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Technical and technological developments in recent decades have stimulated the rapid development of methods and tools in the field of statistical process quality control, which also includes control charts. The principle of control charts defined by Dr. W. Shewhart has been known for more than 100 years. Since then, they have been used in many industries to monitor and control processes. This paper aims to assess the possibilities of use and the selection of the most suitable type of control chart for monitoring the quality of a process depending on its nature. This tool should help operators in monitoring coking time, which is one of the important control variables affecting the quality of coke production. The autoregressive nature of the variable being monitored was considered when selecting a suitable control chart from the group of options considered. In addition to the three traditional types of control charts (Shewhart's, CUSUM, and EWMA), which were applied to the residuals of individual values of different types of ARIMA models, various statistical tests, and plots, a dynamic EWMA control chart was also used. Its advantage over traditional control charts applied to residuals is that it works with directly measured coking time data. This chart is intended to serve as a method to monitor the process. Its role is only to alert the process operator to the occurrence of problems with the length of the coking time. [ABSTRACT FROM AUTHOR]

The advent of modern data acquisition and computing techniques has enabled high-speed monitoring of high-dimensional processes. The short sampling interval makes the samples temporally correlated, even if there is no underlying autocorrelation among covariates. In this study, we introduce a new process monitoring scheme in a Bayesian framework. The key strategy of this study is to incorporate sequential observations into the estimation procedure for the parameters of interest to update the prior distribution. Based on the updated prior, we obtain the most appropriate estimation of the process parameters at each sampling epoch by maximizing the posterior probability. In addition, conventional statistical process control and monitoring methodologies suffer from the “curse of dimensionality.” The closed form of the estimate developed in this study through Bayesian updates enables the proposed method to be effective for high-dimensional process monitoring. Various simulation studies demonstrate the superiority of the proposed scheme in the high-speed monitoring of high-dimensional processes. Moreover, a few sample paths of the estimated mean in a procedure of the proposed method are illustrated to provide practitioners with insights into the monitoring and control of the process. Finally, we provide a real-life application to illustrate the proposed method.

In recent years, it has been proven that integrating statistical process control, maintenance policy, and production can bring more benefits for the entire production systems. In the literature of triple-concept integrated models, it has generally been assumed that the observations are independent. However, the existence of correlated structures in some practical applications put the traditional control charts in trouble. The mixed EWMA-CUSUM (MEC) control chart and the ARMA control chart are effective tools to monitor the mean of autocorrelated processes. This paper proposes an integrated model subject to some constraints for determining the decision variables of triple concepts in the presence of autocorrelated data. Three types of autocorrelated processes are investigated to study their effects on the results. Moreover, the results of the MEC and ARMA charts are compared. Due to the complexity of the model, a particle swarm optimization (PSO) algorithm is applied to select optimal decision variables. An industrial example and extensive comparisons are provided

Technical and technological developments in recent decades have stimulated the rapid development of methods and tools in the field of statistical process quality control, which also includes control charts. The principle of control charts defined by Dr. W. Shewhart has been known for more than 100 years. Since then, they have been used in many industries to monitor and control processes. This paper aims to assess the possibilities of use and the selection of the most suitable type of control chart for monitoring the quality of a process depending on its nature. This tool should help operators in monitoring coking time, which is one of the important control variables affecting the quality of coke production. The autoregressive nature of the variable being monitored was considered when selecting a suitable control chart from the group of options considered. In addition to the three traditional types of control charts (Shewhart’s, CUSUM, and EWMA), which were applied to the residuals of individual values of different types of ARIMA models, various statistical tests, and plots, a dynamic EWMA control chart was also used. Its advantage over traditional control charts applied to residuals is that it works with directly measured coking time data. This chart is intended to serve as a method to monitor the process. Its role is only to alert the process operator to the occurrence of problems with the length of the coking time.

Defect identification of tiny-scaled electronics components with high-speed throughput remains an issue in quality inspection technology. Convolutional neural networks (CNNs) deployed in automatic optical inspection (AOI) systems are powerful for detecting defects. However, they focus on individual samples but suffer from poor process control and lack of monitoring and providing the online status regarding the production process. Integrating CNN and statistical process control models will empower high-speed production lines to achieve proactive quality inspection. With the performance of the average run length for a certain range of the shifts, the proposed control chart has high detection performance for small mean shifts in quality. The proposed control chart is successfully applied to an electronic conductor manufacturing process. The proposed model facilitates a systematic quality inspection for tiny electronics components in a high-speed production line. The CNN-based AOI model empowered by the proposed control chart enables quality checking at the individual product level and process monitoring at the system level simultaneously. The contribution of the present study lies in the proposed process control framework integrating with the CNN-based AOI model in which a residual-based mixed multivariate cumulative sum (CUSUM) and exponentially weighted moving average (EWMA) control chart for monitoring online multivariate autocorrelated processes to efficiently detect defects. [ABSTRACT FROM AUTHOR]

This work used two methods for Monitoring and control of autocorrelated processes based on time series modeling. The first method was the simultaneous monitoring of common and assignable causes. This method included applying five steps of data gathering, normality test, autocorrelation test, model selection and control chart selection on all non-stationary process observations. The second method was a novel one for the separate monitoring and control of common and assignable causes. In this method, the process was divided into the parts with and without assignable causes. The first method was greatly non-stationary due to not separating common and assignable causes. This method also implied that the common causes were hidden in the process. The novel method for the separate monitoring of common and assignable causes could turn the process into a stationary one, leading to identifying, monitoring, and controlling common causes without any interference from the assignable causes. The results showed that, unlike the first method, the second method could be very sensitive to the common causes; it could, therefore, suitably monitor, identify and control both assignable and common causes. The current work was aimed to use control charts to monitor and control the bootomhole pressure during the drilling operation.

In this article, an exponential weighted moving average chart based on a likelihood ratio test is developed to monitor the mean and variance shifts simultaneously for autocorrelated processes. A simple method is used to transform the positively autocorrelated data to the negatively autocorrelated data. The average run length of the proposed chart is derived from a simulation approach. The performance of our proposed chart is compared with some existing charts. The results show that the proposed chart provides better performance for detecting a wide range of shifts in the process mean and variance simultaneously. Additionally, the economic performance of different charts under the first‐order autoregressive model is provided. A real example of a stepper motor in the heating, ventilation, and air conditioning module is used to demonstrate the application of the proposed method. [ABSTRACT FROM AUTHOR]

With the growth of automation in process industries, there is correlation in the process variables. Deep learning has achieved many great successes in image and visual analysis. This paper concentrates on developing a deep recurrent neural network (RNN) model to characterize process variables at vary time lags, and then a residual chart is developed to detect mean shifts in autocorrelated processes. The experiment results indicate that the RNN‐based residual chart outperforms other typical methods (eg, autoregressive [AR]‐based control chart, back propagation network [BPN]‐based residual chart). This paper provides guideline for deep learning technique employed as an effective tool in autocorrelated process control. [ABSTRACT FROM AUTHOR]

Monitoring the variability of a process is as important as monitoring its mean. The Shewhart S chart is commonly used in many manufacturing industries to monitor the process variability. This chart assumes that generated process data are normally and independently distributed. The assumption of independence may be unrealistic in industries, as many processes inherently produce autocorrelated data. In this paper, we investigate the effect of autocorrelation on the statistical performance of the S chart. The first-order autoregressive (AR (1)) process is used to model the data from an autocorrelated process of interest. The ARL performance of the S chart for variability derived independent observations is compared with autoregressive process AR (1) with different degrees of correlation. It is found that the correlation structure increases the rate of false alarm of the S chart. Also, the chart is found much less sensitive in detecting changes in the process standard deviation. [ABSTRACT FROM AUTHOR]

We propose to use AR-Sieve Bootstrap in the construction of a control chart of an autocorrelated process influenced by multiple exogenous inputs. The control charts are compared with Exponentially Weighted Moving Average (EWMA) control chart through a simulation study. AR-Sieve bootstrap control limits are narrower than EWMA control limits. While the proposed method yields a higher rate of false alarms, it is quick in detecting even minimal structural changes. [ABSTRACT FROM AUTHOR]

Traditionally, the process capability index is developed assuming that the process output data are independent and follow normal distribution. However, in most environmental cases, the process data have more than one quality characteristic and exhibit property of autocorrelation. We propose two novel multivariate process capability indices for autocorrelated data, NMACp and NMACpm, for the nominal-the-best case. For the smaller-the-better case, Γ(0) is used to modify the ND index and a new multivariate autocorrelated process capability index NMACpu is derived. Furthermore, a simulation study is conducted to compare the performance of the various multivariate autocorrelated indices. The simulation results show that the actual nonconforming rates can be correctly reflected by our proposed indices, which outperform the previous Cpm, MCp, MCpm, NMCp, NMCpm, and ND indices under different time series models. Thus, our proposed capability indices can be used in evaluating the performance of multivariate autocorrelated processes. Finally, a realistic example in hydrological application further demonstrates the usefulness of our proposed capability indices. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Traditionally, the process capability index is developed by assuming that the process output data are independent and follow normal distribution. However, in most environmental cases, the process data are autocorrelated. The autocorrelated process, if unrecognized as an independent process, can lead to erroneous decision making and unnecessary quality loss. In this paper, three new capability indices with unbiased estimators are proposed to relieve the independence assumption for the-nominal-the-best and the-smaller-the-better cases. Furthermore, we use mean squared error (MSE) and mean absolute percent error (MAPE) to compare the accuracy of our proposed indices to previous autocorrelated indices. The results show that our proposed capability indices outperform the predecessors. [ABSTRACT FROM AUTHOR]

We propose a new method for monitoring changes in geometric profiles of objects, where the geometric profiles may change through various modes in a dynamic process. This work is motivated by the need for monitoring changes in geometric shape and sizes of nanoparticles during their chemical self-assembly process; the changes often occur through many different modes before converging to the final state. The proposed multimode geometric-profile monitoring method addresses three issues specific to this process, all of which have never been addressed together by existing process-monitoring methods--profiling of functional data, monitoring of multimode processes, and monitoring of time-correlated processes. The new profile-monitoring method consists of two phases. In phase I, we characterize multiple modes of geometric shape changes under in-control process conditions given a sequence of geometric observations on products. We propose using a mixture of time-series models for this characterization and present an exact Gibbs sampling procedure for Bayesian estimation of model parameters. In phase II, we test whether a new observation of product geometries sampled at a certain time in the current process run exhibits significant out-of-control symptoms. We propose a Bayes factor score-based criteria for this testing. The proposed method is empirically verified using simulated datasets and a real dataset from a nanoparticle self-assembly process. [ABSTRACT FROM AUTHOR]

The expression of residual is obtained according to its dynamic response to mean shift, then the distribution of T statistic applied to the residual is derived, thus the probability of the T statistic lying outside the control limit is calculated. The above-mentioned results are substituted into the infinite definition expression of the average run length (ARL), and then the final finite ARL expression is obtained. An example is used to demonstrate the procedures of the proposed method. In the comparative study, eight autocorrelated processes and four different mean shifts are performed, and the ARL values of the proposed method are compared with those obtained by simulation method with 50,000 replications. The accuracy of the proposed method can be illustrated through the comparative results. [ABSTRACT FROM AUTHOR]

There are two major approaches in dealing with autocorrelated process data in process control, that is, residual-based approaches and methods that modify control limits to adjust for autocorrelation. We proposed a methodology for constructing control charts for autocorrelated process data using the AR-sieve bootstrap. The simulation study illustrates the relative advantage of the AR-sieve bootstrap control chart with respect to the in-control and out-of-control run length and false alarm rate. The proposed methodology works even for small sample sizes and conditions of the near nonstationarity of the generating process. The proposed AR-sieve bootstrap control chart presents the advantage of being distribution-free for certain class of linear models as well as the tracking of actual process observations instead of model residuals, thus facilitating the implementation during actual plant operations. Copyright © 2011 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This article proposes a new method to develop multiscale monitoring control charts for an autocorrelated process that has an underlying unknown ARMA(2, 1) model structure. The Haar wavelet transform is used to obtain effective monitoring statistics by considering the process dynamic characteristics in both the time and frequency domains. Three control charts are developed on three selected levels of Haar wavelet coefficients in order to simultaneously detect the changes in the process mean, process variance, and measurement error variance, respectively. A systematic method for automatically determining the optimal monitoring level of Haar wavelet decomposition is proposed that does not require the estimation of an ARMA model. It is shown that the proposed wavelet-based Cumulative SUM (CUSUM) chart on Haar wavelet detail coefficients is only sensitive to the variance changes and robust to process mean shifts. This property provides the separate monitoring capability between a variance change and a mean shift, which shows its advantage by comparison with the traditional CUSUM monitoring chart. For the purpose of mean shift detection, it is also shown that using the proposed wavelet-based Exponentially Weighted Moving Average (EWMA) chart to monitor Haar wavelet scale coefficients will more successfully detect small mean shifts than direct-EWMA charts. [ABSTRACT FROM PUBLISHER]

Abstract: Process monitoring and diagnosis have been widely recognized as important and critical tools in system monitoring for detection of abnormal behavior and quality improvement. Although traditional statistical process control (SPC) tools are effective in simple manufacturing processes that generate a small volume of independent data, these tools are not capable of handling the large streams of multivariate and autocorrelated data found in modern systems. As the limitations of SPC methodology become increasingly obvious in the face of ever more complex processes, data mining algorithms, because of their proven capabilities to effectively analyze and manage large amounts of data, have the potential to resolve the challenging problems that are stretching SPC to its limits. In the present study we attempted to integrate state-of-the-art data mining algorithms with SPC techniques to achieve efficient monitoring in multivariate and autocorrelated processes. The data mining algorithms include artificial neural networks, support vector regression, and multivariate adaptive regression splines. The residuals of data mining models were utilized to construct multivariate cumulative sum control charts to monitor the process mean. Simulation results from various scenarios indicated that data mining model-based control charts performs better than traditional time-series model-based control charts. [Copyright &y& Elsevier]

The variable parameters (VP) [image omitted] control chart varies all control parameters from the current sample information, and results in more effective monitoring based on statistical and economic criteria. The usual assumption for designing a control chart is that the observations from the process are independent. However, for many processes, such as chemical processes, consecutive measurements are often highly correlated. In the present article, the observations are modeled as an AR(1) process plus a random error, and the properties of the VP [image omitted] charts are evaluated and studied under this model. Based on the study, the VP control scheme generally has better performance in detecting small mean shifts than the standard and the other adaptive [image omitted] charts. However, when the observations are highly autocorrelated, the complexity of the VP [image omitted] chart gives a negative effect on the performance. [ABSTRACT FROM AUTHOR]

The article examines a model-free (MF) test for independence between two time series. It assesses the performance of the test and compares it with the naive (N) test. It also looks into tests involving bootstrapping the MF and N tests. Results have shown that the MF test has a good performance on achieving the desired test size for all sample sizes.

Recent studies demonstrated that the adaptive &Xmacr; control charts are more efficient than fixed parmeters (FP) &Xmacr; control chart from statistical and economic criteria. The usual assumption for designing a control chart is that the observations from the process are independent. However, for many processes, such as chemical processes, consecutive measurements are often highly correlated, especially when the interval between samples is small. In the present paper, the observations are modeled as an AR(1) process plus a random error, and the properties of the variable sampling rate (VSR) &Xmacr; charts are evaluated and studied under this model. Based on the study, the VSR &Xmacr; chart is faster than the FP, variable sampling interval and variable sample size &Xmacr; control charts in detecting mean shifts. However, when the level of autocorrelation is high or the process mean shift is large, the advantage of the VSR &Xmacr; chart is reduced. Copyright © 2008 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Abstract: Traditional control charts assume that processes are serially independent, and autocorrelation among variables makes them unreliable. To handle this problem alternative charts estimate the time series structure of the process and use residuals for control. While in previous studies, estimation is performed using classical statistical methods or artificial neural networks, this study proposes to apply support vector regression (SVR) method for construction of a residuals Multivariate Cumulative Sum (MCUSUM) control chart, for monitoring changes in the process mean vector. Using simulated data, analysis and comparison of the proposed control chart with other charts show that SVR-based control chart is more effective in detecting small shifts in the mean vector. This fact makes the proposed chart a very promising method since the MCUSUM chart is, in practice, designed to detect small shifts in the process parameters. [Copyright &y& Elsevier]

1. The process standard deviation is proportional to the process mean (constant coefficient of variation). 2. The autocorrelated process. [ABSTRACT FROM AUTHOR]

The properties of the estimator of C[sub p] for autocorrelated data in presence of measurement errors are discussed. This work is motivated by the fact that while some efforts have been dedicated in the literature to the statistical properties of the capability index estimator when the data are autocorrelated, scarce attention has been given to the evaluation of these properties when sample data are affected by measurement errors. In this paper, for a first order stationary autoregressive process, the performances of the estimator of C[sub p] in the case of measurement errors are derived and compared with those obtained in the error free case. [ABSTRACT FROM AUTHOR]

Engineering Process Controllers (EPC) are frequently based on parametrized models. If process conditions change, the parameter estimates used by the controllers may become biased, and the quality characteristics will be affected. To detect such changes it is adequate to use Statistical Process Control (SPC) methods. The run length statistic is commonly used to describe the performance of an SPC chart. This paper develops approximations for the first two moments of the run length distribution of a one-sided Shewhart chart used to detect two types of process changes in a system that is regulated by a given EPC scheme: i) changes in the level parameter; ii) changes in the drift parameter. If the drift parameter shifts, it is further assumed that the form of the drift process changes from a linear trend under white noise (the in-control drift model) into a random walk with drift model. Two different approximations for the run length moments are presented and their accuracy is numerically analyzed. [ABSTRACT FROM AUTHOR]