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1
Wang, Hai Yu. "Statistical Process Control on Time Delay Feedback Adjustment Process." Advanced Materials Research 211-212 (February 2011): 305–9. http://dx.doi.org/10.4028/www.scientific.net/amr.211-212.305.
Анотація:
Control chart can be designed to quickly detect small shifts in the mean of a sequence of independent normal observations. But this chart cannot perform well for autocorrelated process. The main goal of this article is to suggest a control chart method using to monitoring process with different time delay feedback controlled processes. A quality control model based on delay feedback controlled processes is set up. And the calculating method of average run length of control charts based on process output and control action of multiple steps delay MMSE feedback controlled processes is provided to evaluate control charts performance. A simple example is used to illustrate the procedure of this approach.
2
Benková, Marta, Dagmar Bednárová, Gabriela Bogdanovská, and Marcela Pavlíčková. "Use of Statistical Process Control for Coking Time Monitoring." Mathematics 11, no. 16 (August 8, 2023): 3444. http://dx.doi.org/10.3390/math11163444.
Анотація:
Technical and technological developments in recent decades have stimulated the rapid development of methods and tools in the field of statistical process quality control, which also includes control charts. The principle of control charts defined by Dr. W. Shewhart has been known for more than 100 years. Since then, they have been used in many industries to monitor and control processes. This paper aims to assess the possibilities of use and the selection of the most suitable type of control chart for monitoring the quality of a process depending on its nature. This tool should help operators in monitoring coking time, which is one of the important control variables affecting the quality of coke production. The autoregressive nature of the variable being monitored was considered when selecting a suitable control chart from the group of options considered. In addition to the three traditional types of control charts (Shewhart’s, CUSUM, and EWMA), which were applied to the residuals of individual values of different types of ARIMA models, various statistical tests, and plots, a dynamic EWMA control chart was also used. Its advantage over traditional control charts applied to residuals is that it works with directly measured coking time data. This chart is intended to serve as a method to monitor the process. Its role is only to alert the process operator to the occurrence of problems with the length of the coking time.
3
Rashid, Kawa. "Design Tukey’s Control Chart and mix with CUSUM Control Chart." Journal of Zankoy Sulaimani - Part A 24, no. 1 (June 20, 2022): 55–66. http://dx.doi.org/10.17656/jzs.10869.
Анотація:
Statistical process control is a collection of valuable tools for detecting alteration in a process. It has wide application in many areas field and other fields where variation is being monitored. The variation may be a natural cause variation or a particular cause variation. Statistical process control deals with the monitoring process to detect disturbances in the process. These disturbances may be from the process mean or variance. This study proposes efficient charts for detecting early shifts in dispersion parameters by applying the Fast Initial Response feature. We propose and compare the performance of different cumulative sum (CUSUM)control charts for phase II monitoring of location based on mean and median. The (CUSUM) control chart, which is a method of data analysis based on John Tukey's principles control chart (TCC), is used to compare the proposed charts with their existing counterparts is used to evaluate new charts to existing charts using performance measures such as average run length, the standard deviation of run length, additional quadratic loss, relative average run length, and performance comparison . The proposed charts detect early shifts in the process dispersion faster and have better overall. This article is a similar effort to design an improved charting structure in the form of mixed or using Tukey -CUSUM chart together, to show the process control chart., and drawing the Average Run Length ARL value.
4
Avakh Darestani, Soroush, and Mina Nasiri. "Statistical process control." International Journal of Quality & Reliability Management 33, no. 1 (December 31, 2015): 2–24. http://dx.doi.org/10.1108/ijqrm-08-2013-0130.
Анотація:
Purpose – In this context, process capability indices (PCI) reveal the process zones base on specification limits (SLs). Most of the research on control charts assumed certain data. However, to measure quality characteristic, practitioners sometimes face with uncertain and linguistic variables. Fuzzy theory is one of the most applicable tools which academia has employed to deal with uncertainty. The paper aims to discuss these issues. Design/methodology/approach – In this investigation, first, fuzzy and S control chart has been developed and second, the fuzzy formulation of the PCIs such as C pm ,C pmu ,C pml , C pmk , P p , P pl , P pu , P pk are constructed when SLs and measurements are at both triangular fuzzy numbers (TFNs) and trapezoidal fuzzy numbers (TrFNs) stages. Findings – The results show that using fuzzy make more flexibility and sense on recognition of out-of-control warnings. Research limitations/implications – For further research, the PCIs for non-normal data can be conducted based on TFN and TrFN. Practical implications – The application case is related to a piston company in Konya’s industry area. Originality/value – In the previous researches, for calculating C p , C pk , C pm and C pmk indices, the base approach was calculate standard deviation for a short term variation. For calculating these indices, the variation between subgroups are being ignored. Therefore, P p and P pk indices solved this fault by mentioning long term and short term variations. Therefore these two indices calculate the actual process capability.
5
Aslam, Muhammad, Nasrullah Khan, and Muhammad Khan. "Monitoring the Variability in the Process Using Neutrosophic Statistical Interval Method." Symmetry 10, no. 11 (November 1, 2018): 562. http://dx.doi.org/10.3390/sym10110562.
Анотація:
Existing variance control charts are designed under the assumptions that no uncertain, fuzzy and imprecise observations or parameters are in the population or the sample. Neutrosophic statistics, which is the extension of classical statistics, has been widely used when there is uncertainty in the data. In this paper, we will originally design S 2 control chart under the neutrosophic interval methods. The complete structure of the neutrosophic S 2 control chart will be given. The necessary measures of neutrosophic S 2 will be given. The neutrosophic coefficient of S 2 control chart will be determined through the neutrosophic algorithm. Some tables are given for practical use. The efficiency of the proposed control chart is shown over the S 2 control chart designed under the classical statistics in neutrosophic average run length (NARL). A real example is also added to illustrate the proposed control chart. From the comparison in the simulation study and case study, it is concluded that the proposed control chart performs better than the existing control chart under uncertainty.
6
Vicentin, Damaris Serigatto, Brena Bezerra Silva, Isabela Piccirillo, Fernanda Campos Bueno, and Pedro Carlos Oprime. "Monitoring process control chart with finite mixture probability distribution." International Journal of Quality & Reliability Management 35, no. 2 (February 5, 2018): 335–53. http://dx.doi.org/10.1108/ijqrm-11-2016-0196.
Анотація:
Purpose The purpose of this paper is to develop a monitoring multiple-stream processes control chart with a finite mixture of probability distributions in the manufacture industry. Design/methodology/approach Data were collected during production of a wheat-based dough in a food industry and the control charts were developed with these steps: to collect the master sample from different production batches; to verify, by graphical methods, the quantity and the characterization of the number of mixing probability distributions in the production batch; to adjust the theoretical model of probability distribution of each subpopulation in the production batch; to make a statistical model considering the mixture distribution of probability and assuming that the statistical parameters are unknown; to determine control limits; and to compare the mixture chart with traditional control chart. Findings A graph was developed for monitoring a multi-stream process composed by some parameters considered in its calculation with similar efficiency to the traditional control chart. Originality/value The control chart can be an efficient tool for customers that receive product batches continuously from a supplier and need to monitor statistically the critical quality parameters.
7
Thepvongs, Somchart, and Brian M. Kleiner. "Inspection in Process Control." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 42, no. 16 (October 1998): 1170–74. http://dx.doi.org/10.1177/154193129804201619.
Анотація:
Consistent with the precepts of total quality control and total quality management, there has been a resource shift from incoming and outgoing inspection processes to statistical quality control of processes. Furthermore, process control operators are responsible for their own quality, necessitating the in-process inspection of components. This study treated the statistical process control task of “searching” control charts for out-of-control conditions as an inspection task and applied the Theory of Signal Detection to better understand this behavior and improve performance. Twelve subjects participated in a research study to examine how the portrayal of control chart information affected signal detection theory measures. The type of display did not have a significant effect on the sensitivity and response criterion of subjects. These results are discussed in terms of the applicability of Signal Detection Theory in control chart decision making as well as implications on display design.
8
Omar, M. Hafidz, Sheikh Y. Arafat, M. Pear Hossain, and Muhammad Riaz. "Inverse Maxwell Distribution and Statistical Process Control: An Efficient Approach for Monitoring Positively Skewed Process." Symmetry 13, no. 2 (January 25, 2021): 189. http://dx.doi.org/10.3390/sym13020189.
Анотація:
(1) Background: The literature discusses the inverse Maxwell distribution theoretically without application. Control charting is promising, but needs development for inverse Maxwell processes. (2) Methods: Thus, we develop the VIM control chart for monitoring the inverse Maxwell scale parameter and studied its statistical properties. The chart’s performance is evaluated using power curves and run length properties. (3) Results: Further, we use simulated data to compare the shift detection capability of our chart with Weibull, gamma, and lognormal charts. (4) Conclusion: The analysis demonstrates our chart’s efficiency for monitoring skewed processes. Finally, we apply our chart for monitoring real world lifetimes of car brake pads.
9
Gloi, Aime M., Vladimir Stankovich, Stanley Mayas, and Benjamin Rodriguez. "Statistical process control: machine performance check output variation." International Journal of Research in Medical Sciences 11, no. 7 (June 30, 2023): 2365–71. http://dx.doi.org/10.18203/2320-6012.ijrms20232072.
Анотація:
Background: The aim of this study was to illustrate and evaluate the use of different statistical process control (SPC) aspects to examine linear accelerator daily output variation through machine performance check (MPC) over a month. Methods: MPC daily output data were obtained over a month after AAPM TG-51 were performed. Baseline data were set, and subsequent data were conducted through SPC. The Shewhart chart was used to determine the upper and lower control limits, whereas CUSUM for subtle changes. Results: The upper and lower control limits obtained via SPC analysis of the MPC data were found to fall within AAPM Task Group 142 guidelines. MPC output variation data were within ±3% of their action limits values and were within 1% over thirty days of data. The process capability ratio and process acceptability ratio, Cp and Cpk values were ≥2 for all energies. Potential undetected deviations were captured by the CUSUM chart for photons and electrons beam energy. Conclusions: Control charts were found to be useful in terms of detecting changes in MPC output.
10
Niezgoda, Janusz. "The Use of Statistical Process Control Tools for Analysing Financial Statements." Folia Oeconomica Stetinensia 17, no. 1 (June 27, 2017): 129–37. http://dx.doi.org/10.1515/foli-2017-0010.
Анотація:
Abstract This article presents the proposed application of one type of the modified Shewhart control charts in the monitoring of changes in the aggregated level of financial ratios. The control chart x̅ has been used as a basis of analysis. The examined variable from the sample in the mentioned chart is the arithmetic mean. The author proposes to substitute it with a synthetic measure that is determined and based on the selected ratios. As the ratios mentioned above, are expressed in different units and characters, the author applies standardisation. The results of selected comparative analyses have been presented for both bankrupts and non-bankrupts. They indicate the possibility of using control charts as an auxiliary tool in financial analyses.
11
Shah, Samip, Pandya Shridhar, and Dipti Gohil. "Control chart : A statistical process control tool in pharmacy." Asian Journal of Pharmaceutics 4, no. 3 (2010): 184. http://dx.doi.org/10.4103/0973-8398.72116.
12
Desriawati, Riyani, Sutawanir Darwis, Nusar Hajarisman, Suliadi Suliadi, and Achmad Widodo. "Statistical Process Control Vibrasi Bearing untuk Identifikasi Degradasi." STATISTIKA Journal of Theoretical Statistics and Its Applications 20, no. 1 (September 24, 2020): 1–7. http://dx.doi.org/10.29313/jstat.v20i1.5298.
Анотація:
Statistical Process Control (SPC) is usually applied to the production process of goods, with the aim of detecting the quality of a production item that is within or beyond the specified specifications. In this study, SPC was applied to the bearing vibration signal to detect the first observable defect on a machine that functions as part of a prognostic tool for maintenance decision making. The detection of damage and prognostic are two important aspects in machine maintenance based on current conditions or better known as Condition (data) Based Maintenance (CBM). This paper discusses the shewhart average level chart and adaptive shewhart average level chart to detect the first observable defect. The shewhart chart is built with two assumptions, i.e. that the data must vary randomly around an established mean and follows a normal distribution. However, the adaptive Shewhart chart there is no need for normal assumption. The exploration of our data shows that the assumption of normality is not fulfilled, so that the Shewhart average level chart is not implemented. The adaptive Shewhart chart shows that the warning line for bearing 1 amounted to 5.547 and 3.631, for bearing 2 amounted to 5.491 and 3.635, for bearing 3 amounted to 5.762 and 3, 573, for bearing 4 of 5.604 and 33.615. The action line for bearing 1 is 6.026 and 3.152, for bearing 2 is 5.955 and 3.171, for bearing 3 is 6.309 and 3.026, for bearing 4 is 6.101 and 3.118. The first observable defect was t = 81 for bearing 1, t = 146 for bearing 2, t = 40 for bearing 3 and t = 61 for bearing 4. The adaptive Shewart chart can be used as a toll to estimate the initiation of transition state from normal to degenerate.
13
Wang, Hai Yu. "Multivariate Statistical Process Control with Multi-Dots Alarm Rules." Applied Mechanics and Materials 120 (October 2011): 275–79. http://dx.doi.org/10.4028/www.scientific.net/amm.120.275.
Анотація:
This paper mainly studied to building multivariate control charts of multi-dots alarm rules. For different multi-dots alarm rules, control limit parameters can be given by a kind of method of calculating average run length. Then the performances of those kinds of multivariate control schemes under different alarm rules were compared with Hotelling T2 chart, MCUSUM and MEWMA. We can find from this compare that those charts under different alarm rules have advantage in detecting small changes in the mean vector of a multivariate process. At last, an example is used to illustrate how this method can be used in practice.
14
Magalhães, Maysa Sacramento de, and Francisco Duarte Moura Neto. "Economic-statistical design of variable parameters non-central chi-square control chart." Production 21, no. 2 (June 17, 2011): 259–70. http://dx.doi.org/10.1590/s0103-65132011005000031.
Анотація:
Production processes are monitored by control charts since their inception by Shewhart (1924). This surveillance is useful in improving the production process due to increased stabilization of the process, and consequently standardization of the output. Control charts keep track of a few key quality characteristics of the outcome of the production process. This is done by means of univariate or multivariate charts. Small improvements in control chart methodology can have significant economic impact in the production process. In this investigation, we propose the monitoring of a single variable by means of a variable parameter non-central chi-square control chart. The design of the chart is accomplished by means of optimizing a cost function. We use here a simulated annealing optimization tool, due to the difficulty of classical gradient based optimization techniques to handle the optimization of the cost function. The results show some of the drawbacks of using this model.
15
Cossich, Vinicius, Marcio Antonio Vilas Boas, Naila Cristina Kepp, Allan Remor Lopes, and Dário Machado Júnior. "Statistical process control in pulsed drip irrigation systems." Ambiente e Agua - An Interdisciplinary Journal of Applied Science 18 (January 11, 2024): 1–19. http://dx.doi.org/10.4136/ambi-agua.2933.
Анотація:
Pulse drip application is a management option to prevent clogging and improve water-use efficiency. The measurement of distribution uniformity is opportune for pulse irrigation projects to ensure efficiency since the shorter the pulse time, the more negative influence the transition periods have. The objective of the present work was to monitor the quality of a drip irrigation system with different emitter models and different pulse times. The experiment was conducted under laboratory bench conditions, and 25 tests were performed for each pulse application (2, 3, 6 and 12 minutes) and emitter model (non-compensated and auto-compensated). Uniformity was determined using Christiansen's uniformity coefficient (CUC) and distribution uniformity coefficient (DUC) and monitored by Shewhart control charts and exponentially weighted moving average (EWMA). The decrease in pulse time caused a decrease in flow rate and uniformity only in drip irrigation systems with non auto-compensated emitters. However, this decrease is subtle, to the point that uniformity presented an excellent performance for all pulses tested. The use of the Shewhart and EWMA control charts were effective in monitoring the uniformity of the pulsed drip irrigation system. The Shewhart control chart was more robust in identifying isolated non-compensated uniformities, while the EWMA control chart was more sensitive in identifying unstable propensity. In the treatments with the non-compensated emitter, the statistical process control was able to identify that shorter pulse times resulted in greater application instability. As for the auto-compensated emitters, the statistical process control highlighted the concern with application uniformity after maintenance interference.
Keywords: anti-drain emitter, high-frequency irrigation, irrigation interval, statistical quality control.
16
Shamsuzzaman, Mohammad. "Optimization Design of 2-EWMA Control Chart Based on Random Process Shift." Applied Mechanics and Materials 465-466 (December 2013): 1185–90. http://dx.doi.org/10.4028/www.scientific.net/amm.465-466.1185.
Анотація:
The exponentially weighted moving average (EWMA) control charts are widely used for detecting process shifts of small and moderate sizes in Statistical Process Control (SPC).This article presents an algorithm for the optimization design of a multi-EWMA scheme comprising two EWMA control charts (known as 2-EWMA chart) considering random process shifts in mean. The random process shifts in mean is characterized by a Rayleigh distribution. The design algorithm optimizes the charting parameters of the 2-EWMA chart based on loss function. Comparative study shows that the optimal 2-EWMA chart outperforms the original 2-EWMA chart, as well as the original EWMA chart. In general, this article will help to enhance the detection effectiveness of the 2-EWMA chart, and facilitate its applications in SPC.
17
Xiao, Ying Zhe, and Ya Nan Huang. "Research on Statistical Process Control of Packaging & Printing Quality Management." Key Engineering Materials 467-469 (February 2011): 13–18. http://dx.doi.org/10.4028/www.scientific.net/kem.467-469.13.
Анотація:
This paper states not only the development course of quality management but also the actuality that the packaging & printing enterprise confronts. In addition, it explains the necessity of applying SPC. The first, it is discussed and studied the basic tool of SPC-control chart for statistical process. Based on this way, -R control chart is used to analyze and control the overprint precision. According to these control charts, the spot staffs can find the deficiencies in the quality control itself by finding the correlative process fluctuation and the slow variation in time. In addition, SPC provides objective bases for the quality management personnels to assess semi-products or products quality.
18
Yang, Siyuan F., and Wei-Ting K. Chien. "Effectiveness Indices for Statistical Process Control Chart Performance." ECS Transactions 27, no. 1 (December 17, 2019): 221–26. http://dx.doi.org/10.1149/1.3360623.
19
Abu-Shawiesh, Moustafa O., and Mokhtar B. Abdullah. "NEW ROBUST STATISTICAL PROCESS CONTROL CHART FOR LOCATION." Quality Engineering 12, no. 2 (September 1999): 149–59. http://dx.doi.org/10.1080/08982119908962572.
20
Zhang, Nien Fan. "A Statistical Control Chart for Stationary Process Data." Technometrics 40, no. 1 (February 1998): 24–38. http://dx.doi.org/10.1080/00401706.1998.10485479.
21
Klyachkin, Vladimir N., and Anastasiya V. Alekseeva. "OPTIMIZATION OF PARAMETERS OF GENERALIZED DISPERSION ALGORITHM AT STATISTICAL PROCESS CONTROL." Автоматизация процессов управления 3, no. 65 (2021): 41–47. http://dx.doi.org/10.35752/1991-2927-2021-3-65-41-47.
Анотація:
When monitoring a real production process using statistical methods, the question of early detection of violations arises. In most cases, several indicators are monitored simultaneously in the production process, and a change in the values of some indicators leads to a change in others. If there is a dependence of indicators for their monitoring, multivariate statistical control tools are used, in particular generalized variance chart. By varying the parameters of the chart, its efficiency can be significantly increased, this allows minimizing the time the process is in an unstable state.Applying the approach of A. Duncan, which he developed for Shewhart charts, a formula for the expectation of the duration of an unstable state of a process was obtained and a Python program was developed to minimize it. To test the set optimization problem, the calculation of the data of two process indicators is given and the optimal parameters of the generalized variance chart are obtained, at which the duration of the process in an unstable state is minimal.
22
Mayashari, Mayashari, Erna Tri Herdiani, and Anisa Anisa. "COMPARISON OF CONTROL CHART X ̅ BASED ON MEDIAN ABSOLUTE DEVIATION WITH S." BAREKENG: Jurnal Ilmu Matematika dan Terapan 18, no. 2 (May 25, 2024): 0737–50. http://dx.doi.org/10.30598/barekengvol18iss2pp0737-0750.
Анотація:
A stable and controlled process will produce products of good quality following predetermined specifications. A control chart is one of the statistical tools that can be used to measure the stability of a product process in a controlled state. Control charts commonly used to evaluate the statistical control process are Shewhart control charts ( and ). The control chart is used to control the process, as seen from the average and variability of the process. If the data used is not normally distributed or there are outliers, then an alternative control chart, namely the Median Absolute Deviation (MAD), can be used. MAD is used to monitor the process mean and process standard deviation because it has properties that are robust or resistant to deviations. This research aims to form a control chart based on MAD, apply it to data on fat content in animal feed products, and compare control charts based on with the control chart based on control chart based on MAD. The limitations in this study are the quality characteristics used consist of only one variable and the data is not normally distributed, only limited to the mean process, and the data used in this study are observation data on the fat content contained in animal feed products at PT Japfa Comfeed Indonesia Tbk Makassar Unit from December 2021 to January 2022. The results of this study show that the control chart based on MAD detects more out-of-control points than the control based on . The performance of the control chart based on MAD is better at detecting changes in the process than the control chart based on because it has a relatively smaller ARL value.
23
MUHAMMAD, SAAD, IDREES MUHAMMAD DAWOOD, ANSARI ARSALAN, SAMI ABDUL, RAUF MUHAMMAD, and JAMIL ATIF. "Reduction of non-conforming through statistical process control charts in textile industry." Industria Textila 73, no. 05 (October 26, 2022): 537–43. http://dx.doi.org/10.35530/it.073.05.202166.
Анотація:
The textile industry of Pakistan is a growing sector that contributes to the economy. Pakistan exports depend heavily upon textile goods. A minor defect in the finished good can cause a major loss of the export goods. Due to the involving the number of workers checking the product repeatedly can be very expensive therefore the quality engineering techniques of Statistical Control Charts are used in the textile industry. This research study aims at developing process control charts for the textile industry in Pakistan. For this purpose, the textile industry was taken into consideration. P-chart was developed to monitor the variation in the process with a Six Sigma standard deviation. The collection of data was for six months from various departments of the textile industry. The attribute data were collected for the analysis from 4 different units of the industry. The construction of the P-Chart includes the Control Limits (CL), Upper Control Limits (UCL), 3 sigma deviations from the mean Control Limit (CL), Lower Control Limits (LCL), –3 sigma deviation from the mean Control Limit (CL). The result showed that the processes of the production units were under control, however, the mean was not centred which was due to some common cause of the process which is acceptable. The P-chart can serve as a standard for the new process to be developed.
24
Alves, Custodio Da Cunha, Andréa Cristina Konrath, Elisa Henning, Olga Maria Formigoni Carvalho Walter, Edson Pacheco Paladini, Teresa A. Oliveira, and Amílcar Oliveira. "The Mixed CUSUM-EWMA (MCE) control chart as a new alternative in the monitoring of a manufacturing process." Brazilian Journal of Operations & Production Management 16, no. 1 (January 21, 2019): 1–13. http://dx.doi.org/10.14488/bjopm.2019.v16.n1.a1.
Анотація:
Goal: The objective is to conclude, based on a comparative study, if there is a significant difference in sensitivity between the application of MCE and the individual application of the CUSUM or EWMA chart, i.e., greater sensitivity particularly for cases of lesser magnitude of change.
Design/Methodology/Approach: These are an applied research and statistical techniques such as statistical control charts are used for monitoring variability. Results: The results show that the MCE chart signals a process out of statistical control, while individual EWMA and CUSUM charts does not detect any situation out of statistical control for the data analyzed.
Limitations: This article is dedicated to measurable variables and individual analysis of quality characteristics, without investing in attribute variables. The MCE chart was applied to items that are essential to the productive process development being analysed.
Practical Implications: The practical implications of this study can contribute to: the correct choice of more sensitive control charts to detect mainly small changes in the location (mean) of processes; provide clear and accurate information about the fundamental procedures for the implementation of statistical quality control; and encourage the use of this quality improvement tool.
Originality/Value: The MCE control chart is a great differential for the improvement of the quality process of the studied company because it goes beyond what CUSUM and EWMA control charts can identify in terms of variability.
25
Pham, D. T., and E. Oztemel. "An Integrated Neural Network and Expert System Tool for Statistical Process Control." Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 209, no. 2 (February 1995): 91–97. http://dx.doi.org/10.1243/pime_proc_1995_209_060_02.
Анотація:
Control charts are a basic means for monitoring the quality characteristics of a manufacturing process to ensure the required quality level. They are used to track product and process variations through graphical representation of the quality variable of interest. A control chart shows the state of control of a process and can exhibit different types of patterns which are indicative of long-term trends in it. This paper describes the integration of an expert system and a neural-network-based pattern recognizer for analysing and interpreting control charts. The expert system has an on-line process monitoring package to detect general out-of-control situations and a diagnosis module to suggest corrective actions. The pattern recognizer is an on-line system comprising two neural networks and an heuristics module designed to identify incipient process abnormalities from control chart patterns. The paper also compares neural networks and expert systems and provides the rationale for the integration process.
26
Rojas-Preciado, Wilson, Mauricio Rojas-Campuzano, Purificación Galindo-Villardón, and Omar Ruiz-Barzola. "Control Chart T2Qv for Statistical Control of Multivariate Processes with Qualitative Variables." Mathematics 11, no. 12 (June 6, 2023): 2595. http://dx.doi.org/10.3390/math11122595.
Анотація:
The scientific literature is abundant regarding control charts in multivariate environments for numerical and mixed data; however, there are few publications for qualitative data. Qualitative variables provide valuable information on processes in various industrial, productive, technological, and health contexts. Social processes are no exception. There are multiple nominal and ordinal categorical variables used in economics, psychology, law, sociology, and education, whose analysis adds value to decision-making; therefore, their representation in control charts would be useful. When there are many variables, there is a risk of redundant or excessive information, so the application of multivariate methods for dimension reduction to retain a few latent variables, i.e., a recombination of the original and synthesizing of most of the information, is viable. In this context, the T2Qv control chart is presented as a multivariate statistical process control technique that performs an analysis of qualitative data through Multiple Correspondence Analysis (MCA), and the Hotelling T2 chart. The interpretation of out-of-control points is carried out by comparing MCA charts and analyzing the χ2 distance between the categories of the concatenated table and those that represent out-of-control points. Sensitivity analysis determined that the T2Qv control chart performs well when working with high dimensions. To test the methodology, an analysis was performed with simulated data and with a real case applied to the graduate follow-up process in the context of higher education. To facilitate the dissemination and application of the proposal, a reproducible computational package was developed in R, called T2Qv, and is available on the Comprehensive R Archive Network (CRAN).
27
Maqsood, Mediha, Aamir Sanaullah, Yasar Mahmood, Afrah Yahya Al-Rezami, and Manal Z. M. Abdalla. "Efficient control chart-based monitoring of scale parameter for a process with heavy-tailed non-normal distribution." AIMS Mathematics 8, no. 12 (2023): 30075–101. http://dx.doi.org/10.3934/math.20231538.
Анотація:
<abstract> <p>Statistical process control is a procedure of quality control that is widely used in industrial processes to enable monitoring by using statistical techniques. All production processes are faced with natural and unnatural variations. To maintain the stability of the production process and reduce variation, different tools are used. Control charts are significant tools to monitor a production process. In this article, we design an extended exponentially weighted moving average (EEWMA) chart under the assumption of inverse Maxwell (IM) distribution, an IM EEWMA (IMEEWMA) control chart. We have estimated the performance of the proposed chart in terms of various run-length (RL) properties, including the average RL, standard deviation of the RL and median RL. We have also carried out a comparative analysis of the proposed chart with the existing Shewhart-type chart for IM distribution (VIM chart) and IM exponential weighted moving average (IMEWMA) chart. We observed that the proposed IMEEWMA chart performed better than the VIM chart and IMEWMA chart in terms of the ability to detect small and moderate shifts. To demonstrate its practical application, we have applied the IMEEWMA chart, along with existing control charts, to monitor the lifetime of car brake pad data. This real-world example illustrates the superiority of the IMEEWMA chart over its counterparts in industrial scenarios.</p> </abstract>
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Agustin, Annisa, and Anton Mulyono Azis. "ANALISIS PENGENDALIAN KUALITAS PRODUK MIE DENGAN METODE STATISTICAL PROCESS CONTROL." ANALISIS 14, no. 01 (March 1, 2024): 16–32. http://dx.doi.org/10.37478/als.v14i01.3203.
Анотація:
CV Manunggal Jaya is a company engaged in food production in Bandung. The company, which produces noodles experiences product defects that exceed the company's tolerance limit. The purpose of this study is to determine quality control, factors that cause product defects, and the application of the Statistical Process Control (SPC) method. This research method is a descriptive quantitative method with data collection using interviews, observation and documentation. Data processing is done using tools such as flowchart, fishbone diagram, check sheet, histogram, control chart (p-chart) and pareto diagram. Based on the research results that there are 3 (three) quality controls, namely: Quality control of raw materials, production processes, finished products. Product defects are caused by human factors, raw materials, machines, and methods. There are three types of defects that occur at CV Manunggal Jaya Bandung, namely size defects, texture defects and dirty dough. Based on the results of the control chart (p-chart), it is known that the CL is 0.0081, UCL is 0.00961, LCL is 0.00668. The results of the analysis using the Pareto diagram tool state that the priority of improvements that must be made by the company is on texture defects.
29
Sensiani, Sensiani, Tatik Widiharih, and Rita Rahmawati. "GRAFIK PENGENDALI MULTIVARIATE EXPONENTIALLY WEIGHTED MOVING COVARIANCE MATRIX (MEWMC) PADA DATA SAMPEL ZAT KANDUNGAN BATU BARA (Studi Kasus : PT Bukit Asam (Persero) Tbk. Tahun 2016)." Jurnal Gaussian 9, no. 1 (February 28, 2020): 1–15. http://dx.doi.org/10.14710/j.gauss.v9i1.27517.
Анотація:
The progress of industrial business in the midst of global competition increased rapidly. A businessman should have special treatment for their products to compete of market quality. The quality of product is an important factor in choosing a product or service, particularly for the costumers. In technological development, the factors of failure in the product can be minimized by Statistical Quality Control. Besides to reducing diversity in product characteristics, statistical quality control can increase business income. The data source of this research is sekunder sample data of coal products of PT Bukit Asam (Persero) Tbk. with seven variables, the variables is Total Moisture (TM), Inherent Moisture (IM), Ash Content (ASH), Volatile Matter (VM), Fixed Carbon (FC), Total Sulfur (TS), and Calorific Value (CV). The analytical method is the controlling chart of Multivariate Exponentially Weighted Moving Covariance Matrix (MEWMC) which is one of the multivariate charts that serves to detect small shift in covariance matrix and the development of Multivariate Exponentially Weighted Moving Average (MEWMA) charts. Based on the results of the analysis, the MEWMA control chart is statistically controlled with a weighting value λ=0,2 while the MEWMC chart with λ=0,2 is not controlled statistically and detected small shift in covariance matrix . In a controlled process, the capability value of multivariate process is 0,83222 < 1 which means the process is not capable.Keywords: MEWMA control chart, MEWMC control chart, Process capability analysis.
30
Noskievičová, Darja. "Complex Control Chart Interpretation." International Journal of Engineering Business Management 5 (January 1, 2013): 13. http://dx.doi.org/10.5772/56441.
Анотація:
Identification of the assignable causes of process variability and the restriction and elimination of their influence are the main goals of statistical process control (SPC). Identification of these causes is associated with so called tests for special causes or runs tests. From the time of the formulation of the first set of such rules (Western Electric rules) several different sets have been created (Nelson rules, Boeing AQS rules, Trietsch rules). This paper deals with the comparison analysis of these sets of rules, their basic statistical properties and the mistakes accompanying their application using SW support. At the end of this paper some recommendations for the correct application of the runs tests are formulated.
31
Huang, Wei-Heng, and Arthur B. Yeh. "A Nonparametric Phase I Control Chart for Monitoring the Process Variability with Individual Observations Based on Empirical Likelihood Ratio." International Journal of Reliability, Quality and Safety Engineering 25, no. 03 (April 23, 2018): 1850015. http://dx.doi.org/10.1142/s0218539318500158.
Анотація:
Among the statistical process control (SPC) techniques, the control chart has been proven to be effective in process monitoring. The Shewhart chart is one of the most commonly used control charts for monitoring the process mean and variability based on the assumption that the distribution of the quality characteristic is normal. However, in practice, many quality characteristics are not normally distributed. Most of the existing nonparametric control charts are designed for Phase II monitoring. Little has been done in developing the nonparametric Phase I control charts especially for individual observations. In this work, we propose a new nonparametric Phase I control chart for monitoring the scale parameter based on the empirical likelihood ratio test. The simulation results show that the proposed chart is more effective than the existing charts in terms of signal probability. A real example is used to demonstrate how the proposed chart can be applied in practice.
32
Sufinah Dahari, Muzalwana Abdul Talib, and Adilah Abdul Ghapor. "Robust Control Chart Application in Semiconductor Manufacturing Process." Journal of Advanced Research in Applied Sciences and Engineering Technology 43, no. 2 (April 17, 2024): 203–19. http://dx.doi.org/10.37934/araset.43.2.203219.
Анотація:
Statistical Process Control (SPC) charts are frequently used in the semiconductor manufacturing environment to monitor process quality and detect special-cause variations, hence, to take corrective actions when necessary. The important aspects of control charts to consider on production floors are identifying the primary objective of implementing control charts, the type of data to monitor and the most appropriate control limits to establish. When the quality data is a type of attribute data like the proportion of defectives from a production lot, a p-chart approach is most suitable. In p-chart applications, although the assumption of normally distributed process data is not mandatory, the widespread practice is to assume the normal distribution of process data when establishing the control limits. Yet again, the reality of industrial settings is that process data are most likely influenced by outliers, resulting in highly skewed distributions. This paper addresses these issues by proposing robust SPC charting techniques to detect special-cause variations in the semiconductor manufacturing processes. Here, we present a case study of a semiconductor company in Malaysia, Dominant Opto Technologies Sdn. Bhd. to propose three robust statistical approaches for monitoring the proportion of defectives in production lots. We apply M-estimates, median, and interquartile range to calculate the upper control limits (UCL) and found that robust estimators are more effective in detecting early process deterioration and capturing the out-of-control (OOC) conditions better than traditional control charts. By proposing robust methods, this study enlightens the practical aspects of process quality improvement for real-life manufacturing setups. Because a high OOC rate may impact manufacturing productivity, we recommend the decision-makers choose the types of control charts based on the implications of each robust approach toward quality and productivity. The significance of this study includes providing insights into setting up the appropriate attribute control charts for detecting defective proportions for professionals and SPC researchers working in these areas.
33
Jorani, Rasheed Majeed, Maroua Haddar, Fakher Chaari, and Mohamed Haddar. "Gear Crack Detection Based on Vibration Analysis Techniques and Statistical Process Control Charts (SPCC)." Machines 11, no. 2 (February 20, 2023): 312. http://dx.doi.org/10.3390/machines11020312.
Анотація:
Vibration condition monitoring is a non-devastating technique that can be performed to detect tooth cracks propagating in gear systems. This paper proposes to apply a new methodology using time-domain analysis, frequency-domain analysis, and statistical process control charts (SPCC) for gear crack detection of a 10 DOF dynamic model of spiral bevel gear system (SBGS). The gear mesh stiffness effect used in the model has been studied analytically for different levels of crack faults. Adding Gaussian white noise is discussed as the first step to simulating the initial modeling signals of real-world conditions. Second, time-domain signal analysis was performed to identify periodic vibration pulses as failure components and calculate the statistical standard deviation (STD) feature as a fault-sensitive feature. Third, a fast Fourier transform (FFT) to time signals of the variable gear mesh stiffness was applied to determine the gear mesh frequency and sidebands to detect tooth cracks. Fourth, the SPCC was designed using the Shewhart X-bar chart and an exponentially weighted moving average (EWMA) chart based on the STD feature of the healthy gears. Finally, in the testing stage, the control charts are carried out with simulation signals under faulty conditions to detect the different levels of cracks. The results showed that the EWMA chart outperformed the time domain analysis, frequency domain analysis, and Shewhart X-bar chart in detecting all levels of cracks at an early stage.
34
Chaudhary, Aamir Majeed, Aamir Sanaullah, Muhammad Hanif, Mohammad M. A. Almazah, Nafisa A. Albasheir, and Fuad S. Al-Duais. "Efficient Monitoring of a Parameter of Non-Normal Process Using a Robust Efficient Control Chart: A Comparative Study." Mathematics 11, no. 19 (October 3, 2023): 4157. http://dx.doi.org/10.3390/math11194157.
Анотація:
The control chart is a fundamental tool in statistical process control (SPC), widely employed in manufacturing and construction industries for process monitoring with the primary objective of maintaining quality standards and improving operational efficiency. Control charts play a crucial role in identifying special cause variations and guiding the process back to statistical control. While Shewhart control charts excel at detecting significant shifts, EWMA and CUSUM charts are better suited for detecting smaller to moderate shifts. However, the effectiveness of all these control charts is compromised when the underlying distribution deviates from normality. In response to this challenge, this study introduces a robust mixed EWMA-CUSUM control chart tailored for monitoring processes characterized via symmetric but non-normal distributions. The key innovation of the proposed approach lies in the integration of a robust estimator, based on order statistics, that leverages the generalized least square (GLS) technique developed by Lloyd. This integration enhances the chart’s robustness and minimizes estimator variance, even in the presence of non-normality. To demonstrate the effectiveness of the proposed control chart, a comprehensive comparison is conducted with several well-known control charts. Results of the study clearly show that the proposed chart exhibits superior sensitivity to small and moderate shifts in process parameters when compared to its predecessors. Through a compelling illustrative example, a real-life application of the enhanced performance of the proposed control chart is provided in comparison to existing alternatives.
35
عمران, سلمان حسين. "Statistical Quality Control of Industrial Products at the General Company for Vegetable Oils." Journal of Engineering 18, no. 06 (June 1, 2012): 135–49. http://dx.doi.org/10.31026/j.eng.2012.06.08.
Анотація:
This research includes the using of statistical to improve the quality of can plastics which is produced at the state company for Vegetable oils (Almaamon factory ) by using the percentage defective control chart ( p-chart ) of a fixed sample. A sample of size (450) cans daily for (30) days was selected to determine the rejected product . Operations research with a (win QSB ) package for ( p-chart ) was used to determine test quality level required for product specification to justify that the process that is statistically controlled.The results show high degree of accuracy by using the program and the mathematical operations (primary and secondary ) which used to draw the control limits charts and to reject the statistically uncontrolled samples . Moreover a final chart was drawn to be used in the factory .The research shows improvement of the can product by percentage (0.06 % ), product defects percentage was lowered from ( 0.53 % to 0.47 % ) for the production process which becomes statistically controlled .Also it was found that it was within Iraqi specification (1093).
36
Runger, George C., Frank B. Alt, and Douglas C. Montgomery. "Contributors to a multivariate statistical process control chart signal." Communications in Statistics - Theory and Methods 25, no. 10 (January 1996): 2203–13. http://dx.doi.org/10.1080/03610929608831832.
37
Ng, Jun Jie. "Statistical process control chart as a project management tool." IEEE Engineering Management Review 46, no. 2 (June 1, 2018): 26–28. http://dx.doi.org/10.1109/emr.2018.2834379.
38
Noskievicova, Darja. "APSS – Software Support for Decision Making in Statistical Process Control." Quality Innovation Prosperity 22, no. 3 (November 30, 2018): 19. http://dx.doi.org/10.12776/qip.v22i3.1141.
Анотація:
<p><strong>Purpose:</strong> SPC can be defined as the problem solving process incorporating many separate decisions including selection of the control chart based on the verification of the data presumptions. There is no professional statistical software which enables to make such decisions in a complex way.</p><p><strong>Methodology/Approach:</strong> There are many excellent professional statistical programs but without complex methodology for selection of the best control chart. Proposed program in Excel APSS (Analysis of the Process Statistical Stability) solves this problem and also offers additional learning functions.</p><p><strong>Findings:</strong> The created SW enables to link altogether separate functions of selected professional statistical programs (data presumption verification, control charts construction and interpretation) and supports active learning in this field.</p><p><strong>Research Limitation/implication: </strong>The proposed SW can be applied to control charts covered by SW Statgraphics Centurion and Minitab. But there is no problem to modify it for other professional statistical SW.</p><strong>Originality/Value of paper: </strong>The paper prezents the original SW created in the frame of the research activities at the Department of Quality Management of FMT, VŠB-TUO, Czech Republic. SW enables to link altogether separate functions of the professional statistical SW needed for the complex realization of statitical process control and it is very strong tool for the active learning of statistical process control tasks.
39
Elevli, Sermin, Nevin Uzgören, Deniz Bingöl, and Birol Elevli. "Drinking water quality control: control charts for turbidity and pH." Journal of Water, Sanitation and Hygiene for Development 6, no. 4 (September 26, 2016): 511–18. http://dx.doi.org/10.2166/washdev.2016.016.
Анотація:
Water treatment processes are required to be in statistical control and capable of meeting drinking water specifications. Control charts are used to monitor the stability of quality parameters by distinguishing the in-control and out-of-control states. The basic assumption in standard applications of control charts is that observed data from the process are independent and identically distributed. However, the independence assumption is often violated in chemical processes such as water treatment. Autocorrelation, a measure of dependency, is a correlation between members of a series arranged in time. The residuals obtained from an autoregressive integrated moving averages (ARIMA) time series model plotted on a standard control chart is used to overcome the misleading of standard control charts in the case of autocorrelation. In this study, a special cause control (SCC) chart, also called a chart of residuals from the fitted ARIMA model, has been used for turbidity and pH data from a drinking water treatment plant in Samsun, Turkey. ARIMA (3,1,0) for turbidity and ARIMA (1,1,1) for pH were determined as the best time series models to remove autocorrelation. The results showed that the SCC chart is more appropriate for autocorrelated data to evaluate the stability of the water treatment process, since it provides a higher probability of coverage than an individual control chart.
40
Feltz, Carol J., and Jyh-Jen Horng Shiau. "Statistical process monitoring using an empirical Bayes multivariate process control chart." Quality and Reliability Engineering International 17, no. 2 (2001): 119–24. http://dx.doi.org/10.1002/qre.393.
41
Resti, Aulia, Tatik Widiharih, and Rukun Santoso. "GRAFIK PENGENDALI MIXED EXPONENTIALLY WEIGHTED MOVING AVERAGE – CUMULATIVE SUM (MEC) DALAM ANALISIS PENGAWASAN PROSES PRODUKSI (Studi Kasus : Wingko Babat Cap “Moel”)." Jurnal Gaussian 10, no. 1 (February 28, 2021): 114–24. http://dx.doi.org/10.14710/j.gauss.v10i1.30938.
Анотація:
Quality control is an important role in industry for maintain quality stability. Statistical process control can quickly investigate the occurrence of unforeseen causes or process shifts using control charts. Mixed Exponentially Weighted Moving Average - Cumulative Sum (MEC) control chart is a tool used to monitor and evaluate whether the production process is in control or not. The MEC control chart method is a combination of the Exponentially Weighted Moving Average (EWMA) and Cumulative Sum (CUSUM) charts. Combining the two charts aims to increase the sensitivity of the control chart in detecting out of control. To compare the sensitivity level of the EWMA, CUSUM, and MEC methods, the Average Run Length (ARL) was used. From the comparison of ARL values, the MEC chart is the most sensitive control chart in detecting out of control compared to EWMA and CUSUM charts for small shifts. Keywords: Grafik Pengendali, Exponentially Weighted Moving Average, Cumulative Sum, Mixed EWMA-CUSUM, Average Run Lenght, EWMA, CUSUM, MEC, ARL
42
Tomak, Leman, Yuksel Bek, and Yılmaz Tomak. "To Compare Time-Weighted Graphs to Evaluate the Inclination of the Acetabular Component of Patients Who Had Total Hip Replacement Surgery." BioMed Research International 2015 (2015): 1–6. http://dx.doi.org/10.1155/2015/129610.
Анотація:
Time-weighted graphs are used to detect small shifts in statistical process control. The aim of this study is to evaluate the inclination of the acetabular component with CUmulative SUM (CUSUM) chart, Moving Average (MA) chart, and Exponentially Weighted Moving Average (EWMA) chart. The data were obtained directly from thirty patients who had undergone total hip replacement surgery at Ondokuz Mayis University, Faculty of Medicine. The inclination of the acetabular component of these people, after total hip replacement, was evaluated. CUSUM chart, Moving Average chart, and Exponentially Weighted Moving Average were used to evaluate the quality control process of acetabular component inclination. MINITAB Statistical Software 15.0 was used to generate these control charts. The assessment done with time-weighted charts revealed that the acetabular inclination angles were settled within control limits and the process was under control. It was determined that the change within the control limits had a random pattern. As a result of this study it has been obtained that time-weighted quality control charts which are used mostly in the field of industry can also be used in the field of medicine. It has provided us with a faster visual decision.
43
Pimenta, Cristie Diego, Messias Borges Silva, Fernando Augusto Silva Marins, and Aneirson Francisco da Silva. "Application of Statistical Monitoring Using Autocorrelated Data and With the Influence of Multicollinearity in a Steel Process." International Journal of Statistics and Probability 10, no. 4 (June 22, 2021): 96. http://dx.doi.org/10.5539/ijsp.v10n4p96.
Анотація:
The purpose of this article is to demonstrate a practical application of control charts in an industrial process that has data auto-correlated with each other. Although the control charts created by Walter A. Shewhart are very effective in monitoring processes, there are very important statistical assumptions for Shewhart's control charts to be applied correctly. Choosing the correct Control Chart is essential for managers to be able to make coherent decisions within companies. With this study, it was possible to demonstrate that the original data collected in the process, which at first appeared to have many special causes of variation, was actually a stable process (no anomalies present). However, this finding required the use of autoregressive models, multivariate statistics, autocorrelation and normality tests, multicollinearity analysis and the use of the EWMA control chart. It was concluded that it is of fundamental importance to choose the appropriate control chart for monitoring industrial processes, to ensure that changes in processes do not incorporate non-existent variations and do not cause an increase in the number of defective products.
44
Seoh, Yee Kam, Voon Hee Wong, and Mahboobeh Zangeneh Sirdari. "A study on the application of control chart in healthcare." ITM Web of Conferences 36 (2021): 01001. http://dx.doi.org/10.1051/itmconf/20213601001.
Анотація:
The most concerning issues in the healthcare system will always be quality control and quality improvement as they are significant to the health condition of the patient. A quality statistical tool such as statistical process control (SPC) charts will be efficient and highly effective in reducing the sources of variation within the healthcare process and in monitoring or controlling improvement of the process. The control chart is a statistical process control methodology designed to evaluate the process improvement or change in the manufacturing industry and is being implemented gradually in the healthcare sector. This will enable healthcare organizations to prevent unnecessary investment or spending in any changes that sound good but do not have any positive impact on real progress or improvement. When there is greater participation of humans in healthcare, the risks of error are also greater. Control charts help determine the source of error by differentiating the common and special cause of variation, each requiring a different response from healthcare management. This paper intends to deliver an overview of SPC theory and to explore the application of SPC charts by presenting a few examples of the implementation of control charts to common issues in the healthcare sector. After a brief overview of SPC in healthcare, the selection and construction of the two widely used control charts (Individuals and Moving Range chart, U chart) were adopted and illustrated by using the example from healthcare.
45
Chen, Li-Pang, Syamsiyatul Muzayyanah, SU-FEN YANG, Bin Wang, Ting-An Jiang, and Shengjin Gan. "MONITORING PROCESS LOCATION AND DISPERSION SIMULTANEOUSLY USING A CONTROL REGION." DYNA 97, no. 1 (January 1, 2022): 71–78. http://dx.doi.org/10.6036/10115.
Анотація:
Control charts are effective tools for detecting out-of-control conditions of process parameters in manufacturing and service industries. The development of distribution-free control charts is important in statistical process control when the process quality variable follows an unknown or a non-normal distribution. This research thus proposes to use a distribution-free technology to establish a new control region based on the exponentially weighted moving average median statistic and exponentially weighted moving average interquartile range statistic for simultaneously monitoring the process location and dispersion and further sets up a corresponding new control chart. We compute the out-of-control average run length to evaluate out-of-control detection performance of the proposed control region and also compare the proposed control region with some existing location and dispersion control charts. Results show that our proposed chart always exhibits superior detection performance when the shifts in process location and/or dispersion are small or moderate. The new control region is thus recommended. Keywords: control chart, distribution-free, dispersion and location, EWMA, kernel control region, kernel density estimation.
46
CASTAGLIOLA, PHILIPPE, GIOVANNI CELANO, SERGIO FICHERA, and VALERIA NUNNARI. "A VARIABLE SAMPLE SIZE S2-EWMA CONTROL CHART FOR MONITORING THE PROCESS VARIANCE." International Journal of Reliability, Quality and Safety Engineering 15, no. 03 (June 2008): 181–201. http://dx.doi.org/10.1142/s0218539308003039.
Анотація:
Monitoring the stability of measures dispersion from a process quality parameter is an important aspect of Statistical Process Control which should be carefully planned by practitioners. To perform this task, this paper proposes an adaptive EWMA chart as a practical and efficient tool. The developed EWMA chart is the Variable Sample Size (VSS) version of a static S2-EWMA control chart previously developed by one of the authors to monitor the sample variance. The way to compute the design parameters of this VSS S2-EWMA control chart is discussed and an optimal design strategy based on the Average Time to Signal (ATS) after a shift in process dispersion is presented. The statistical performance of the VSS S2-EWMA has been evaluated by means of a comparison with two other EWMA charts: the static S2-EWMA and the adaptive (VSI) S2-EWMA allowing to vary the sampling intervals. The obtained results show how the possibility of varying the sample size significantly improves the statistical performance over the static S2-EWMA; furthermore, some interesting findings suggest to implement the VSS S2-EWMA with respect to the VSI S2-EWMA when some particular process operating conditions occur.
47
Li, Ying Ji, and Wei Xi Ji. "Statistical Process Control Technology Applied in ERP System." Advanced Materials Research 421 (December 2011): 461–64. http://dx.doi.org/10.4028/www.scientific.net/amr.421.461.
Анотація:
For the high and strict quality requirement in the manufacturing process of nuclear power parts, this paper is based on the combination of Statistical Process Control technology and the ERP quality management and control the production quality based on the control chart. PowerBuilder 9.0 and SQL Server2000 were used to design and develop the system while PowerBuilder 9.0 as front-end development tool and SQL Server2000 as back-end DBMS respectively. Firstly, collect the quality data of the production process (some important processes). Then, analysis these data and form control chart. Real-time monitor production process by the control charting to ensure the process is stability. Organic combination of SPC and ERP to improve and control the quality, not only enrich the analytical data of SPC, but also make up the ERP data to analysis and control quality data.
48
Hu, Xuelong, Guan Sun, Fupeng Xie, and Anan Tang. "Monitoring the Ratio of Two Normal Variables Based on Triple Exponentially Weighted Moving Average Control Charts with Fixed and Variable Sampling Intervals." Symmetry 14, no. 6 (June 14, 2022): 1236. http://dx.doi.org/10.3390/sym14061236.
Анотація:
In statistical process control (SPC), the ratio of two normal random variables (RZ) is a valuable statistical indicator to be taken as the charting statistic. In this work, we propose a triple exponentially weighted moving average (TEWMA) chart for monitoring the RZ. Additionally, the variable sampling interval (VSI) strategy has been adopted to different control charts by researchers. With the application of this strategy, the VSI-TEWMA-RZ chart is then developed to further improve the performance of the proposed TEWMA-RZ chart. The run length (RL) properties of the proposed TEWMA-RZ and VSI-TEWMA-RZ charts are obtained by the widely used Monte-Carlo (MC) simulations. Through the comparisons with the VSI-EWMA-RZ and the VSI-DEWMA-RZ charts, the VSI-TEWMA-RZ chart is statistically more sensitive than the VSI-EWMA-RZ and the VSI-DEWMA-RZ charts in detecting small and moderate shifts. Moreover, it turned out that the VSI-TEWMA-RZ chart has better performance than the TEWMA-RZ chart on the whole. Furthermore, this paper illustrates the implementation of the proposed charts with an example from the food industry.
49
Kotegova, K. A., A. D. Zaberezhniy, L. A. Neminushchaya, T. A. Skotnikovа, V. I. Eremets, E. V. Markova, S. A. Grin, and V. M. Popova. "Stability assessment of immunobiological medicinal products manufacturing for veterinary use with Shewhart control charts." Вестник российской сельскохозяйственной науки, no. 5 (December 15, 2023): 78–82. http://dx.doi.org/10.31857/2500-2082/2023/5/78-82.
Анотація:
According to the current Rules of Good Manufacturing Practice of the Eurasian Economic Union, the principles of risk management for product quality must be applied to all aspects of the production and use of medicines for medical and veterinary purposes. The recommended quality risk management method used by regulators is the Shewhart Chart. Control charts, proposed in 1924 by W. Shewhart, are a graphical tool for applying statistical principles to process control. Statistical process control is a methodology for establishing and maintaining production at an acceptable and stable level, providing the required product quality. Control charts are used to collect data during the continuous recording of the characteristics of the quality of manufactured products. As a result of continuous analysis of information, control charts help to identify unusual patterns of data variation and take preventive measures to eliminate them and increase process stability. The use of Shewhart maps leads to a more detailed understanding of the process and helps to discover ways for valuable improvements. The purpose of the work is to assess the stability of the production of immunobiological medicinal products for veterinary use using Shewhart's control charts. The immunobiological drug "Oralrabivak" produced at the Schelkovo Biokombinat FKP was used as a model object. We used control charts of individual values (X-chart) and moving ranges (Rm-chart) and maps of mean values (-chart) and sample standard deviations (s-chart). The assessment of the stability of the production of Oralrabivak using Shewhart's control charts showed that during two production cycles (2021 and 2022) the technological process was in a state of statistical controllability. To improve the process and ensure the quality of the finished product, the specialists of the enterprise have taken preventive measures. The methodology of control charts made it possible to visualize and study in detail a new production process for the enterprise for a deeper understanding of its features by the specialists of the enterprise and further improvement.
50
Mohd Razali, Nur Hidayah, Lazim Abdullah, Zabidin Salleh, Ahmad Termimi Ab Ghani, and Bee Wah Yap. "Interval Type-2 Fuzzy Standardized Cumulative Sum Control Charts in Production of Fertilizers." Mathematical Problems in Engineering 2021 (October 11, 2021): 1–20. http://dx.doi.org/10.1155/2021/4159149.
Анотація:
Statistical process control is a method used for controlling processes in which causes of variations and correction actions can be observed. Control chart is one of the powerful tools of statistical process control that are used to control nonconforming products. Previous literature suggests that fuzzy charts are more sensitive than conventional control charts, and hence, they provide better quality and conformance of products. Nevertheless, some of the data used are more suitable to be presented in interval type-2 fuzzy numbers compared to type-1 fuzzy numbers as interval type-2 fuzzy numbers have more ability to capture uncertain and vague information. In this paper, we develop an interval type-2 fuzzy standardized cumulative sum (IT2F-SCUSUM) control chart and apply it to data of fertilizer production. This new approach combines the advantages of interval type-2 fuzzy numbers and standardized sample means which can control the variability. Twenty samples with a sample size of six were examined for testing the conformance. The proposed IT2F-SCUSUM control chart unveils that 15 samples are “out of control.” The results are also compared to the conventional CUSUM chart and type-1 fuzzy CUSUM chart. The conventional chart shows that 13 samples are “out of control.” In contrast, the type-1 fuzzy CUSUM chart shows that the process is “out of control” for 14 samples. In the analysis of average run length, the proposed IT2F-SCUSUM chart outperforms the other two CUSUM charts. Thus, we can conclude that the IT2F-SCUSUM chart is more sensitive and takes lesser number of observations to identify the shift in the process. The analyses suggest that the IT2F-SCUSUM chart is a promising tool in examining conformance of the quality of the fertilizer production.