Concurrent Control Chart Pattern  Recognition: A Systematic Review

García, Ethel; Peñabaena‐Niebles, Rita; Jubiz-Diaz, María; Perez-Tafur, Angie

doi:10.3390/math10060934

Cited by 14 publications

(4 citation statements)

References 84 publications

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“…The control chart is a statistical quality control tool in the form of a graphical display of quality characteristics that have been measured or quantified in a sample according to time or observations (García, Peñabaena-Niebles, Jubiz-Diaz, & Perez-Tafur, 2022). The control chart was first introduced by DR. Walter Andrew Shewhart of Bell Telephone Laboratories, United States, in 1924 to eliminate abnormal variations by separating variations caused by common causes.…”

Section: Methodsmentioning

confidence: 99%

The Implementation of Control Charts as a Verification Tool in a Time Series Model for COVID-19 Vaccine Participants in Pontianak

Imro'ah,

Huda,

Pratama

2023

ComTech

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Vaccines are the primary weapon used to stop the outbreak, especially amid the COVID-19 pandemic. Thus, supplying vaccines to control the COVID-19 pandemic is essential, especially in minimizing the incidence and achieving herd immunity to break the chain of COVID-19. West Kalimantan has taken firm anticipatory steps to prevent COVID-19 in the form of a vaccination program in Indonesia. The highest vaccination achievement occurs in Pontianak City, the province’s capital. The research analyzed data on vaccine participants in Pontianak using time series analysis. In addition, the residuals from the time series model were used as observations in constructing the control chart. The research also analyzes the accuracy of the time series model using the Individual Moving Range (IMR) control chart. The results show that the ARIMA model (5,0,2) is the best because it fulfills the assumption of white noise. However, the ARIMA (5,0,2) model is inaccurate in making predictions because the residuals from the ARIMA (5,0,2) model are out of control (based on the IMR control chart). Hence, it is necessary to evaluate in determining the time series model. It can be analyzed using a control chart. Therefore, measuring the model’s accuracy on the best model is essential in predicting several subsequent periods.

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Section: Methodsmentioning

confidence: 99%

The Implementation of Control Charts as a Verification Tool in a Time Series Model for COVID-19 Vaccine Participants in Pontianak

Imro'ah,

Huda,

Pratama

2023

ComTech

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“…The occurrence of concurrent patterns suggests that the manufacturing process is simultaneously influenced by several assignable causes. In a recent study, García et al [28] conducted a comprehensive review of the research on concurrent patterns.…”

Section: Related Workmentioning

confidence: 99%

Multivariate Process Control Chart Pattern Classification Using Multi-Channel Deep Convolutional Neural Networks

et al. 2023

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Statistical process control (SPC) charts are commonly used to monitor quality characteristics in manufacturing processes. When monitoring two or more related quality characteristics simultaneously, multivariate T2 control charts are often employed. Like univariate control charts, control chart pattern recognition (CCPR) plays a crucial role in multivariate SPC. The presence of non-random patterns in T2 control charts indicates that a process is influenced by one or more assignable causes and that corrective actions should be taken. In this study, we developed a deep learning-based classification model for recognizing control chart patterns in multivariate processes. To address the problem of the insufficient representation of one-dimensional (1D) data, we explore the advantages of using two-dimensional (2D) image data obtained from a threshold-free recurrence plot. A multi-channel deep convolutional neural network (MCDCNN) model was developed to incorporate both 1D and 2D representations of control chart data. This model was tested on multivariate processes with different covariance matrices and compared with other traditional algorithms. Moreover, the effects of imbalanced datasets and dataset size on classification performance were analyzed. Simulation studies revealed that the developed MCDCNN model outperforms other techniques in identifying multivariate non-random patterns. For the most significant one, our proposed MCDCNN method achieved a 10% improvement over traditional methods. The overall results suggest that the developed MCDCNN model can be beneficial for intelligent SPC.

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“…Stage 1: Data Generation. As discussed in one review [32], due to the lack of fully documented public databases, 41 out of 44 papers evaluated the performance of the CCPR models by simulated data, while only 3 papers implemented real data in their studies. In this study, a large number of CCP samples are generated by the Monte-Carlo method, which is usually applied to generate CCPs both for training and testing.…”

Section: The Proposed 1dcnn-gwosvm Schemementioning

confidence: 99%

A Novel Scheme of Control Chart Patterns Recognition in Autocorrelated Processes

Wu,

Hou,

Lei

et al. 2023

Mathematics

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Control chart pattern recognition (CCPR) can quickly recognize anomalies in charts, making it an important tool for narrowing the search scope of abnormal causes. Most studies assume that the observations are normal, independent and identically distributed (NIID), while the assumption of independence cannot always be satisfied under continuous manufacturing processes. Recent research has considered the existence of autocorrelation, but the recognition rate is overestimated. In this paper, a novel scheme is proposed to recognize control chart patterns (CCPs) in which the inherent noise is autocorrelated. By assuming that the inherent noise follows a first-order autoregressive (AR (1)) process, the one-dimensional convolutional neural network (1DCNN) is applied for extracting features in the proposed scheme, while the grey-wolf-optimizer-based support vector machine (GWOSVM) is employed as a classifier. The simulation results reveal that the proposed scheme can effectively identify seven types of CCPs. The overall accuracy is 89.02% for all the autoregressive coefficients, and the highest accuracy is 99.43% when the autoregressive coefficient is on the interval (−0.3, 0]. Comparative experiments indicate that the proposed scheme has great potential to identify CCPs in autocorrelated processes.

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Concurrent Control Chart Pattern Recognition: A Systematic Review

Cited by 14 publications

References 84 publications

The Implementation of Control Charts as a Verification Tool in a Time Series Model for COVID-19 Vaccine Participants in Pontianak

The Implementation of Control Charts as a Verification Tool in a Time Series Model for COVID-19 Vaccine Participants in Pontianak

Multivariate Process Control Chart Pattern Classification Using Multi-Channel Deep Convolutional Neural Networks

A Novel Scheme of Control Chart Patterns Recognition in Autocorrelated Processes

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