Control Charts Research Articles

A hybrid approach based on critical chain for project buffer consumption prediction and monitoring

PurposeThe critical chain project buffer monitor process addresses uncertainty and variability in project duration. However, classical buffer monitor methods only consider buffer consumption, while the dynamic allocation of buffer zones and the buffer consumption trend of activities are ignored. This paper presents the innovative framework for dynamic monitoring of project buffer which covers the dynamic buffer allocation, predictive analytics of buffer utilization and a new monitoring technique based on control chart graph.Design/methodology/approachFirst, a dynamically buffer allocation model is framed, and buffer zones are given to the activities considering risks. Then, a predictive model amalgamating Bayesian Optimization, Convolutional Neural Networks, and Long Short-Term Memory networks (BO-CNN-LSTM) is framed. Finally, a new buffer monitor framework is constructed that takes into account historical information about buffer usage and utilizes two thresholds derived from control chart theory.FindingsThis approach is empirically tested on a representative agricultural website project in China. The results show that, first, the dynamic buffer allocation makes better use of the project buffer, reduces buffer waste and increases the possibility of timely completion of the project. Second, the BO-CNN-LSTM model predicts better than Long Short-Term Memory (LSTM) and Grey Neural Network Model (GNNM), providing project managers with new management insights and perspectives. Third, the novel monitoring procedure makes the leveraging of historical data possible in the control of the schedule deviations, allowing for more timely interventions in the course of the implementation of the project.Originality/valueA new project buffer monitoring method suitable for uncertain project environments is proposed.

An Improvement Project to Lower Pneumothorax Rates in Neonates Born at 36 Weeks' Gestational Age or Beyond.

Our institutional data revealed high pneumothorax rates in term neonates resuscitated in the delivery room (DR). Other studies have reported that high rates of continuous positive airway pressure (CPAP) in the DR are associated with increased pneumothorax rates. We sought to test the hypothesis that quality improvement efforts to reduce the use of CPAP in the DR would be associated with a reduced incidence of pneumothorax. We performed a series of interventions to make minor revisions to our DR respiratory care algorithm focusing on optimizing CPAP use by providing education to the DR team to the revisions. For neonates born at 36 weeks of gestation or beyond, we evaluated the use of CPAP in the DR and the number of births between pneumothorax events before and after the algorithm was implemented. We used statistical process control charts to assess improvement. CPAP utilization in the DR for infants 36 weeks or older decreased from 3.4% to 1.0%. Frequency of pneumothorax decreased, with births between pneumothorax events increasing from 293 to 530. We found no increase in the number of neonates requiring a higher level of care with respiratory distress. We found that a reduction in the use of CPAP in DR was associated with a decrease in the rate of pneumothorax without an increase in neonates requiring additional care with respiratory distress.

Enhanced RUL Prediction of Rolling Bearings Using Nonlinear Wiener Model with an Extended Incremental Kalman Filter

Abstract To predict the remaining useful life (RUL) of rolling bearings, a novel two-stage degradation model is constructed, taking into account the two-phase characteristics of bearing performance degradation, which includes stable (Stage 1) and degrading (Stage 2) phases. The model employs an autoregressive model and a nonlinear Wiener process to describe the performance degradation in each stage. Subsequently, a residual cumulative sum control chart (RCUSUM) is proposed to identify the first change -point from Stage 1 to Stage 2. In response to the limitations of existing extended Kalman filter (EKF) methods that overlook the dynamic characteristics of state increments for state updates, an adaptive extended increment Kalman filter (EIKF) is introduced to update the degradation state and achieve accurate RUL predictions of rolling bearings. Finally, the effectiveness and applicability of this method are validated using a self -constructed dataset from 16004 bearing test data and XJTU-SY bearing data. The results demonstrate that this approach can accurately identify first change -point and enhance the accuracy of RUL predictions.

Dynamic Process Dispersion Monitoring Through Bayesian‐Driven Smoothing Adjustments

ABSTRACTThis study introduces a Bayesian adaptive exponentially weighted moving average (AEWMA) control chart designed to monitor process dispersion in case of normal distribution by integrating various loss functions. It exhibits robust performance in identifying shifts in process dispersion across different scales. Evaluation involves Monte Carlo simulations to calculate run length characteristics and a comprehensive comparative analysis against existing charts. Our findings emphasize the increased sensitivity of the Bayesian AEWMA control chart to shifts of different magnitudes. Furthermore, an experiment was performed in the context of another field, specifically semiconductor manufacturing, to compare the performance of the proposed Bayesian control chart using different loss functions. It showed that the suggested chart was much better than the existing control charts in terms of observing out‐of‐control signals. In summary, this article develops an innovative approach with various loss function approaches and improves the accuracy and efficiency depending on the dispersion change in the Phase II process, thus it is a valuable contribution to the further development of the quality control and monitoring process.

Improving Process Control Through Decision Tree-Based Pattern Recognition

This paper explores the integration of decision tree classifiers in the assessment of machining process stability using control charts. The inherent variability in manufacturing processes requires a robust system for the early detection and correction of disturbances, which has traditionally relied on operators’ experience. Using decision trees, this study presents an automated approach to pattern recognition on control charts that outperforms the accuracy of human operators and neural networks. Experimental research conducted on two datasets from surface finishing processes demonstrates that decision trees can achieve perfect classification under optimal parameters. The results suggest that decision trees offer a transparent and effective tool for quality control, capable of reducing human error, improving decision making, and fostering greater confidence among company employees. These results open up new possibilities for the automation and continuous improvement of machining process control. The contribution of this research to Industry 4.0 is to enable the real-time, data-driven monitoring of machining process stability through decision tree-based pattern recognition, which improves predictive maintenance and quality control. It supports the transition to intelligent manufacturing, where process anomalies are detected and resolved dynamically, reducing downtime and increasing productivity.

Optimizing codebook training through control chart analysis

Optimizing codebook training through control chart analysis

OCRClassifier: integrating statistical control chart into machine learning framework for better detecting open chromatin regions

Open chromatin regions (OCRs) play a crucial role in transcriptional regulation and gene expression. In recent years, there has been a growing interest in using plasma cell-free DNA (cfDNA) sequencing data to detect OCRs. By analyzing the characteristics of cfDNA fragments and their sequencing coverage, researchers can differentiate OCRs from non-OCRs. However, the presence of noise and variability in cfDNA-seq data poses challenges for the training data used in the noise-tolerance learning-based OCR estimation approach, as it contains numerous noisy labels that may impact the accuracy of the results. For current methods of detecting OCRs, they rely on statistical features derived from typical open and closed chromatin regions to determine whether a region is OCR or non-OCR. However, there are some atypical regions that exhibit statistical features that fall between the two categories, making it difficult to classify them definitively as either open or closed chromatin regions (CCRs). These regions should be considered as partially open chromatin regions (pOCRs). In this paper, we present OCRClassifier, a novel framework that combines control charts and machine learning to address the impact of high-proportion noisy labels in the training set and classify the chromatin open states into three classes accurately. Our method comprises two control charts. We first design a robust Hotelling T2 control chart and create new run rules to accurately identify reliable OCRs and CCRs within the initial training set. Then, we exclusively utilize the pure training set consisting of OCRs and CCRs to create and train a sensitized T2 control chart. This sensitized T2 control chart is specifically designed to accurately differentiate between the three categories of chromatin states: open, partially open, and closed. Experimental results demonstrate that under this framework, the model exhibits not only excellent performance in terms of three-class classification, but also higher accuracy and sensitivity in binary classification compared to the state-of-the-art models currently available.

Economic Design of Quality control by Using Triangular Fuzzy Number with Alpha Cut

Manufacturers today prioritize increasing output quality while reducing per-unit costs to remain competitive. Statistical Process Control (SPC) is a useful tool for boosting quality and output. However, decision-makers in industries face difficulties in cost and quality control due to uncertainty in data. Fuzziness can be applied to uncertain data to manage the economic design of a control chart, allowing for better control over the control chart's economic design. This study seeks to enhance the economic design of X control charts by using fuzzy set theory, specifically utilizing triangular fuzzy numbers for cost parameters and the signed distance approach for defuzzification. The objective is to enhance the adaptability of these charts in unpredictable situations. The proposed model enables the incorporation of cost parameters inside a fuzzy framework, with the objective of minimizing the control chart while adhering to the permissible limit. The applicability and improved accuracy of the model in optimizing control chart parameters for a glass bottle production process are exemplified by an effective illustration. This study highlights the need of integrating fuzzy logic into SPC. It suggests a technique that improves the cost-effectiveness and operational effectiveness of control charts in situations when data is uncertain and imprecise. . KEYWORDS :Statistical quality control, Economic quality control, Triangular fuzzy number, Signed-distance, Alpha cut.

EWMA control chart framework for efficient Maxwell quality characteristic monitoring: An application to the aerospace industry

The Maxwell distribution is extensively employed in statistical modeling to analyze data, notably in the fields of chemistry, astrophysics, demography, actuarial sciences, economics, industry, and engineering. Recently, the application of the Maxwell data generating process (DGP) has focused extensively on the domain of statistical control charts. The V chart and VSQ chart are often used to identify unexpected changes in the distributional shift of the Maxwell process. The VSQ chart offers considerably better performance than the V chart for detecting moderate to large shifts in the scale parameter. However, the VSQ chart adopts the fundamental structure of the Shewhart monitoring scheme and is insensitive to small alterations in the target parameter. We propose a new control chart, namely, the Maxwell exponentially weighted moving average (MXEWMA) chart, for improved monitoring of quality attributes that are assumed to conform to Maxwell data generation. The factors used to design the parameters of the proposed chart are computed at different false alarm probabilities and across various sample sizes. The effectiveness of the suggested scheme is considered in terms of the different features of the run length (RL) distribution, including the average, median and standard deviation. A comparative study of the MXEWMA chart with the existing VSQ chart was performed across various sample sizes. The comparative analysis showed that the MXEWMA chart is an effective alternative and performs well in detecting reasonably small shifts in the parameter. Simulated data are employed to describe the computational procedure of the MXEWMA scheme. The simulation analysis demonstrated that the MXEWMA chart outperforms the existing method in identifying slight changes in the studied parameters. A real dataset is also considered to support the theoretical part of the work.

Statistical process control applied in the analysis of defects in asynchronous electrical machines

This paper refers to defects recorded for asynchronous electrical motors. The aim of this paper is to conclude about the quality of the production process of the asynchronous electrical machines using the statistical process control. The main objective of statistical process control is to find and conclude about the abnormalities in the production processes, using statistical tools of analysis and control, the so called quality control charts. For a certain measured quality characteristic, analyzing the corresponding control chart of that characteristic, one can conclude about the fact whether the respective process is getting out of the control or it is under control. These conclusions are made using the so called control limits, LCL and UCL (lower and upper control limit).

Sensitive and Accurate Determination of 32 PFAS in Human Serum Using Online SPE-UHPLC-HRMS

Per- and polyfluoroalkyl substances’ (PFAS) extreme persistence has been linked to many adverse effects on human health including increased risk of certain cancers. This study presents the development and validation of a new, highly sensitive method for the quantification of 32 PFAS in human serum using online solid-phase extraction (SPE) coupled with ultra-high performance liquid chromatography–high resolution mass spectrometry (UHPLC–HRMS). Legacy and emerging PFAS were targeted. Main steps of sample pretreatment include protein precipitation (PP), pellet rinsing, centrifugation, preconcentration through solvent evaporation, and online SPE using a weak anion-exchange polymeric sorbent. The PP and pellet-rinsing procedures were optimized through a comprehensive exploration of solvent combinations. Following this, a pretreatment that offers the best compromise for the targeted PFAS was identified using principal component analysis. The method demonstrated excellent linearity (R² = 0.977–0.997) with limits of quantification ranging from 8.9 to 27ng/L, 5 to 15 times lower than previous methods. Precision (intraday 2.6–14.0% and interday 1.3–11.0% relative standard deviation) and accuracy (recoveries 72.7–106%) were robust. The method was validated in accordance with ISO/IEC 17025 and successfully applied to five human serum samples, confirming its suitability for high-throughput profiling of PFAS in biomonitoring studies. This method is the first to use online SPE for the simultaneous determination of a broad range of PFAS, including ether congeners such as perfluoro(2-ethoxyethane) sulfonic acid and Nafion byproduct 2. Furthermore, control charts were employed to assess instrument performance during routine analysis and implement necessary actions.

On-line recognition of mixture control chart patterns using hybrid CNN and LSTM for auto-correlated processes

Statistical process control (SPC), designed to detect and identify process disturbances, is an effective quality control method for ensuring process stability. Owing to the growing automation of the industry, the manufacturing process can be autocorrelated. Engineer process control (EPC) is typically used to address autocorrelation. However, process disturbances can be offset by feedback compensation, making control chart patterns (CCPs) difficult to identify. Most studies on control chart pattern recognition (CCPR) techniques are based on a single abnormal control chart pattern (CCP). However, a mixture of CCPs can occur during real-world manufacturing processes. With the development of intelligent manufacturing systems, early detection of abnormal concurrent CCPs has become an important issue. In this study, a hybrid model combining a convolutional neural network (CNN) and long short-term memory (LSTM) in an online detection system was used to recognize the concurrent CCPR problem in SPC-EPC processes. The results showed that the average accuracy of the deep learning CNN-LSTM method was 99.83%, which was significantly better than that of the machine learning method. In addition, the running time of the CNN-LSTM model was shortened. In a comparison of online monitoring between the deep learning method and the machine learning method, the CNN-LSTM model for an online monitoring system identified abnormal concurrent patterns faster. Therefore, the proposed CNN-LSTM online monitoring scheme can be applied confidently and successfully to identify mixture CCPs in an SPC-EPC process.

The impact of the COVID-19 pandemic on hospital admissions in a psychiatric ward in a general hospital in Greece.

The negative consequences of the COVID-19 pandemic and the subsequent restrictive measures on the mental health and well-being of the population and psychiatric patients have been widely recognized. Patients' treatment attendance and engagement with mental health services had been negatively affected by the pandemic, whereas patients were less likely to receive timely outpatient care. The pandemic also impacted the use of inpatient services. The aim of the present study was to explore the variability of attendance and admissions to a general hospital psychiatric ward over a 12-month interval after the onset of the pandemic (March 2020), compared to the respective 12-month pre-pandemic interval. A retrospective, observational pre/post study was performed, involving a general hospital psychiatric ward in Corfu, Northwest Greece, which serves an insular catchment area of approximately 100,000 inhabitants. For data analysis, c- and u-charts of statistical process control charts were employed, using monthly data (March 2019 to February 2021). Overall, a significant decline in attendance rates was observed, mostly accounted for by a 26.5% reduction in voluntary attendance rates (1516 patients prior vs. 1114 patients after the onset of the pandemic). The involuntary commitment of patients did not differ between the two periods (106 prior vs. 100 after the onset of the pandemic). Admission rates did not change significantly between the two periods. Diagnoses that exhibited significant variance in examinations between the two study periods were mood disorders and personality disorders, whereas there was no significant variation in the number of admissions across different diagnoses. Length of hospital stay increased significantly by 13.2% over the first year of the pandemic, from 25.57 days (Md= 13, IQR= 22) during the pre-COVID-19 period to 28.95 days (Md= 22, IQR= 28) during the COVID-19 period. Patients with schizophrenia and related disorders (Mean= 34.25 days, SD= 43.19) and mood disorders (Mean= 26.26, SD= 33.48) had prolonged hospital stays compared to other diagnoses. These findings highlight significant shifts in psychiatric care delivery during the pandemic and underscore the need for targeted interventions to address the evolving demands on mental health services during public health crises.

CUSUM Control Chart for Symlets Wavelet to Monitor Production Process Quality.

CUSUM Control Chart for Symlets Wavelet to Monitor Production Process Quality.

Контроль двопараметричних технологічних процесів

An urgent practical task is to ensure the serviceability and safety of complex technical and information systems by monitoring their current states. Additionally, an important task is to use control charts to automatically detect the moment when certain limit states are exceeded by the characteristic features (parameters) of the technological process. In practice, it is quite common for characteristic features to be correlated. The presence of correlation affects the effectiveness of control charts in determining the moment of process disruption. The study objective is to compare the effectiveness of using different control chart types to control a correlated parameter process. The following control charts were studied: Shewhart, Hotelling, and modified Shewhart charts − principal component charts. Numerical modeling of a two-parameter process was conducted with the introduction of a non-random change in one of them at two control points, i.e., the process disorder was modeled at the appropriate time points. Subsequently, statistical studies of the modeled process were conducted using three types of control charts. The results revealed that the Shewhart charts did not reveal any process disruption, which is explained by the correlation of the parameters. Hotelling charts revealed a processing disorder at two points, but they do not indicate the cause of the disorder. The calculated partial Hotelling criterion did not indicate the cause of the disorder. The principal component charts revealed both the presence of a process disorder in two points and identified which parameter caused the disorder. This result demonstrates the effectiveness of principal component charts in detecting process disorder with correlated parameters, as well as their superiority over other charts that were studied. In addition, with an increase in the number of parameters, this method becomes even more effective, as it allows to reduce the number of parameters studied while maintaining their statistical significance. Keywords: effectiveness of control charts, Shewhart charts, Hotelling charts, principal components, statistical quality control, correlation of parameters.

Agile monitoring dashboard for clinical research studies

BackgroundClinical trial success hinges on efficient participant recruitment and retention. However, slow accrual and attrition frequently hinder progress. To address these challenges, a novel dashboard tool with control charts has been developed to provide investigators on the multi-site study of Delirium and Neuropsychological Recovery among Emergency General Surgery Survivors (DANE study) with timely information to improve trial recruitment.MethodsA quality monitoring Excel dashboard with control chart functionality developed by the principal investigator’s (PI) group and implemented in a department of a large hospital was re-engineered for research study recruitment purposes. The dashboard provides the PIs and other stakeholders with timely, actionable, and unbiased information on the count of participants who have completed each stage or action within the process, the rates of completion and trends, both for the current week and cumulatively.ResultsThe DANE dashboard was prototyped using Microsoft Excel for accessibility and rapid development. The tool integrates with a REDCap database, simplifying data import and analysis. By facilitating informed decision-making throughout the recruitment process, the DANE dashboard has significantly enhanced clinical trial efficiency and led to changes in the eligibility criteria and improvements in the approach and consent processes.ConclusionsThe DANE dashboard for monitoring participant recruitment and attrition in research studies represents a significant step towards enhancing study management and decision-making processes. It can be adapted to other clinical studies and other staged processes with attrition. The generic version, currently under development, holds promise for evolving into a valuable simulator by incorporating a spreadsheet for generating random data and accounting for resource constraints. This enhancement could further be augmented by integrating forecasting capabilities into the control charts.Trial registrationThe Delirium and Neuropsychological Recovery among Emergency General Surgery Survivors (DANE) study (NCT05373017, 1R01AG076489-01) is a multi-site, two-arm, single-blinded randomized controlled clinical trial to evaluate the efficacy of the Emergency General Surgery (EGS) Delirium Recovery Model to improve the cognitive, physical, and psychological recovery of EGS delirium survivors over 65. The DANE study received approval from the University of Wisconsin-Madison/University of Wisconsin Hospitals and Clinics Institutional Review Board (IRB, no. 2022–0545, approval date September 14, 2022), and Indiana University agreed to cede IRB review to University of Wisconsin-Madison/University of Wisconsin Hospital and Clinics (September 29, 2022).

A robust diagnostic approach in mean shifts for multivariate statistical process control

The control chart is very useful and efficient to detect abnormal changes of a process, however it is difficult to identify out-of-control variables that are responsible for the change. Accurate fault diagnosis has become increasingly important, which helps engineer quickly eliminate the root causes of abnormal changes. However, most of existing diagnostic methods were developed under the assumption of normality, of which the performance is affected by the nonnormality. To overcome these limitations, this paper attempts to combine Bootstrap technique to propose a diagnosis method that is robust to underlying distribution, but also applicable to the case of small out-of-control sample size. Bootstrap technique is used to improve the diagnostic ability of the diagnostic method, which is our first attempt. Compared with the existing procedures, numerical simulations favour the proposed procedure. Finally, an example from a bolt production process is provided to illustrate the implementation of the proposed procedure.

Implementing a Quality Improvement Initiative to Screen for Dementia in a Down Syndrome Specialty Clinic.

Using quality improvement methods, we aimed to implement a protocol to assess for dementia among adults with Down syndrome (DS). To track implementation, interval retrospective chart review of patients with DS with visits to the Massachusetts General Hospital DS Program (MGH DSP) was conducted quarterly. The impact of a newly implemented protocol created and informed by clinical experts in the MGH DSP including laboratory tests, imaging, referrals, and screening tools for dementia and mental health concerns, was analyzed using statistical process control charts. From December 2021 to December 2022, the MGH DSP developed and implemented a new clinical protocol to screen for dementia in 44 adults with DS, ages 40 and above, at a total of 48 visits. We found high rates of completion of two screening surveys (85% and 81%, respectively) and an 84% adherence to our overall protocol elements by clinical staff. Among those with dementia-like symptoms, medical evaluation was collected and summarized. We show that it is possible to successfully implement a new protocol, including the use of a dementia screener, in line with published evidence-based care guidelines for adults with DS. We present our protocol as one successful approach focused on pre-visit screening for symptoms of dementia and mental health concerns and evaluating for co-occurring medical conditions in adults with DS.

A time-varying EWMA-CV control chart based on Upper Triangular Minimax ranked set sampling method

When the process mean fluctuates with time and process standard deviation is a linear function of process mean, the coefficient of variation is usually used to measure the process variation in industry. On the other hand, the use of Ranked Set Sampling as a smart sampling technique enables the ranking of samples before measurement. The purpose of this research is to introduce a new sampling method referred to as Upper Triangular Minimax Ranked Set Sampling and design a time-varying Exponentially Weighted Moving Average control chart to monitor the coefficient of variation. Also, in this research, average run length, standard deviation of run length, median of run length, extra quadratic loss, relative average run length and performance comparison index criteria under two states including zero-state and steady-state have been used to compare the performance of the designed control chart with the ones provided in the literature. The results show the superiority of the proposed control chart based on the novel sampling method rather than the control charts available in the literature. The application of the proposed control chart is also shown through a real case and compared with available competitors in the literature.

Adaptive EWMA Control Chart by Adjusting the Risk Factors through Artificial Neural Network

ABSTRACTThis article focuses on operationalizing a quality improvement framework to improve the quality and safety of patients in hospitals. Assessing the effectiveness of health care services, especially utilizing various patient health statuses, poses several difficulties. The artificial neural networks model assesses patient risk factors and enhances proper management. Our proposed approach extends the exponentially weighted moving average (EWMA) control chart to a risk‐adaptive EWMA chart. This chart is developed from residuals estimated from the artificial neural networks model, thus allowing an assessment of actual data from the patients undergoing cardiothoracic surgery. For patient evaluation, we employ artificial neural networks to establish the suggested control chart. The results indicate that this chart outperforms other methods, such as the risk‐adjusted EWMA chart for shift detection, which may present improvements in the healthcare system's patient care.