Monitoring Charts Research Articles

Diagram Kendali Triple Homogeneously Weighted Moving Average dan Penerapannya pada Proses Plating Aksesori Mobil PT. XYZ

Abstract. Quality control is carried out to maintain or improve product quality to meet consumer satisfaction. One of the methods that can be used is control chart. Triple Homogeneously Weighted Moving Average (THWMA) is one of the control chart techniques that can be used in quality control. The Triple Homogeneosly Weighted Moving Average control chart is a control chart for efficient monitoring of the process average under zero state and steady state which is very sensitive in identifying small and continuous changes in the process effectively and using three times sample information. THWMA control is considered superior to the previously existing Homogeneosly Weighted Moving Average (HWMA) and Double Homogeneosly Weighted Moving Average (DHWMA) control charts and from other competitors. In our thesis we will apply a THWMA control chart to monitor the plating process of PT. XYZ. The results obtained in Phase I of the in control chart, so that the parameter values ​​used in Phase II are of 10.691 dan of 0.67. Phase II control chart using K = 2.994 and λ = 0.25, it is found that the THWMA control chart is controlled.
 Abstract. Quality control is carried out to maintain or improve product quality to meet consumer satisfaction. One of the methods that can be used is control chart. Triple Homogeneously Weighted Moving Average (THWMA) is one of the control chart techniques that can be used in quality control. The Triple Homogeneosly Weighted Moving Average control chart is a control chart for efficient monitoring of the process average under zero state and steady state which is very sensitive in identifying small and continuous changes in the process effectively and using three times sample information. THWMA control is considered superior to the previously existing Homogeneosly Weighted Moving Average (HWMA) and Double Homogeneosly Weighted Moving Average (DHWMA) control charts and from other competitors. In our thesis we will apply a THWMA control chart to monitor the plating process of PT. XYZ. The results obtained in Phase I of the in control chart, so that the parameter values ​​used in Phase II are of 10.691 dan of 0.67. Phase II control chart using K = 2.994 and λ = 0.25, it is found that the THWMA control chart is controlled.

Optimal design of S2‐EWMA control chart for short production runs

AbstractIn recent fast pace smart manufacturing environment, the short production runs (SPR) control charts offer a higher degree of flexibility compared to the traditional control charts. This is due to Phase I data collection not needed for SPR control charts and this eases practitioners in estimating the parameters and setting up the control charts. This paper proposes the methods to optimally design the SPR S‐chart and SPR S2‐EWMA control chart for process variance monitoring in terms of truncated average run length (TARL), truncated standard deviation of the run length (TSDRL), and expected truncated average run length (ETARL) performance measures. The optimal parameters in minimizing the TARL and ETARL values for SPR S2‐EWMA control chart are presented. The control chart performances are compared between both the SPR S‐chart and SPR S2‐EWMA control charts. Results show that SPR S2‐EWMA control chart has a higher chart sensitivity and it triggers the out‐of‐control signal faster than the SPR S‐chart. An illustration using the real electronic manufacturing industry data is given in this paper to explain the implementation of the SPR S‐chart and SPR S2‐EWMA control chart.

Optimized control charts using indifference regions

In statistical process monitoring, the CUSUM and EWMA control charts have received considerable attention because of their remarkable ability to detect small sustained shifts. In practice, small process variation and shifts are anticipated beforehand in many processes, so the focus should be on detecting a moderate to a large shift. The aforementioned charts identify minor changes in population parameters as out-of-control scenarios; thus, “small” and potentially practically insignificant shifts are producing signals. To counteract this, both charts are amended to accommodate an indifference region by optimizing the detection of a shift at the outer boundaries of the indifference region. The results show that the adapted CUSUM and EWMA monitoring schemes yield comparable results. On nearly all occasions, the CUSUM chart outperforms the EWMA chart, yet the EWMA chart seems more robust and is easier to interpret. Furthermore, we provide two practical examples to illustrate the use-case of optimized charts to mitigate small (unimportant) variations, such as seasonality and modest temporary shifts. Overall, this work provides a general approach tailored to practice in quality control, e.g., as prescribed by ISO standards. It also answers a recent call in statistical process monitoring literature to reconsider the design of control charts.

Bayesian multivariate control charts for multivariate profiles monitoring

ABSTRACT In many topical applications, the product’s quality can be well described in terms of statistical regression relationships between one or more response and a set of explanatory variables. In the literature, various types of regression models have been proposed for profile monitoring applications, and each of those regression models can be implemented and applied in its standard frequentist’s and its Bayesian variant. We formulate two popular Phase II multivariate cumulative sum control charts for monitoring multivariate linear profiles in terms of Bayesian regression models, and we show empirically that the resulting new Bayesian control charts perform better than the corresponding non-Bayesian control charts. For the comparative evaluation of the control charts we employ the average run length criterion. Moreover, we propose a new Bayesian approach, which we refer to as the informative prior generation method. The key idea of this method is to make use of historical datasets to generate informative prior distributions. The advantage of this method is that we do not ignore the historical data from Phase I. Instead we re-use it to construct informative prior distributions for Phase II monitoring. The applicability and the superiority of the proposed Bayesian control charts are illustrated through extensive simulation studies.

The Effect of Honey Therapy on the Management of Oral Mucosites in Head and Neck Cancer Patients with Chemoradiotherapy: A Randomized Controlled Study

Purpose: Mucositis management has an important place in the treatment of these cancer patients. Therefore, the aim of our study is to examine the effect of honey treatment on oral mucositis management in patients with head and neck cancer receiving radiotherapy. 
 Methods: The study was planned as a randomized controlled, single-blind, parallel group study. The study was conducted on 32 patients with 16 patients in each parallel group. Five data collection tools were used to collect the research data. Patient Identification Form , Researcher Mucositis Index, VAS- Pain, Honey Management Monitoring Chart (given to the patients in the treatment group). Oral Care Management Follow-up Form (given to the patients in the control group). 
 Results: The number of days with mucositis was similar in both parallel groups (Honey Group / Mean ± SD: 14.88 ± 7.36, Bicarbonate Group / Mean ± SD: 14.38 ± 6.33).
 Conclusion: Honey application has not been found to be superior to bicarbonate mouthwash on the development and staging of oral mucositis.

A distribution-free control chart for joint monitoring of location and scale in finite horizon productions

A distribution-free control chart for joint monitoring of location and scale in finite horizon productions

On designing new mixed modified exponentially weighted moving average - exponentially weighted moving average control chart

Control charts are an extremely valuable tool in statistical process control (SPC) for processing information from a system or procedure over time and displaying statistically significant changes in data while also showing how a system or process is changing. The modified exponentially weighted moving average - exponentially weighted moving average control chart (MEME) is proposed as a new mixed control chart for monitoring and quickly detecting moderate to small shifts in the mean process. Average and median run lengths are instruments for assessing efficiency through Monte Carlo simulations based on symmetric and nonsymmetric distributions. The performance comparison showed that the proposed chart outperformed alternatives control chart in simulation and in two real-data applications by lowering the out-of-control average run length.

Mixed Tukey Exponentially Weighted Moving Average-Modified Exponentially Weighted Moving Average Control Chart for Process Monitoring

The goal of this study is to present the mixed Tukey exponentially weighted moving average-modified exponentially weighted moving average control chart (MEME-TCC) for monitoring process location with symmetric and skewed distributions in an attempt to significantly improve detection ability. With the benefits of nonparametric assumption robustness. The average and median run lengths are supporting measurements for assessing the performance of a monitoring scheme using Monte Carlo simulation. Furthermore, the average extra quadratic loss (AEQL), relative mean index (RMI), and performance comparison index (PCI) can all be used to evaluate overall performance criteria. The proposed chart is compared with existing charts such as; EWMA, MEWMA, TCC, MEME, MMEE, and MMEE-TCC. The comparison result shows that the proposed chart is the best control chart for detecting small to moderate shifts among all distributional settings. Nevertheless, the EWMA chart detects large shifts more effectively than other charts, except in the case of the gamma distribution, where MEWMA performs best. The results of adapting the proposed control chart to two sets of real data corresponded to the research findings. Doi: 10.28991/ESJ-2023-07-03-014 Full Text: PDF

Design of moving average chart and auxiliary information based chart using extended EWMA

The control chart is the most valuable tool in the manufacturing process to track the output process in the industries. Quality specialists always want a visual framework that recognizes sustainable improvements in the monitoring processes. The efficiency of a control chart is increased by utilizing a memory-based estimator or by using any extra information relevant to the key variable. In this study, we present Extended EWMA (EEWMA) and EWMA based monitoring charts for observing the process location using moving average (MA) statistic under two different situations, i.e., when some extra information is known and unknown. We also propose an EEWMA control chart using Auxiliary Information. The output of these charts is evaluated and contrasted to the various existing charts on the basis of average run length (ARL). The comparison indicates that the proposed charts outperform rivals in identifying all types of shifts in the process location parameter. The implementation of these plans is also rendered to incorporate them in a practical situation.

A maximum dual CUSUM chart for joint monitoring of process mean and variance

ABSTRACT A dual chart provides more sensitivity than the conventional chart when it is known that a shift size varies within a given interval. In this paper, we propose a maximum dual CUSUM (MDC) chart for monitoring the joint shifts (that lie in different intervals) in the mean and variance of a normally distributed process. The Monte Carlo simulation method is used to estimate the zero-state and steady-state run-length properties of the MDC chart, which include the average run-length (ARL), expected weighted run-length (EWRL) and expected relative ARL (ERARL). Based on detailed run-length comparisons in terms of the EWRL and ERARL, it is found that the MDC chart outperforms the maximum adaptive EWMA (MAE) and maximum weighted adaptive CUSUM (MWAC) charts when detecting a range of the joint shift sizes. Moreover, the diagnostic abilities of the MDC chart are also studied. Real and simulated datasets are considered to demonstrate the implementation of the MAE, MWAC and MDC charts.

On Cointegration Analysis for Condition Monitoring and Fault Detection of Wind Turbines Using SCADA Data

Cointegration theory has been recently proposed for condition monitoring and fault detection of wind turbines. However, the existing cointegration-based methods and results presented in the literature are limited and not encouraging enough for the broader deployment of the technique. To close this research gap, this paper presents a new investigation on cointegration for wind turbine monitoring using a four-year SCADA data set acquired from a commercial wind turbine. A gearbox fault is used as a testing case to validate the analysis. A cointegration-based wind turbine monitoring model is established using five process parameters, including the wind speed, generator speed, generator temperature, gearbox temperature, and generated power. Two different sets of SCADA data were used to train the cointegration-based model and calculate the normalized cointegrating vectors. The first training data set involves 12,000 samples recorded before the occurrence of the gearbox fault, whereas the second one includes 6000 samples acquired after the fault occurrence. Cointegration residuals—obtained from projecting the testing data (2000 samples including the gearbox fault event) on the normalized cointegrating vectors—are used in control charts for operational state monitoring and automated fault detection. The results demonstrate that regardless of which training data set was used, the cointegration residuals can effectively monitor the wind turbine and reliably detect the fault at the early stage. Interestingly, despite using different training data sets, the cointegration analysis creates two residuals which are almost identical in their shapes and trends. In addition, the gearbox fault can be detected by these two residuals at the same moment. These interesting findings have never been reported in the literature.

Assessment of the neonatal referral and transport system for patients with gastroschisis in Kenya

To assess the neonatal referral and transport system for gastroschisis patients referred to a tertiary level hospital in Kenya. This was a prospective cross-sectional study carried out at Kenyatta National Hospital (KNH) which recruited patients with gastroschisis using consecutive sampling approach. Data were collected on pre-, intra-transit factors, time and distance covered. Assessment was done using pre and intra transit factors as per the standard transport protocols in literature. Twenty-nine patients presented with gastroschisis during the eight month study period. Mean age was 7.07h. There were 16 (55.2%) males and 13 (44.8%) females. Mean birthweight was 2020g, and a mean gestational age of 36.5weeks. Mean duration of transit was 5h. Mean distance from referring facility was 153.1km. Most affected factors in the pre-transit protocol were lack of monitoring chart (0%), comment on blood investigations (0%), gastric decompression (3.4%), and prenatal obstetric scan (44.8%). For intra-transit scores, most affected were incubator use (0%), bowel monitoring (0%), functioning nasogastric tube (13.8%), and adequate bowel cover (34.5%). This study demonstrates that pre-transit and transit care of neonates with gastroschisis is inadequate in Kenya. Interventions needed, as identified by this study, to promote care of neonates with gastroschisis are advised.

A review and comparison of control charts for ordinal samples

Qualitative, more specifically, ordinal data generating processes are common in real-world process control implementations. In this study, a survey of control charts for the sample-based monitoring of independent and identically distributed ordinal data is provided together with critical comparisons of the control statistics, for memory-less Shewhart-type and for memory-utilizing exponentially weighted moving average (EWMA) and cumulative-sum types of control charts. New results and proposals are also provided for process monitoring. Using some real-world quality scenarios from the literature, a simulation study for performance comparisons is conducted, covering sixteen different types of control chart. It is shown that demerit-type charts used in combination with EWMA smoothing generally perform better than the other charts, which may rely on quite sophisticated derivations. A real-world data example for monitoring flashes in electric toothbrush manufacturing is discussed to illustrate the application and interpretation of the control charts in the study.

A joint monitoring of the process mean and variance with a generally weighted moving average maximum control chart

Single control charts are widely used to control assignable causes that shift the process due to variations in the mean and the dispersion. In the present article, the exponentially weighted moving average maximum (EWMA-Max) control chart is extended to the new single generally weighted moving average maximum (referred as GWMA-Max) control chart for joint monitoring of the process mean and variability. The proposed chart is compared with the EWMA-Max and DEWMA-Max charts in terms of the run-length performance measures. The results reveal that the GWMA-Max chart is very efficient in detecting small shifts in the process mean and variability concurrently. Finally, a practical implementation of the GWMA-Max chart is displayed using real and simulated datasets.

Online monitoring for error detection in vat photopolymerization

ABSTRACT Sensor implementation together with data processing in manufacturing provides continuous measurements enabling process monitoring, optimization, and automation. Additive manufacturing (AM) technologies are shifting from one-off and prototyping production to batch, mass, and continuous production. Current AM technologies are mostly focused on small production devices, without monitoring systems. This study aims to bring AM closer to automation by proposing an online monitor system for bottom-up photopolymerization AM (VPP) systems. The sensor-generated data is used to capture the detachment error of built parts from the build platform that otherwise cannot be observed physically by the machine operator. The detachment itself will not stop the build job, which results in lost material and operating time. The online monitoring procedure consists of two phases, an offline and an online one, respectively. The offline phase is used for training a prediction model to be used in connection with a control chart for online monitoring. The monitoring control chart is advantageous as only the detachment predictions need to be recorded. The research carried out in this article brings novelty in both the vat photopolymerization set-up, as well as in an online monitoring procedure that can be easily extended to similar technologies.

Evaluation and prediction of individual growth trajectories

Background Conventional growth charts offer limited guidance to track individual growth. Aim To explore new approaches to improve the evaluation and prediction of individual growth trajectories. Subjects and methods We generalise the conditional SDS gain to multiple historical measurements, using the Cole correlation model to find correlations at exact ages, the sweep operator to find regression weights and a specified longitudinal reference. We explain the various steps of the methodology and validate and demonstrate the method using empirical data from the SMOCC study with 1985 children measured during ten visits at ages 0–2 years. Results The method performs according to statistical theory. We apply the method to estimate the referral rates for a given screening policy. We visualise the child’s trajectory as an adaptive growth chart featuring two new graphical elements: amplitude (for evaluation) and flag (for prediction). The relevant calculations take about 1 millisecond per child. Conclusion Longitudinal references capture the dynamic nature of child growth. The adaptive growth chart for individual monitoring works with exact ages, corrects for regression to the mean, has a known distribution at any pair of ages and is fast. We recommend the method for evaluating and predicting individual child growth.

Evaluating an Intervention to Improve the Quality of Routine Immunization Data and Monitoring System in Enugu State, Nigeria: A Pre-and Post-Study

ABSTRACT Background: Studies evaluating what interventions might improve the quality of routine immunization (RI) data and use in decision-making are scarce in Sub-Saharan Africa. This study assessed the effect of a data-focused intervention on the accuracy of RI data and the quality of the data monitoring system (DMS). Methods: A ”before and after” study was conducted in randomly selected health facilities (n = 60) in Enugu State, Nigeria. Data on three routine vaccinations in tallied registers, facility reports, and District Heath Information System II, and the quality of the DMS were compared before and after the intervention. The DMS covered recording, reporting, archiving, demographic information, core output and analysis, and data use. The intervention included monthly data review meetings and supervision of facility RI staff. We compared mean differences (MD) of log-transformed data accuracy and quality scores between baseline and end-line using paired samples t-test. Additionally, interviews were conducted with purposively selected RI stakeholders (n = 21) on the interventions and self-selected changes in RI data management practices. Data were coded and analyzed thematically using NVivo software version 11. Results: The accuracy of Measles2 facility reports improved post-intervention (MD = -0.62, t = 2.70, ρ = 0.009). The overall quality of the DMS (MD = -10.1, t = -4.4, ρ = 0.001), recording (MD = -6.5, t = -3.0, ρ = 0.048), demographic information (MD = -24.1, t = -6.1, ρ = 0.001), core output and analysis (MD = -14.2, t = -3.9, ρ = 0.004), and data use (MD = -17.05, t = -4.3, ρ = 0.001) improved post-intervention. Regarding the qualitative findings, adopting “tally as you vaccinate,” timely reporting, use of demographic information, archiving paper records, updating the monitoring charts regularly, and data-informed decisions improved the RI data monitoring system. However, resistance to changes in recording practices, staff shortage, use of inappropriate denominators, interrupted supply of monitoring charts, and lack of funds constrained the data monitoring system. Conclusion: Supervision and data review meetings improve the accuracy of Measles2 and the quality of the RI DMS. The feasibility of sustaining the data management practices requires RI stakeholders to address contextual limitations identified in this study.

Performance of Max-HEWMAMS control chart for simultaneous monitoring of process mean and variability in the presence of measurement errors

Performance of Max-HEWMAMS control chart for simultaneous monitoring of process mean and variability in the presence of measurement errors

Enhancing inpatient diabetes care by developing a new Capillary Blood Glucose and ketone monitoring chart: a Quality Improvement Project (QIP)

Enhancing inpatient diabetes care by developing a new Capillary Blood Glucose and ketone monitoring chart: a Quality Improvement Project (QIP)

An earth mover’s distance based multivariate generalized likelihood ratio control chart for effective monitoring of 3D point cloud surface

With the development of measurement technology, non-contact high-definition measurement (HDM) systems have allowed rapid collection of large-scale point cloud data, providing an opportunity to monitor the entire surface geometry of manufactured parts. However, traditional control charts do not apply to such large-scale point cloud data. Although some researchers have proposed the use of improved multivariate control charts for high-dimensional data, the multivariate control charts cannot be directly used for large-scale and autocorrelated point cloud data. Considering the structural characteristics and spatial properties of the point cloud, this paper proposes an earth mover’s distance based multivariate generalized likelihood ratio (EMD-MGLR) control chart to effectively monitor point cloud surface by making full use of the three-dimensional (3D) information of point cloud data. The EMD method regards point cloud data as a distribution and calculates the EMD distance between the two distributions to quantify the deviation region between the point cloud surface and the nominal model. Combined with the multivariate generalized likelihood ratio control chart, the processing quality of the 3D surface can then be monitored by the statistics of EMD. The advantages of the proposed method are illustrated and verified by numerical and experimental examples. An experimental example on the 3D surfaces of combustion chambers is used to illustrate the methodology and to test its effectiveness in monitoring surface defects.