faults. This research examines the feasibility of using cumulative summation (CUSUM) control charts and artificial neural networks together for fault detection and diagnosis (FDD). The proposed FDD strategy was tested on a model of the heat transport system of a CANDU nuclear reactor.The results of the investigation indicate that a FDD system using CUSUM control charts and a radial basis function (RBF) neural network is not only feasible but also of promising potential. The control charts and neural network are linked by using a characteristic fault signature pattern for each fault which is to be detected and diagnosed. When tested, the system was able to eliminate all false alarms at steady state, promptly detect six fault conditions, and correctly diagnose five out of the six faults. The diagnosis for the sixth fault was inconclusive.