Search

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.

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]

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]

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]

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]