Monitoring Charts Research Articles

A Control Chart for the Joint Monitoring of Mean and Variance With Klein's Supplementary Rule

ABSTRACTSupplementary run rules have been employed since the 1950s to enhance the performance of control charts, such as the control chart, which aims to control the process mean, or control chart, which aims to control the process variance. One simple, effective, and popular suggestion is Klein's 2‐of‐2, which signals an out‐of‐control process only when two sequential sample averages are observed above the upper control limit or below the lower control limit. We propose to implement Klein's 2‐of‐2 for a more complex control chart, the joint control chart, which aims to simultaneously monitor the mean and the variance of a process. Compared with the traditional control chart, using this rule for the control chart reduces the average run length (ARL) and standard deviation of the run length (SDRL) for detecting small process shifts while maintaining high applicability owing to the ease of use in employing the 2‐of‐2 rule in practical environments. Klein's run rule was implemented in the control chart through a 15‐state Markov chain, and its performance was compared with the basic control chart as well as the exponentially weighted moving average (EWMA) chart, known to have excellent capabilities in detecting small process average shifts at the cost of a more complex control scheme. The newly proposed Klein control chart demonstrated superior performance compared to the traditional control chart and competitive performance even against the more complex joint EWMA control chart.

A comparative study on sequential detection of random mean change in multivariate normal data stream

For the detection of random mean changes in a multivariate normal data stream, we explore and compare various direction-invariant monitoring charts. These include the (adaptive) largest eigenvalue charts employing exponential weighted moving variance (EWMV), the moving largest eigenvalue (MLE) based on moving sample variance, and both adaptive and windowed cumulative sum (CUSUM) charts with a reference value for the largest eigenvalue. The comparison involves assessing the power of detection (POD) given false detection probability (FDP) at a signal length and the average delay detection time (ADDT) given a false alarm rate (FAR) at a change time. Our primary contribution involves introducing the moving generalized likelihood ratio (MGLR) chart, which relies solely on the chi-square property of the adjusted generalized likelihood ratio. We provide simple approximations for FDP and FAR for design purposes. Our results indicate that the EWMV chart is recommended, and the MGLR chart performs effectively. Detection of volatility change in 30 Dow Jones Industrial stock prices is used for illustration.

Evaluation of statistical process control charts for infant mortality monitoring in Brazilian cities with different population sizes.

The control chart is a classic statistical technique in epidemiology for identifying trends, patterns, or alerts. One meaningful use is monitoring and tracking Infant Mortality Rates, which is a priority both domestically and for the World Health Organization, as it reflects the effectiveness of public policies and the progress of nations. This study aims to evaluate the applicability and performance of this technique in Brazilian cities with different population sizes using infant mortality data. In this article, we evaluate the effectiveness of the statistical process control chart in the context of Brazilian cities. We present three categories of city groups, divided based on population size and classified according to the quality of the analyses when subjected to the control method: consistent, interpretable, and inconsistent. In cities with a large population, the data in these contexts show a lower noise level and reliable results. However, in intermediate and small-sized cities, the technique becomes limited in detecting deviations from expected behaviors, resulting in reduced reliability of the generated patterns and alerts.

An image-based nonparametric multivariate EWMA control chart for metal additive manufacturing process monitoring

Additive manufacturing offers considerable promise in terms of manufacturing flexibility, but print quality remains a major obstacle to its widespread adoption. Consequently, timely and accurate detection of process variations or anomalies is critical for implementing corrective actions. Melt pool characteristics are crucial to final product quality, and current state-of-the-art studies on melt pool image flow have primarily focused on in-situ sensing, feature extraction, and their relationship with process settings and material properties. This study aims to develop a statistical process control approach to detect process variations immediately using a predefined distribution of monitoring statistics. Two main challenges are addressed: 1) the unknown distribution of melt pool image features, rendering traditional parametric control charts unreliable and 2) the autocorrelation of melt pool image flow features, violating the traditional control chart assumption of independent and identically distributed monitoring data. To overcome these challenges, we propose a multivariate nonparametric Exponential Weighted Moving Average (EWMA) control chart that can appropriately accommodate stationary serial data correlation to monitor process stability by tracking physical features extracted from the original images. Key features of the proposed control chart include its ability to eliminate serial correlation effects and its distribution-free nature, requiring no prior information about the data distribution. Comparative analysis of simulated and real data demonstrates that the proposed approach outperforms baseline methods in anomaly detection accuracy. An actual case study in laser metal deposition (LMD) further validates the effectiveness of the proposed method.

Sum of defects (SOD) chart for joint monitoring of the two parameters of a zero-inflated Poisson process

Rapid technological advancements and automation in today's world have led to many high quality manufacturing processes, where most of the products are free from defects. The defects data from high quality processes are commonly modeled by twoparameter zero- inflated Poisson (ZIP) distribution. Most of the existing control charting procedures for ZIP processes are developed assuming that all the manufactured units are inspected one by one, and most often separate control charts are proposed for monitoring the two parameters of a ZIP process. However, joint monitoring of the two parameters in a single chart can offer significant operational advantages because one needs to focus on a sole chart and a single charting statistic. Again, in many manufacturing set ups 100% inspection may not always be feasible. Recently, a few one-chart schemes with group inspection of small samples are reported in literature, and all these schemes require estimation of the two parameters based on accumulated samples till the current sampling stage for computation of the monitoring statistics. This accumulation of samples introduces several limitations in these schemes. In this paper, probability mass function and the cumulative distribution function (cdf) of the sum of defects (SOD) in a sample of size are defined, and a control chart for SOD is proposed. Both the parameters of a ZIP process can be jointly monitored using the SOD chart. Again, since no accumulation of the samples is required for application of the SOD chart, it is free from all limitations of the existing one-chart schemes with group inspection. Two case studies based on past data are presented. The performance of the SOD chart is found to be very encouraging.

New Lepage-type control charts for joint monitoring of location and scale

This paper introduces two new Shewhart Lepage-type control charts for jointly monitoring the location and scale parameters of continuous processes. The first is based on the van der Waerden (VW) and Ansari-Bradley (AB) tests while the other is on the VW and Mood tests. Statistical performances, in terms of average run length (ARL), of these charts are evaluated numerically and compared with that of their two competitor charts in the existing literature under four distributional models for the process output, namely, the normal, lognormal, Laplace, and logistic. The numerical results show that irrespective of the process distribution, one of the proposed charts (based on VW and Mood tests) has the best performance among all the four charts in the detection of simultaneous shifts in both the parameters of all sizes as well as in the detection of shifts in the scale that have not been accompanied by shifts in the location (only-scale shifts) of all sizes. Moreover, the other proposed chart (based on VW and AB tests) performs better than one of the competitor charts in the existing literature for detecting simultaneous shifts as well as only-scale shifts of all sizes. In addition, it performs better than the other competitor chart for detecting the simultaneous shifts consisting of a large location shift and a small scale shift, however, performs worse than that for detecting the simultaneous shifts consisting of a small location shift and a scale shift of any size, as well as for detecting the only-scale shifts of all sizes. All the four charts perform almost similarly in the detection of only-location shifts of all sizes. Regression models are provided for the proposed charts, which facilitate their designs in practice. Finally, the practical implementation of the proposed charts is illustrated through a real-data example.

An efficient multivariate depth-based EWMA control chart for monitoring location

In multivariate statistical process control (MSPC), when there is a limited knowledge or there is no knowledge about the underlying process distribution, nonparametric control charts are useful. This paper introduces a new powerful multivariate depth-based control chart for online monitoring of location parameters in MSPC. Actually, by incorporating the exponentially weighted moving average control scheme, the weighted version of a well-known multivariate depth-based test statistic is used to formulate the new charting statistic. The methodology is based on adapting the intended charting statistic to the online sequential monitoring of the location parameters. The proposed control chart is affine-invariant and has a conditional distribution-free property over the class of elliptically symmetric distributions. This control chart is also very efficient in detecting small or moderate shifts in the process location parameter, especially for large dimensions.

A resilient S2 monitoring chart with novel outlier detectors

AbstractWhile researchers and practitioners are seamlessly trying to develop methods for minimizing the effect of outliers in control charts, detecting and screening these outliers continue to pose serious challenges. Keeping in view, the researchers rely on robust estimators to modify the detection limits structure so that the chart can be more sensitive against outliers. In this study, we propose a robust control chart based on , , , , and estimators, whilst the process parameter is estimated from Phase‐I. Through intensive Monte‐Carlo simulations, the study presents how the estimation of parameter(s) and presence of outliers affect the efficacy of the chart, and then how the proposed outlier detectors bring the chart back to normalcy by restoring its efficacy and sensitivity. Average properties are used as the performance measures. The properties establish the superiority of the proposed scheme over and Tukey's outlier detectors. The applicability of the study includes the effectiveness of the proposed detectors in industrial data set but is not limited to manufacturing industries.

Midwives’ Knowledge, Perceptions and Practices on Utilization of the Partograph at Mbuya Nehanda Maternity Hospital

Globally, maternal death has been an issue of concern, while obstructed and prolonged labour are among the top five causes of these deaths in Africa and Zimbabwe particularly. Proper and effective utilization of the Partograph significantly helps in preventing and reducing the high numbers of birthing complications of such a nature. A Partograph is a labour monitoring chart that provides a pictorial overview of maternal and foetal well-being during the intrapartum period. The study aimed to assess the knowledge, perceptions, and practices on the utilization of the Partograph among midwives working at Mbuya Nehanda Maternity Hospital. A descriptive cross sectional design was used, and a semi-structured questionnaire was administered to 60 participants chosen using the purposive sampling technique. Data were collected and analysed using the Statistical Package for Social Sciences (SPSS version 22) and presented using charts, tables, and narration. The study identified that midwives were knowledgeable, although they had a negative attitude towards the Partograph attributed by a lack of motivation caused by the absence of support from mentors and supervisors, lack of workshops, and on-the-job training on the use of the graphical chart. Underutilization of the tool, which pointed to poor practice, was attributed to understaffing of midwives, high workload, and burnout syndrome among the labour ward midwives. These implications could lead to omissions, litigations, poor maternal service delivery, and lawsuits. Periodic workshops, symposiums, on-the-job training would go a long way in improving attitudes. The study concluded that the Partograph is a very important tool that should be effectively used in the labour ward so that complications are picked up early, thereby improving maternal outcomes. Support for the midwives, supervision, mentorship, non-monetary incentives, and increased staffing with an acceptable midwife-patient ratio of probably 1:2 would improve practice and yield a positive birthing outcome of a live and healthy mother and baby.

Coping mechanisms and strategies adopted to improve the quality and timeliness of immunization data among lower-level private-for-profit service providers in Kampala Capital City, Uganda.

Lower-level urban private-for-profit health service providers are actively engaged in the delivery of immunization services. However, not much is known about their everyday endeavours to improve data quality and ensure the submitted data meets the quality and timeliness requirements as per established guidelines. The objective of this paper was to examine the coping mechanisms and strategies adopted to improve the quality and timeliness of immunization data among lower-level private-for-profit service providers in Kampala Capital City, Uganda. A qualitative study design was adopted with in-depth interviews (n = 17) and key informant interviews (n = 8) completed among frontline health workers, district health managers and immunization implementing partners. Analysis followed a thematic approach with coding conducted using Atlas. ti, a qualitative data management software. Overall, coping mechanisms and strategies adopted to improve the data quality among lower-level urban private-for-profit immunization service providers included; Experiential attachment for practical skills acquisition in data management, data quality peer-to-peer learning among private-for-profit immunization service providers, registration using cohort system for easy tracking of records during subsequent visits, separation of visiting service user records from regular attendants, service delivery modifications such as reward for better performance, engagement of Village Health Teams (VHTs) in outreaches and data completion, and data quality checks through review of monitoring charts to identify gaps in data quality. Within the urban context, the delivery of immunization services by lower-level private-for-profit services faces data quality challenges some of which stem from the implementation context. Different coping strategies focusing on frontline health worker skills, enhanced experience sharing through peer-to-peer learning, modifications to registration and routine review of monitoring charts. However, these efforts were often faced with chronic barriers such as the high staff turnover calling for dedicated efforts to optimize the available implementation opportunities like guidelines mandating the public health facilities to supervise the lower-level private immunization service providers within their catchment areas to respond to the identified gaps.

Practical Improvement of Noncontact Production Monitoring of Doping in SiC Wafers with Extended Epilayer Defects

We discuss two defect related practical improvements in the corona noncontact CV metrology, (CnCV) for SiC. The improvements are introduced in response to requests from industrial tool users. The first improvement quantifies mapping of electrically active defects with the QUAD technique (Quality, Uniformity, and Defects). It provides the capability of user selectable die grids directly comparable with Near UV-PL and optical defect mapping. This shall enhance understanding of the device killer defects and help to correlate epilayer defects and device yield. The second improvement introduces auto-remeasurement of outliers appearing in doping measurements on defective sites. This procedure is analogous to that used in the Hg probe technique and it provides a means for correcting defect related distortions in SPC doping monitoring charts.

Spatio-temporal feature extraction network based multi-performance indicators synergetic monitoring method for complex industrial processes

In the context of smart manufacturing, modern industrial processes are becoming increasingly complex in terms of process flows, product varieties, and performance indicators (PIs). Performance-driven process monitoring attracts extensive attentions in recent years. However, most methods require spatio-temporal correspondence between process variables and PIs, and rarely consider the correlation among various PIs. In this paper, a spatio-temporal feature extraction network-based multi-performance indicators synergetic monitoring framework is presented. Firstly, considering the missing data in PI measurements, a weighted sum of tensor nuclear norm (WSTNN) based batch-process data completion approach is developed, which can adeptly handle local missing and incomplete data issues and establish the spatio-temporal correspondence for subsequent modeling. Secondly, for a specific PI, a new canonical variate analysis embedded spatio-temporal convolutional network (CVA-STCN) is designed to extract the PI-related features with spatio-temporal dependency. Thirdly, considering the dynamic interaction of multiple PIs, a third-order feature tensor is established to perform the future fusion, and the correlations among various PI-related features are explored via tensor decomposition. Finally, a hierarchical multi-performance indicators synergetic monitoring model is developed over several subspaces. The proposed method is verified on Tennessee Eastman process and an actual hot strip mill process. Overall, the monitoring performance of the proposed method outperforms the traditional ones, in terms of higher fault detection rates and lower false alarm rates. Moreover, the information provided by the multi-subspace synergetic monitoring charts can offer valuable guidance to field engineers.

Predictors of successful transition of adolescents and young adults living with HIV from pediatric to adult-oriented care in southern Ethiopia: a retrospective cohort study

BackgroundThe introduction of highly active antiretroviral therapy has significantly improved the life expectancies of children and adolescents living with HIV, leading to an increased number transitioning to adult care. However, there has been a lack of studies in Ethiopia focusing on factors influencing the success of this transition. Therefore, this study aimed to determine predictors of a successful transition from pediatric to adult HIV clinics among adolescents and young adults living with HIV in health facilities in southern Ethiopia.MethodsA retrospective cohort study included 337 adolescents and young adults who transitioned to adult-oriented HIV care. Successful transition was defined as having a viral load of less than 1000 copies/ml and maintaining care during the first year post-transition. Patients’ antiretroviral therapy (ART) cards and monitoring charts were reviewed. Secondary data analysis was conducted using a multivariable binary logistic regression model to identify predictors of a successful transition. Using the variance inflation factor, we checked for multi-collinearity between variables and assessed model fitness with the Hosmer and Lemeshow goodness-of-fit test. Adjusted Odds Ratio (AOR) with 95% confidence intervals (CI) and P-value ≤ 0.05 measured the strength of association and statistical significance.ResultsOf 337 participants, 230 (68.25%) successfully transitioned (95% CI = 63.25, 73.25). Transitioning at age 18 or older (AOR = 4.25; 95% CI = 2.29, 7.87), residing in an urban area (AOR = 1.78; 95% CI = 1.04, 3.02), and being on antiretroviral therapy for more than two years (AOR = 4.25; 95% CI = 1.17, 4.94; P < 0.017) were identified as positive predictors and opportunistic infection (AOR = 0.34; 95% CI = 0.15, 0.75; P < 0.008) was identified as a negative predictor for a successful transition from pediatric to adult ART clinic.ConclusionThis study sheds light on the challenges faced by HIV patients transitioning from pediatric to adult care, with less than 70% successfully navigating this critical phase. Factors such as age at transition, residence, duration of ART, and the presence of opportunistic infections were identified as key predictors of successful transition. The findings underscore the urgent need for tailored interventions, including standardized transition plans that address age and urban/rural disparities, to enhance transition outcomes for adolescents and young adults living with HIV in the region.

Comfort assessment of wind induced vibrations for slender structures by field monitoring and numerical analysis

Slender structures often lead to vibration discomfort for occupants when exposed to wind forces. This study proposes an innovative method for assessing comfort against wind-induced vibrations for slender structures that combines field monitoring, numerical simulations, codal provisions, and Chang's comfort chart. The method utilizes ambient vibration tests (AVT) and operational modal analysis (OMA) to create a reliable finite element (FE) model for the structure. It involves analyzing the time history and calculating the peak acceleration values at various points within the structure using synthetic ambient wind forces derived from superposing waves. The comfort assessment compares peak acceleration values estimated from time history analysis against those provided in Chang's chart for different comfort levels. The effectiveness of the proposed method is demonstrated through a case study on a tall, slender reinforced concrete (RC) staircase structure, confirming its suitability for practical applications.

Robust statistical industrial fault monitoring: A machine learning-based distributed CCA and low frequency control charts

Over the past two decades, there has been a notable increase in the complexity and dynamism of industrial and manufacturing systems. Traditional fault detection strategies exhibit limited robustness in terms of detection accuracy, inefficiency, and poor detection outcomes. This paper presents an innovative, robust machine learning (ML) based distributed canonical correlation analysis (DCCA) and low-frequency generated univariate control charts CUSUM and EWMA using wavelet transforms (WT) to monitor industrial and manufacturing systems, specifically focusing on abnormalities and fault detection. The framework’s performance is tested using previous algorithms such as multiscale PCA and PLS, and the (CSTR) is utilized as the benchmark case application. The results illustrate that the ML-based multiscale low-frequency (DCCA) monitoring charts for fault cases exhibited an (MDR) of 0 %, (FAR) of 0.2 %, (FDR) of 100 %, precision of 99.8 %, and an F1-score of 99.9 %. This method enables robust detection and ensures complete monitoring of aberrant circumstances.

Multiple exponentially weighted moving average control schemes for monitoring and diagnostics of correlated quality variables of different types with individual observations

In the era of big data analytics, more data, in terms of quantity and variety, are being collected for quality control purposes, thus leading to new challenges, especially for multivariate statistical process control (MSPC). One such challenge, which has received very little attention, is multivariate control chart (MCC) for monitoring correlated quality variables of different types. A recent study by Huang et al. (2023), which develops a Shewhart type MCC, is the first attempt to tackle such a challenge. The proposed chart of Huang et al. (2023), which utilizes the step-down multiple testing procedure of Holm (1979), not only can monitor correlated variables of different types, but also can provide instantaneous diagnostics of which parameters are out of control when the chart signals. In this study, we adapt and extend the methodology of Huang et al. (2023) to develop an exponentially weighted moving average (EWMA) chart specifically for the case when the sample size is one. The proposed chart is shown to be effective in detecting parameter changes as well as diagnosing which parameters are out of control when the chart signals.

Joint monitoring of mean and variance using Max-EWMA control chart under lognormal process with application to engine oil data

The control charts are frequently employed in process monitoring to assess the average and variability of a process, assuming a normal distribution. However, it is worth noting that some process distributions tend to exhibit a positively skewed distribution, such as the lognormal distribution. This article proposed a maximum exponential weighted moving average control chart for joint monitoring of mean and variance under a lognormal process. The proposed control chart is evaluated by using the run length profile such as ARL and SDRL. The Monte Carlo simulation is conducted by using the R language to find the run length profile. An application is presented to demonstrate the design of the proposed control chart.

IoT-Based Framework for Implementation of Leaf Color Chart for Nitrogen Monitoring in Crop

Effective nitrogen management is essential in precision agriculture to optimize crop yields and promote environmental sustainability. Conventional techniques for evaluating nitrogen levels, although commonly used, suffer from a lack of accuracy and scalability. This research presents a novel design framework that combines the Leaf Colour Chart (LCC), a well-established agricultural tool for nitrogen evaluation, with modern Internet of Things (IoT) technologies. The proposed system utilizes specialized sensors to gather precise leaf color data, which is subsequently analyzed using cloud-based computer vision techniques to correctly and instantaneously predict nitrogen levels. The framework streamlines data gathering and processing, allowing for accurate nitrogen management. This enables the application of fertilizer to specific areas and minimizes wastage. This article provides an overview of the system architecture, examines the difficulties and resolutions in integrating Internet of Things (IoT) technology in agriculture, and showcases case studies that demonstrate the effectiveness of the framework in practical environments. The incorporation of IoT technology with the Leaf Colour Chart signifies a notable progression in agricultural technology, offering the potential to enhance crop management techniques and support sustainable agriculture endeavors.

New adaptive EWMA CV control chart with application to the sintering process

This research presents a new adaptive exponentially weighted moving average control chart, known as the coefficient of variation (CV) EWMA statistic to study the relative process variability. The production process CV monitoring is a long-term process observation with an unstable mean. Therefore, a new modified adaptive exponentially weighted moving average (AAEWMA) CV monitoring chart using a novel function hereafter referred to as the "AAEWMA CV" monitoring control chart. the novelty of the suggested AAEWMA CV chart statistic is to identify the infrequent process CV changes. A continuous function is suggested to be used to adapt the plotting statistic smoothing constant value as per the process estimated shift size that arises in the CV parametric values. The Monte Carlo simulation method is used to compute the run-length values, which are used to analyze efficiency. The existing AEWMA CV chart is less effective than the proposed AAEWMA CV chart. An industrial data example is used to examine the strength of the proposed AAEWMA CV chart and to clarify the implementation specifics which is provided in the example section. The results strongly recommend the implementation of the proposed AAEWMA CV control chart.

Max-mixed EWMA control chart for joint monitoring of mean and variance: an application to yogurt packing process

The Max-Mixed EWMA Exponentially Weighted Moving Average (MM EWMA) control chart is a statistical process control technique used for joint monitoring of the mean and variance of a process. This control chart is designed to detect small and moderate shifts in the mean and variance of a process by comparing the maximum of two statistics, one based on the mean and the other on the variance. In this paper, we propose a new MM EWMA control chart. The proposed chart is compared with existing control charts using simulation studies, and the results show that the chart performs better in detecting small and moderate shifts in both the mean and variance. The proposed chart can be helpful in quality control applications, where joint monitoring of mean and variance is necessary to ensure a product’s or process’s quality. The real-life application of the proposed control chart on yogurt packing in a cup data set shows the outperformance of the MM EWMA control chart. Both simulations & real-life application results demonstrate the better performance of the proposed chart in detecting smaller shifts during the production process.