Moving Range Control Charts Research Articles

Fuzzy control charts for individual observations to analyze variability in health monitoring processes

In healthcare monitoring, the quality of medical services and patient outcomes are significantly influenced by the unnatural variations emphasizing the importance of effective control and monitoring strategies. In such scenarios the quality is compromised when a shift in the medical process is not detected timely. In this study, we commenced a comprehensive analysis of the variability present in the patient's hematocrit levels. We introduced a novel approach using fuzzy control charts for individual measurements to detect shift within hematocrit levels effectively. For the initial forecasting of hematocrit level variability, we employed the exponential smoothing method by using R software. Following this, we proposed a set of fuzzy control charts intended for individual measurements, including the fuzzy moving average control chart, fuzzy weighted moving control chart, and fuzzy moving range control chart. These control charts are defined with fuzzy control rules, allowing them to analyze and interpret the small shifts in the process precisely. Also, to measure the process's capability for providing a deeper understanding of the processes, fuzzy process capability indices are introduced. A Monte Carlo simulation approach is employed to obtain the performance metrics. Finally, a case study sourced from Kaggle was conducted to evaluate the performance of the proposed fuzzy control charts for assessing hematocrit levels.

Quality analysis of BCG vaccine for bladder cancer immunotherapy using Shewhart control charts

SCIENTIFIC RELEVANCE. The complexity of standardising immunobiologicals, including bacillus Calmette–Guérin (BCG) vaccines, requires improving the approach to statistical analysis of consistency in their quality indicators. Quality analysis tools include Shewhart control charts, which can be used for real-time and retrospective identification and analysis of trends in the processes of interest.AIM. This study aimed to analyse quality indicators of a BCG vaccine for bladder cancer immuno­therapy using Shewhart control charts to monitor consistency in manufacturing processes and quality testing of vaccines.MATERIALS AND METHODS. The study involved a retrospective analysis of the bacterial concentration, dispersity index, loss-on-drying, and potency data obtained during the quality control of Imuron-vac, a BCG vaccine for bladder cancer immunotherapy, in the past 5 years. As part of the statistical data analysis, the authors calculated the means, investigated the corre­lation between the test results provided by the manufacturer and the quality control results obtained at the testing centre (TC), and checked the distributions for normality. Individuals and moving range control charts were plotted and analysed for each quality indicator.RESULTS. The correlation coefficients (r) ranged from 0.34 to 0.70 for the data submitted by the manufacturer and the data obtained at the TC. Pearson’s goodness-of-fit test showed that the distribution of the quality control results obtained at the TC was normal, with χ2 = 14.03 for the dispersity index values and χ2 = 4.93 for the loss-on-drying measurements. Shewhart control charts for loss-on-drying and potency data suggested that the production process and/or the testing process deviated from the state of statistical control. Consistency monitoring of the bacterial concentration and the dispersity index showed that the state of statistical control was achieved. The authors established control limits for further real-time monitoring of the dispersity index consistency.CONCLUSIONS. Shewhart control charts are applicable to the quality control of BCG vaccines for bladder cancer immunotherapy at the TC. Continuous monitoring of consistency in quality indicators is needed to improve the standardisation of vaccine production processes and testing methods, as this standardisation will result in high product quality.

Effectiveness evaluation of the energy efficiency and conservation measures for stipulation of Thailand energy management system in factory

The energy management system in Thailand uses line graphs for monitoring energy consumption or specific energy consumption without determining which energy measure has a significant effect on achieving the specific energy target. In this study, energy saving potential was evaluated to understand energy performance based on correlations between production volume and energy consumption. Energy targets were set so as to identify energy measures in the subsystems with energy consumption of approximately 80% separated in two ways: electricity and heat. The objective of this study is to create a new method for controlling and monitoring specific energy consumption from small to large shifts by incorporating the modified individuals and moving range control charts using a small number of subgroups. These modified control charts that have a run-down pattern with 8 consecutive points below the center line were first initiated to identify the effectiveness of implementing energy measures in the energy sector, whereas the control charts are typically used to detect out-of-control processes from abnormalities in process variation for a large number of subgroups. The control charts are reliable and not only for electricity but also heat. Additionally, the effectiveness of energy measures can be achieved through one or more measures.

Application of Statistical Process Control for Structural Health Monitoring of a High-Speed Railway Track System

To make full use of the massive monitoring data accumulated in China high-speed railways, an improved statistical process control (SPC) framework was introduced to analyze the discontinuous monitoring data of the track system on a high-speed railway elevated station. Multilinear regression models and time series difference equation (TSDE) models were first developed to separate common-cause variations in the monitoring data. Then, individual control charts, moving range control charts, and exponentially weighted moving average control charts were constructed to detect special-cause variations. Results showed that the variations of girder displacement and track slab–girder relative displacements mainly resulted from temperature effects and linear trends related with material damages. Moreover, visible serial dependence was found in the regression model residuals, which could be effectively captured by the TSDE model. Numerous outliers were detected at the measuring points of rail–track slab displacement 15 and track slab–girder relative displacement 17 by more than three control charts, implying higher sensitivity to special causes. With respect to the special causes triggering the anomalous responses of local and overall track systems, sixteen and twenty-eight significant special events were detected, respectively.

Capacity Analysis of Cable Insulation Crosslinking Production Process Based on IoT Big Data

In the cable insulation crosslinking production process, the process quality is determined by multiple processes and multiple related process variables, and there are complex autocorrelation and cross-correlation among these process variables. In this paper, different process variables are studied. By implementing statistical process control on a univariate, single value control chart (I) and moving range control chart () are drawn, and process capability CP is analyzed to find variables with weak process capability for improvement. At the same time, based on the background of the continuous development of the Internet of Things and big data technology, this paper evaluates the complex autocorrelation and cross-correlation between the overall process variables, adopts the multivariate statistical process control technology, and combines the multivariate HOTELLING T2 control chart and MEWMA control chart to reflect the statistical characteristics of multiple variables, thus avoiding misjudgment or omission of process quality monitoring model. By studying the setting of cable production process parameters and quality standards, combined with the application of complete multivariate statistical process control (MSPC) around the evaluation results, process, and technical requirements, this research can provide effective guidance for the cable insulation crosslinking production process

Production Process Control for Aero Engine Part Manufacturing

This paper presents a method for production process control for aero engine part manufacturing based on product quality problem identification and statistical process control. A framework is provided. The control flow of the method is developed. The proposed method applies single value moving range control chart to monitor the production process of aero engine parts. An application case is provided. The results of the case study indicate the quality improvement of the products produced. The research can provide a reference for companies in aviation industry in China to apply control charts to monitor and control production processes for product quality improvement.

Analysis and control of the paper moisture content variability by using fuzzy and traditional individual control charts

For several decades, the fuzzy theory has been used to develop the so-called fuzzy control charts to analyze process data in a more reliable and flexible way than traditional control charts. Control charts are more commonly used in the papermaking processes to check the stability of the data source. These charts are conceived as devices that enhance routine time-by-time adjustments during the production process, so their value is incomparable. A critical variable that must be controlled during the papermaking process is the moisture content of the paper. However, due to the paper’s hygroscopic characteristics, its moisture content values may carry some uncertainty derived from measurement systems and the nature of the papermaking process. Therefore, an alternative approach that includes fuzz control charts to handle these uncertainties is proposed in this paper. The method to convert individual data to fuzzy numbers is based on the sigma level process as a first stage, and then, the fuzzy individual and moving range control charts are introduced using the α−cut fuzzy midrange approach. Data from the moisture content of a 240 ​g coated paper sample is used to prove the performance of the fuzzy individual control charts. According to the proposed methodology, findings show that fuzzy individual and moving range control charts have greater flexibility than traditional individual control charts by reflecting a larger amplitude of their control limits, in addition to a lower number of “out of control” values.

A distribution-free joint monitoring scheme for location and scale using individual observations

Recent advances in data acquisition and storage technologies have permitted the rapid collection of data over time at a relatively low cost. The implication of these advances to modern quality engineering is that many of today’s processes produce samples of individual observations that are grossly non-normal and, potentially, very highly autocorrelated. Consequently, the typical assumptions required by traditional control charting strategies for individual observations are not likely to be met by today’s more modern processes. This presents a significant challenge for today’s quality engineer practitioner, particularly when the false alarm rate of the monitoring strategy should be adequately controlled. In this effort, we propose a new joint monitoring scheme for location and scale using individual observations that relaxes some of the assumptions that limit the use of traditional control charts in today’s practice. In addition, the proposed method is extremely practitioner-friendly and easy to implement. We compare performances of our new scheme to the commonly-used individuals and moving range control charts. Results suggest the proposed scheme provides an effective and robust means to jointly monitor the kind of processes most prevalent in today’s modern industry. We demonstrate our method using open source data available from a selective laser melting (SLM) process, where the detection of hot spots at a given location on a manufactured part was of interest.

Optimal Performance of Caps’ Pressing Process Using Taguchi-Grey Method

This paper aims at improving the performance of caps’ pressing process using Taguchi-grey relational analysis. Five main quality characteristics were considered, involving cap’s height, inner diameter, outer diameter, angle, and plastic weight. The individual and moving range control charts were constructed for each quality characteristic. Then, process capability analysis was then carried out to assess process performance at initial process factor settings, at which the process was found incapable in producing conforming caps for some quality characteristics. Thus, the Taguchi’s designed experiments were employed to provide experimental layout followed by the grey relational analysis to determine the combination of optimal factor settings. Results showed significant in almost all the five quality responses and thereby resulted in huge production and quality cost savings. 

Multi-objective robust design of helical milling hole quality on AISI H13 hardened steel by normalized normal constraint coupled with robust parameter design

Helical milling is a hole-making process which has been applied in hardened materials. Due to the difficulties on achieving high-quality boreholes in these materials, the influence of noise factors, and multi-quality performance outcomes, this work aims the multi-objective robust design of hole quality on AISI H13 hardened steel. Experiments were carried out through a central composite design considering process and noise factors. The process factors were the axial and tangential feed per tooth of the helix, and the cutting velocity. The noise factors considered were the tool overhang length, the material hardness and the borehole height of measurement. Response models were obtained through response surface methodology for roughness and roundness outcomes. The models presented good explanation of data variability and good prediction capability. Mean and variance models were derived through robust parameter design for all responses. Similarity analysis through cluster analysis was performed, and average surface roughness and total roundness were selected to multi-objective optimization. Mean square error optimization was performed to achieve bias and variance minimization. Multi-objective optimization through normalized normal constraint was performed to achieve a robust Pareto set for the hole quality outcomes. The normalized normal constraint optimization results outperformed the results of other methods in terms of evenness of the Pareto solutions and number of Pareto optimal solutions. The most compromise solution was selected considering the lowest Euclidian distance to the utopia point in the normalized space. Individual and moving range control charts were used to confirm the robustness achievement with regard to noise factors in the most compromise Pareto optimal solution. The methodology applied for robust modelling and optimization of helical milling of AISI H13 hardened steel was confirmed and may be applied to other manufacturing processes.

P143] Quality assurance of tomoedge dynamic jaws: A retrospective analysis with the statistical control charts approach

Purpose TomoEDGETM refers to the sliding window motion of the jaws along the superior-inferior edge of the treatment target. An automatic procedure, (“Dynamic Jaws Sweep” DJS), tests the movement functions with specific exercises and measures the alignment of the linac photon source in relation to jaw collimation evaluating the longitudinal dose profiles. The results of the DJS were retrospectively analyzed using the statistical control charts. Methods From June 2013 to September 2016 the DJS was performed, on a weekly basis, on a TomoHDATM system for a total amount of 130 runs. All measurements were analyzed with TomoTherapy Quality Assurance (TQA) software that provides: the fluence output factor (FOF) for the three clinical jaws aperture (J20, J14, J7), the time skew and the linac photon source position along the longitudinal direction (IECy SP). Qualitative graphs related to specific exercises are also produced. For each quantitative parameter the Individual (ICC) and the Moving Range chart control (MRCC) were implemented. For both charts the Central value, the Upper and Lower Control Limit are based on the average and on the statistical variance of the first 10 runs. The control chart results were compared to the TQA limits, suggested by the vendor. Results The FOF values outside the limits overlap with erroneous qualitative graphs, pointing out a real critical issue of jaw movements. Both the Individual and the Moving range control chart showed analogous results; indeed, with the ICC the FOF were outside limits in 12.3%, 13.1% and 20% of their measurements for J20, J14 and J7 jaw settings respectively and the time skew parameter only in 4.6%, while, with the MRCC, the corresponding values were 9.2%, 10.1%, 14.3% and 9.0% for the time skew parameter. The IECy SP, while remaining within vendor specific values, rapidly diverged from its initial value with an increasing trend, eventually approaching the zero value. Conclusions Control chart approach seems to be an useful tool for studying in depth the results of the DJS procedure. Much more stringent TQA limits, based on the own machine behavior, are useful to easily detect critical issue related to the dynamic jaws movements.

Statistical Process Control pada Pengujian Kestabilan Alat Pengukur Kadar Glukosa Darah Non-Invasif

Stability of a measuring tool is very important. It is necessary to get measurement results that have high precision. This research was conducted to see the stability of Non-Invasive Blood Glucose Measurement Tool. Data of this research is data research from "Pengembangan dan Uji Klinis Purwarupa Alat Pemantauan Kadar Glukosa Darah Non-Invasif Institut Pertanian Bogor". This research used analysis of statistical process control using Individual Moving Range control chart. The results obtained indicate stability process on each respondent. However, among respondents who are one with other respondents has an unstable process despite glucose levels the blood of the respondents is the same.

Using mixed goal programming to optimize performance of extrusion process for multiple responses of irrigation pipes

The performance of the polyethylene extrusion process used in the plastic industry was optimized using mixed goal programming. Four responses, the roll weight, production cycle time, distance between emitters, and thickness, are crucial. After a combination of initial factor settings was determined, individual and moving range control charts were established for each response. Two-phase optimization was implemented using L18 and L9 arrays for conducting experiments. A two-phase fuzzy goal programming model was formulated and employed to determine the combination of optimal factor settings, which was used to perform experiments. The results showed a significant reduction in the average production time from 13.0 to 11.316 min. Moreover, the average respective relative percentages of variability reduction for the roll weight, production cycle time, distance between emitters, and thickness were 48.547%, 49.048%, 47.174%, and 63.704%, respectively, and the corresponding estimated process capability indices for the combination of initial (optimal) factor settings were 0.202 (1.440), 0.330 (0.914), and 0.460 (1.456). Such improvement results in significant reduction in quality costs.

A method for evaluating treatment quality using in vivo EPID dosimetry and statistical process control in radiation therapy

PurposeDue to increasing complexity, modern radiotherapy techniques require comprehensive quality assurance (QA) programmes, that to date generally focus on the pre-treatment stage. The purpose of this paper is to provide a method for an individual patient treatment QA evaluation and identification of a “quality gap” for continuous quality improvement.Design/methodology/approachA statistical process control (SPC) was applied to evaluate treatment delivery using in vivo electronic portal imaging device (EPID) dosimetry. A moving range control chart was constructed to monitor the individual patient treatment performance based on a control limit generated from initial data of 90 intensity-modulated radiotherapy (IMRT) and ten volumetric-modulated arc therapy (VMAT) patient deliveries. A process capability index was used to evaluate the continuing treatment quality based on three quality classes: treatment type-specific, treatment linac-specific, and body site-specific.FindingsThe determined control limits were 62.5 and 70.0 per cent of the χ pass-rate for IMRT and VMAT deliveries, respectively. In total, 14 patients were selected for a pilot study the results of which showed that about 1 per cent of all treatments contained errors relating to unexpected anatomical changes between treatment fractions. Both rectum and pelvis cancer treatments demonstrated process capability indices were less than 1, indicating the potential for quality improvement and hence may benefit from further assessment.Research limitations/implicationsThe study relied on the application of in vivo EPID dosimetry for patients treated at the specific centre. Sampling patients for generating the control limits were limited to 100 patients. Whilst the quantitative results are specific to the clinical techniques and equipment used, the described method is generally applicable to IMRT and VMAT treatment QA. Whilst more work is required to determine the level of clinical significance, the authors have demonstrated the capability of the method for both treatment specific QA and continuing quality improvement.Practical implicationsThe proposed method is a valuable tool for assessing the accuracy of treatment delivery whilst also improving treatment quality and patient safety.Originality/valueAssessing in vivo EPID dosimetry with SPC can be used to improve the quality of radiation treatment for cancer patients.

Analysis and control of variability by using fuzzy individual control charts

The detection of changes in a process within shortest time provides significant benefits in terms of cost and quality. When considering the cost which would show up because of delays in identifying variability, detecting the deviation in the process accurately and quickly has a great importance for investors. In this paper, return volatility in the Borsa Istanbul-30 index (BIST-30) has been analyzed and a fuzzy control chart for individual measurements (FCCIM) has been proposed for use in determining and controlling in the variables of the BIST-30 index. For this purpose, firstly exponential smoothing method is used to forecast the variability of stock price of BIST-30 index by using MINITAB statistical software, and then a fuzzy control chart for individual measurements (FCCIM) which are fuzzy individual control chart (FICC) and fuzzy moving range control chart (FMRCC) with fuzzy control rules have been developed to be used in determining the variability of the process. For this aim, some fuzzy rules have been defined by using Ms EXCEL in fuzzy control chart for individual measurements. A real case application from Istanbul Stock Exchange for BIST-30 has been managed to check the effectiveness of suggested fuzzy control charts.

SU‐D‐201‐04: Evaluation of Elekta Agility MLC Performance Using Statistical Process Control

Purpose:to evaluate the performance and stability of the Elekta Agility MLC model using an automated quality control (QC) test in combination with statistical process control tools.Methods:Leaf positions were collected daily for 11 Elekta units over 5–19 months using the automated QC test, which analyzes 23 MV images to determine the location of MLC leaves relative to the radiation isocenter. The leaf positions are measured at 5 nominal positions, and images are acquired at collimator 0° and 180° to capture all MLC leaves in the field‐of‐view. Leaf positioning accuracy was assessed using individual and moving range control charts. Control limits were recomputed following MLC recalibration (occurred 1–2 times for 4 units). Specification levels of ±0.5, ±1 and ±1.5mm were tested. The mean and range of duration between out‐of‐control and out‐of‐specification events were determined.Results:Leaf position varied little over time, as confirmed by very tight individual control limits (mean ±0.19mm, range 0.09–0.44). Mean leaf position error was −0.03mm (range −0.89–0.83). Due to sporadic out‐of‐control events, the mean in‐control duration was 3.3 days (range 1–23). Data stayed within ±1mm specification for 205 days on average (range 3–372) and within ±1.5mm for the entire date range. Measurements stayed within ±0.5mm for 1 day on average (range 0–17); however, our MLC leaves were not calibrated to this level of accuracy.Conclusion:The Elekta Agility MLC model was found to perform with high stability, as evidenced by the tight control limits. The in‐specification durations support the current recommendation of monthly MLC QC tests with a ±1mm tolerance. Future work is on‐going to determine if Agility performance can be optimized further using high‐frequency QC test results to drive recalibration frequency. Factors that can affect leaf positioning accuracy, including beam spot motion, leaf gain calibration, drifting leaves, and image artifacts, are under investigation.

Development of fuzzy individual x and moving range control chart

Control charts are very effective tools which are used for detecting the assignable cause of variation. This paper investigated fuzzy individual x and moving range (IX-MR) control chart in the case of uncertainty for process parameters and data. In this paper, fuzzy mode method was employed to design fuzzy IX-MR control chart for triangular and trapezoidal fuzzy number fuzzy numbers coded by MATLAB software. Then unnatural patterns were studied in a fuzzy environment. To validate the model, an industrial example was utilised and then the results were verified. Results showed that the fuzzy one was more sensitive than the classic cases and more consistent with the actual situation. Also, fuzzy mode method was more sensitive for trapezoidal than triangular fuzzy numbers.

APPLICATION OF THE GROSS ERROR ANALYSIS TO DISORDERS IDENTIFICATION IN MANUFACTURING PROCESS—A CASE STUDY

The article contains a description of the procedures to be followed in the analysis of the stability of the manufacturing processes based on an Individual Control Chart (XI) and a Moving Range (MR) Control Chart. In the following part of the article, we present the three-sigma rule and the confidence intervals for the average, which enable the elimination of random errors caused by, e.g. an improper conducting of a measurement.Summary of the theoretical considerations is a study case in which survey data from a company, producing car seat frames, are used. The conducted study indicates that the elimination of Gross Error is significant to identify and eliminate the variability of production processes.

A Simulation Study on the Average Run Length of the Moving Range Control Chart

The moving range control chart is usually established to monitor the variability of the process characteristic for situations where the individuals control chart is applied. In the design of a control chart, the average run length (ARL) of the chart is usually used as an important indicator for evaluating control chart performance. In the present paper, the out-of-control ARL of the moving range chart is investigated using computer simulation. Three types of data are generated for study: normal and independent data, non-normal data, and autocorrelated data, such that the effects of non-normality and autocorrelation on the detection ability of the chart can be investigated. From the simulation results, we find that positive skewness coefficient present in the underlying distribution would produce a shorter out-of-control ARL than the normal data. Also, if we are able to know that the data are positively highly autocorrelated, then “span two” should be used for the operation of the moving range chart to keep the chart's detection ability.

Monitoring the EDM Characteristics of Ti-6Al-4V Process Stable Using Individual Moving Range Control Chart

This study deals with the optimization of the process parameters, with regard to multiple performance characteristics, involved in the electrical discharge machining (EDM) of titanium alloy (Ti6Al4V).Based on the grey-Taguchi method were conducted to examine the effect of EDM parameters (pulse current, open voltage, pulse-on-time, and duty factor) on the electrode wear rate, material removal rate and surface roughness. Experimental results indicate that optimal process parameters can be determined effectively so as to improve multiple EDM qualities through this approach. Furthermore, control charts tell us when special causes of variation are impacting machining process. Control charts are basic and powerful tools in statistical process control and are widely used to control various industrial processes. The aim of statistical process control is to explore the predictability of a process. It was found that the EDM process and product characteristics are stable over time.