Chart Scheme Research Articles

A one-chart scheme for joint monitoring of the two parameters of zero-inflated Poisson processes

The high-quality processes usually have more count of zeros than are expected under chance variation and are commonly modeled by zero-inflated Poisson (ZIP) distribution. A ZIP model has two parameters— ϕ ( ϕ ∈ [ 0 , 1 ] ) and λ ( λ >0). Often separate control charts are used for monitoring the two parameters. But a one-chart scheme for joint monitoring of the two parameters offers significant operational advantages. A few one-chart schemes for joint monitoring of the two parameters are reported in literature. However, the monitoring statistic of none of these schemes is defined directly on the observed quality characteristics. This leads to difficulty in understanding of these schemes by the practitioners. Any control charting scheme developed directly on the observed quality characteristic is intuitively appealing to the practitioners and can be easy to interpretations by the practitioners. In this article, a one-chart scheme (called Gamma chart) is developed considering average number of nonconformities in the samples as the monitoring statistic. The performance of the Gamma chart is studied via simulation. The results reveal that it efficiently detect the out-of-control process conditions resulting from moderate shifts in ϕ and/or λ . Finally, a case study from an Indian automobile industry is presented.

A novel adaptive CUSUM system for efficient process mean monitoring: An application in piston ring manufacturing process

The adaptive versions of charting tools are well-established statistical monitoring techniques for detecting unknown changes in the process over a range of shifts. A re-weighted adaptive CUSUM mean (RACUSUM) charting scheme has been proposed in this study for monitoring the range of shifts. The proposed RACUSUM statistic estimates the unknown changes in the ongoing process using an EWMA statistic by plugging in an unbiased shift estimator and choosing a suitable smoothing constant according to the estimated shift to update the slack value of the CUSUM statistic. By employing Monte Carlo simulation using the zero- and steady-state processes, the proposal’s run-length (RL) profiles have been calculated. The proposed RACUSUM chart shows more sensitive behavior under the shift delay process than in the case of zero-state. A comparative analysis between the proposal and competitors has been presented using average RL, extra quadratic loss (EQL), and the relative mean index (RMI) as metrics for 1-sided and 2-sided charting schemes. The robustness of the proposed 2-sided RACUSUM chart against non-normality has also been reported. The proposal’s applicability has been demonstrated using two cases, one from the artificial and the other from a dataset of piston ring manufacturing industrial process.

A novel control chart scheme for online social network monitoring using multivariate nonparametric profile techniques

Online social networks have become indispensable in modern life, facilitating knowledge sharing, social communication, and business marketing. To gain a deeper understanding of individuals' behavior within social networks, researchers have undertaken essential analytical tasks such as change point detection. Recently, nonparametric change point detection methods have attracted researchers' attention due to their generality and flexibility. However, existing methods exhibit limitations, including overlooking network structure, reliance on case-based network attributes, and neglecting the dynamic nature of data, which may have correlations in evolving social networks. In this study, we propose a novel multivariate mixed-effects nonparametric profile control (MENPC) algorithm to address these limitations. The advantage of MENPC relies on its unique point of view in approaching network data, where it incorporates the dynamic nature of data into the monitoring process without assuming internal independence of networks over time. Additionally, it takes into account the network structure by considering both nodal and network-level attributes. Furthermore, by introducing an updating trick formula, the proposed algorithm simplifies computations, effectively balancing memory and speed for online monitoring. We evaluate the effectiveness of MENPC through comprehensive numerical experiments using the degree correlated stochastic block model to simulate interactions in evolving online social networks. The results demonstrate MENPC's superior performance in terms of expected detection delay, showcasing its accuracy and efficiency in comparison to competing approaches including Wilson, and eigenvalue methods. Applying MENPC to the Enron email network dataset further confirms its significant progress in social network monitoring, expanding its potential for various applications.

Methodological proposal for the standardization of the Electronic Navigational Chart scheme in the Caribbean Sea

Since the approval of the use of Electronic Navigational Charts (ENC) as equivalent to paper nautical charts, hydrographic services worldwide began their production following existing cartographic schemes. This paper proposes to standardize ENC production schemes at the regional scale through a regular grid system, which unifies the scale of compilation and cell size by navigational purpose. The implementation of this system requires defining the origin of the grid and facilitates the nomenclature of the ENCs. Additionally, this standardization would help for the harmonized production of ENCs by improving their coverage. The proposed methodology is used to formulate an ENCs scheme in the Caribbean Sea, which is part of the Meso American and Caribbean Sea Regional Hydrographic Commission, in order to show its application and advantages. For this region it is proposed to generate a regular grid scheme with cell sizes and compilation scales (1:K) for Overview, General and Coastal navigational purposes of 24° × 24°, 6° × 6° and 1.5° × 1.5° and 1:3,000, 1:700 and 1:180 respectively, which allows to eliminate irregularities between the ENCs. This proposal contributes to the safety of maritime activities, seeking to enhance the quality of the ENC products in this region and increase trust to Electronic Chart Display and Information System users through enhanced visualization and seamless transition between ENCs of the same compilation scale. Additional serves as input for the modernization of the ENC schemes in the different Regional Hydrographic Commissions of the International Hydrographic Organization.

Pelaksanaan Pembelajaran Tatap Muka Terbatas Perspektif Sosiologi Pendidikan

Implementation of Limited Face-to-Face Learning (PTMT) refers to policies in several countries, including Indonesia, as a response to the COVID-19 pandemic. In the midst of a pandemic, education that was previously carried out entirely face-to-face in schools has been changed to distance learning or daring to reduce the spread of the virus. The purpose of this study is to describe face-to-face learning at Muhammadiyah 2 North Sangatta Elementary School and how teachers and principals implement limited face-to-face learning. This study uses a descriptive approach with qualitative research methods with the aim of observing more closely at natural objects. The descriptive method is a method that focuses on a group of people, conditions, an object and an event in the present. The results of the research are that the Muhammadiyah 2 Elementary School implements limited face-to-face learning carried out systematically, where the school principal and the Elementary School have implemented limited face-to-face learning, where the teaching and learning process in class, protective material is condensed and only with a chart scheme or by way of mapping so that it is easy absorbed by students, then for direct supervision of school principals and from primary and secondary education under the Muhammadiyah Islamic organization, and then structured and measurable evaluation.

Two robust multivariate exponentially weighted moving average charts to facilitate distinctive product quality features assessment

This paper offers new multivariate statistical process monitoring schemes to study the process shift supported by a distinctive product quality features assessment. The proposed procedures help determine whether one or more features have undergone a shift or whether the shift is in the dependence structure but not in the study variables. We use the marginal distributions and pseudo copula observations to effectively apply feature-wise rank-based Lepage and Cucconi tests. The rank-based statistics induce nonparametric properties to the proposed chart. Therefore, its in-control performance is highly robust and nearly distribution-free. It is shown that the new chart gives better results for detecting scale shifts in one or more quality variables than some representative existing nonparametric charts. Some Monte-Carlo simulation studies have been performed to establish the effectiveness of the new charting scheme. A real application involving the production quality of cork stoppers is considered to illustrate the use of the proposed schemes in manufacturing and production. Some encouraging product feature assessment properties are observed.

An optimally designed distribution‐free CUSUM procedure for tri‐aspect surveillance of continuous processes

AbstractIn the past few decades, research on nonparametric process monitoring schemes mainly dealt with the uni‐aspect or bi‐aspect schemes, focusing on monitoring process location or scale separately or jointly. Another critical process characteristic, namely, the process shape, is not explicitly dealt with in‐depth in most existing charting schemes. In classical hypothesis testing, some recent literature clearly showed that the multi‐aspect test statistics, although designed for very restrictive alternatives, often perform as well or better than many statistics for arbitrary distributional shifts. They are often better than Kolmogorov‐Smirnov or Cramér‐von Mises statistics and some empirical likelihood‐based test statistics. The current paper aims to use a tri‐aspect statistic to design a distribution‐free Phase‐II Cumulative Sum (CUSUM) charting scheme for monitoring any arbitrary process changes in the process. The proposal is nonparametric and is equivalent to an unknown standard case. It reflects, in addition, which parameters, among location, scale, or shape, are more responsible for a signal. The construction of the CUSUM scheme from an existing tri‐aspect Shewhart‐type chart is simple, so significant attention is devoted to determining a near‐optimal reference parameter of the chart in keeping an unknown shift type in mind. Comparisons of the optimal performance of various competitors are considered in terms of the median run length (MRL) metric. The proposed charting scheme designed with the tri‐aspect statistic compares highly favorably with many existing SPM schemes. The same is evident from our findings based on the Monte‐Carlo simulation. Finally, the proposed schemes are illustrated with a flow‐width measurement monitoring example.

Phase I analysis of high-dimensional processes in the presence of outliers

One of the significant challenges in monitoring the quality of products today is the high dimensionality of quality characteristics. In this paper, we address Phase I analysis of high-dimensional processes with individual observations when the available number of samples collected over time is limited. Using a new charting statistic, we propose a robust procedure for parameter estimation in Phase I. This robust procedure is efficient in parameter estimation in the presence of outliers or contamination in the data. A consistent estimator is proposed for parameter estimation and a finite sample correction coefficient is derived and evaluated through simulation. We assess the statistical performance of the proposed method in Phase I. This assessment is carried out in the absence and presence of outliers. We show that, in both cases, the proposed control chart scheme effectively detects various kinds of shifts in the process mean. Besides, we present two real-world examples to illustrate the applicability of our proposed method.

Multivariate semiparametric control charts for mixed-type data.

A useful tool that has gained popularity in the Quality Control area is the control chart which monitors a process over time, identifies potential changes, understands variations, and eventually improves the quality and performance of the process. This article introduces a new class of multivariate semiparametric control charts for monitoring multivariate mixed-type data, which comprise both continuous and discrete random variables (rvs). Our methodology leverages ideas from clustering and Statistical Process Control to develop control charts for MIxed-type data. We propose four control chart schemes based on modified versions of the KAy-means for MIxed LArge KAMILA data clustering algorithm, where we assume that the two existing clusters represent the reference and the test sample. The charts are semiparametric, the continuous rvs follow a distribution that belongs in the class of elliptical distributions. Categorical scale rvs follow a multinomial distribution. We present the algorithmic procedures and study the characteristics of the new control charts. The performance of the proposed schemes is evaluated on the basis of the False Alarm Rate and in-control Average Run Length. Finally, we demonstrate the effectiveness and applicability of our proposed methods utilizing real-world data.

CUSUMIN: A cumulative sum interval design for cancer phase I dose finding studies.

Recently, model-assisted designs, including the Bayesian optimal interval (BOIN) design with optimal thresholds to determine the dose for the next cohort, have been proposed for cancer phase I studies. Model-assisted designs are useful because of their good performance as model-based designs in addition to their algorithm-based simplicity. In BOIN, escalation and de-escalation based on boundaries can be understood as a type of change point detection based on a sequential test procedure. Notably, the sequential test procedure is used in a wide range of fields and is known for its application to control charts, statistical monitoring methods used for detecting abnormalities in manufacturing processes. In control charts, abnormalities are detected if the control chart statistics are observed to be outside of the optimal boundaries. The cumulative sum (CUSUM) statistic, which is developed for control chart applications, derives higher power under the same erroneous judgment rate. Hence, it is expected that a more efficient model-assisted design can be achieved by the application of CUSUM statistics. In this study, a model-assisted design based on the CUSUM statistic is proposed. In the proposed design, the dose for the next cohort is decided by CUSUM statistics calculated from the counts of the dose-limiting toxicity and pre-defined boundaries, based on the CUSUM control chart scheme. Intensive simulation shows that our proposed method performs better than BOIN, and other representative model-assisted designs, including modified toxicity probability interval (mTPI) and Keyboard, in terms of controlling over-dosing rates while maintaining similar performance in the determination of maximum tolerated dose.

Performance of the Shiryaev‐Roberts‐type scheme in comparison to the CUSUM and EWMA schemes in monitoring weibull scale parameter based on Type I censored data

AbstractThis paper introduces a process monitoring scheme based on the Shiryaev‐Roberts (SR) procedure for Type I right‐censored Weibull lifetime data for detecting shifts in the scale parameter for a fixed value of the shape parameter. The performance of the new charting scheme is evaluated under different censoring rates and sample sizes via Monte Carlo simulation. Comparison with two existing schemes, namely, the exponentially weighted moving average (EWMA) and cumulative sum (CUSUM) schemes, is performed in terms of average run length (ARL). Both zero‐state and steady‐state shifts are considered in this study for impartial comparisons. The numerical results based on simulation are highly encouraging in favour of the SR procedure. Our findings show that the new scheme outperforms the other two schemes in some cases. An illustrative example is given to illustrate the application of the proposed scheme.

Multivariate cumulative sum control chart for compositional data with known and estimated process parameters

AbstractThis article uses the classic multivariate cumulative sum () chart scheme proposed by Crossier (1988) to present a new modified chart for compositional data (). For this purpose, the data are first transformed using isometric log‐ratio () coordinates representation to eliminate the constant sum constraint of . The ‐ control chart has been defined along with the performance measures of the proposed chart using the average run length (). Besides, the Markov chain method has been used to study the performance of the proposed chart. Assuming that the transformed data are normally distributed, the proposed ‐ charts have been compared with existing competitors such as ‐ and ‐ charts. The comparison shows that the proposed chart has better performance than the ‐ control charts, while the performance of the proposed chart is comparable with the ‐ chart. The effect of the estimated mean vector and variance‐co‐variance matrix on run‐length characteristics of the proposed ‐ control chart has also been studied in this paper. For the performance of ‐ with estimated parameters Monte Carlo simulation has been adopted. The effect of the number of variables , sample size , and subgroup size has also been studied on the data's upper control limit () and . In the end, two illustrative examples of the particle size distribution of plants and production of muesli are provided to represent the practical implementation of the ‐ chart.

Effect of Parameter Estimation on the Performance of Shewhart <img src=image/13425522_01.gif>-joint Chart Looked at in Terms of the Run Length Distribution

Using spread-charts to monitor process variation and thereafter using the <img src=image/13425522_02.gif>-chart to monitor the process mean after is a common practice. To apply these charts independently using estimated 3-sigma limits is common. Recently, some authors considered the application of <img src=image/13425522_02.gif> and R-charts together as a charting scheme, <img src=image/13425522_01.gif>-chart when the standards are known, Case KK, only the mean standard is known, Case KU and both standards unknown, Case UU. The average run length (ARL) performance criterion was used. However, because of the skewed nature of the run length (RL) distribution, many authors have frowned at the use of ARL as a sole performance measure and encouraged the percentiles of the RL distribution instead. Therefore, the cdfs of the RLs of the chart under the cases mentioned will be derived in this work, and the percentiles are used to look at the chart for Case KU and the yet to be considered case of the chart, Case UK where only the process variance is known is included for comparison. These are the contribution to the existing literature. <img src=image/13425522_01.gif>-chart performed better in Case KU than in Case UK and the unconditional in-control median run length described the behavior of the chart better than the in-control ARL.

A comprehensive distribution‐free scheme for tri‐aspect surveillance of complex processes

AbstractDistribution‐free charting schemes for process monitoring are more robust and reliable than their parametric counterparts when the process distributions are unknown and complicated. Most of the existing control charts are uni‐aspect schemes because they can detect a shift in only one aspect of the process distribution, like location or scale. Research on schemes for simultaneous surveillance of location and scale parameters has been very active in recent years, leading to many bi‐aspect schemes. These schemes do not explicitly deal with the shape of the process distribution. However, a shift in the shape parameter with or without a location or scale shift can occur in practice, especially in production or time‐to‐event processes. Note that there are nonparametric schemes for detecting general shifts based on goodness‐of‐fit test statistics or empirical likelihood ratio statistics, which cannot isolate if there is a shift in location, scale, shape, or combination of these parameters. This article introduces a new distribution‐free process monitoring scheme that can detect a shift in the location, scale or shape parameters, or any combinations of them. The new scheme is based on a combined statistic designed via Euclidean distance of the standardized Wilcoxon statistic for location, the standardized Ansari–Bradley statistic for scale, and the standardized Savage‐type statistic for shape. We discuss the implementation design using the average run‐length as the performance metric and investigate the proposed scheme's in‐control performance. It is shown that the new charting scheme is in‐control robust irrespective of the underlying process distribution and therefore applicable to monitor any univariate continuous processes. An out‐of‐control performance comparison study of the new scheme with many existing schemes shows that the new scheme is preferable to the existing schemes. We illustrate the proposed chart with real data to monitor a passenger train's arrival delays in Italy's regional route. The proposed scheme is comprehensive because it integrates the follow‐up procedure via integrated sub‐charts for classifying the signaling component.

An assessment of the effect of using different mappings and Minkowski distances in joint monitoring of the time-between-event processes

Monitoring multiple parameters of a process using a single integrated charting scheme is an attractive research area in statistical process monitoring and control. The Max-type combining function based on Chebyshev’s distance and the Distance-type combining function, essentially based on Euclidean distance, are widely used to construct various schemes for simultaneously monitoring the location and scale parameters. In most of these schemes, normalising the suitable function of maximum likelihood estimators (MLE) of individual parameters is commonly used. While monitoring two-parameter exponential distributions, we show that mapping the pivots based on the maximum likelihood estimators to standard normal variables is not optimal. This paper investigates four different mappings to analyse the transformation effect on the joint monitoring schemes for a two-parameter exponentially distributed process. The Chebyshev’s and Euclidean distances are particular cases of Minkowski distance. We additionally investigate the effect of Manhattan and minimum-type Minkowski distances via Monte Carlo in terms of the run-length properties. The overall chart performance is assessed using the expected weighted run length EWRL. It is observed that the use of Manhattan distance and mapping into the standard Laplace model is more appropriate. A real example of monitoring a high-voltage current in a P-type high-voltage metal oxide semiconductor transistor (HPM) data is given to show the excellent performance of the suggested control chart.

An Effectiveness Study of SPC Control Charts on the Quality Control for Construction in Asphalt Pavement

Aiming at the problems of insufficient dynamic control and data mining of Expressway pavement construction quality in Yunnan, this paper analyzes the application of Exponentially Weighted Moving Average (EWMA) control chart in dynamic control of Expressway asphalt pavement construction quality. Firstly, the quality characteristic value of pavement construction is defined as the data type of multiple variation sources, and the mathematical model of multiple variation sources is studied to solve the problem of excessive false alarm. Then, on the theoretical basis of statistical process control (SPC), the paper compares and analyzes the three control chart schemes of traditional Hugh Hart control chart, Tabular Cumulative Sum (CUSUM) control chart and EWMA control chart, and concludes that the application effect of EWMA control chart is obviously better than the other methods in the aspects of applicability and inspection output. Finally, combining with the measured data of asphalt mixture temperature and asphalt weight in pavement construction, EWMA control diagram is used to carry out practical research, and the results are better.

A CUSUM-Based Approach for Condition Monitoring and Fault Diagnosis of Wind Turbines

This paper presents a cumulative sum (CUSUM)-based approach for condition monitoring and fault diagnosis of wind turbines (WTs) using SCADA data. The main ideas are to first form a multiple linear regression model using data collected in normal operation state, then monitor the stability of regression coefficients of the model on new observations, and detect a structural change in the form of coefficient instability using CUSUM tests. The method is applied for on-line condition monitoring of a WT using temperature-related SCADA data. A sequence of CUSUM test statistics is used as a damage-sensitive feature in a control chart scheme. If the sequence crosses either upper or lower critical line after some recursive regression iterations, then it indicates the occurrence of a fault in the WT. The method is validated using two case studies with known faults. The results show that the method can effectively monitor the WT and reliably detect abnormal problems.

Deep representation learning for process variation management in laser powder bed fusion

Laser Powder Bed Fusion (LPBF) is an additive manufacturing process where laser power is applied to fuse the spread powder and fabricate industrial parts in a layer by layer fashion. Despite its great promise in fabrication flexibility, print quality has long been a major barrier for its widespread implementation. Traditional offline post-manufacturing inspections to detect the defects in finished products are expensive and time-consuming and thus cannot be applied in real-time monitoring and control. In-situ monitoring methods by relying on the in-process sensor data, on the other hand, can provide viable alternatives to aid with the online detection of anomalies during the process. Given the crucial importance of melt pool characteristics to the quality of final products, this paper provides a framework to process the melt pool images by a configuration of Convolutional Auto-Encoder (CAE) neural networks. The network’s corresponding bottleneck layer learns a deep yet low-dimensional representation from melt pools while preserving the spatial correlation and complex features intrinsic in the images. As opposed to the manual annotation of data by X-ray imaging or destructive tests, an agglomerative clustering algorithm is applied to these representations to automatically extract the anomalies and annotate the data accordingly. A control charting scheme based on Hotelling’s T 2 and S 2 statistics is then developed to monitor the process’s stability by keeping track of the learned representations and residuals obtained from the reconstruction of original images. Testing the proposed methodology on the collected data from an experimental build demonstrates that the method can extract a set of complex features that are inextricable otherwise by using hand-crafted feature engineering methods. Moreover, through extensive numerical studies, it is shown that the proposed feature extraction and statistical process monitoring scheme is capable of detecting the anomalies in real-time with accuracy and F 1 score of about 95% and 82%, respectively.

Performance characterization of X group control charts

Group control charts are used for the statistical control of multiple stream processes. Recently, a modified control chart based on the residuals from each stream has been proposed as an alternative charting scheme. This research examines the performance of these charts under varying combinations of shift magnitudes and number of streams affected. Several simulated scenarios were generated and used to evaluate the long-term performance of the two schemes in terms of the average run length. Statistical analysis of the results indicated that the modified chart has an advantage over the traditional group chart in terms of the rate of false alarms. However, both charting schemes appear to have similar shift detection capabilities.

Management of COVID-19 in Children and Adolescents -A Practical Up-to Date Guideline

Sufficient high-quality data are unavailable to describe the management approach and guideline of COVID-19 disease in pediatric and adolescent population which may be due to mild presentation in most of cases and less severe complications than older ages.&#x0D; World Health Organization was concerned with the establishment of an approved guideline to manage the increasing number of COVID-19 patients worldwide aiming to prevent or lessen COVID-19 global burden.&#x0D; The clinical features have a wide spectrum starting from uncomplicated mild illness, mild-moderate pneumonia, severe pneumonia, acute respiratory distress syndrome, sepsis, septic shock, and multisystem inflammatory syndrome in children.&#x0D; Many important definitions were developed to identify the COVID-19 case status including confirmed, suspected, and probable case.&#x0D; Many laboratory tests may be beneficially done but reverse transcriptase – polymerase chain reaction (RT-PCR) for SARS-CoV-2 RNA is diagnostic.&#x0D; Patient isolation and adequate intake of fluid and calories, antipyretics (preferably paracetamol), antibiotics (in secondary bacterial infections), and bed rest are the mainstay of approved supportive treatment. While oxygen supplementation may be added in moderate to severe cases; anticoagulation prophylaxis (enoxaparin) is strongly advised in children with certain situations where hypercoagulability state is identified, and therapy should be evaluated based on risk factors.&#x0D; New approaches, drugs, and therapies are currently under research to manage COVID-19 pediatric and adolescent patients including antivirals (lopinavir/ritonavir, and favipiravir), remdesivir, tocilizumab, dexamethasone, convalescent plasma, and specific treatment of multisystem inflammatory syndrome in children (immunoglobulin, steroids, tocilizumab, anakinra, and aspirin).&#x0D; The practical approach was summarized in a flow chart scheme to assist health care professionals to manage COVID-19 in children and adolescents within a rapid look though details are given in the text.