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Delivery: <= 5 days. True-PDF full-copy in English will be manually translated and delivered via email. GB/T 17989.6-2022: Statistical method of quality control in production process - Control charts - Part 6: EWMA control charts Status: Valid
Basic dataStandard ID: GB/T 17989.6-2022 (GB/T17989.6-2022)Description (Translated English): Statistical method of quality control in production process - Control charts - Part 6: EWMA control charts Sector / Industry: National Standard (Recommended) Classification of Chinese Standard: A41 Word Count Estimation: 31,357 Issuing agency(ies): State Administration for Market Regulation, China National Standardization Administration GB/T 17989.6-2022: Statistical method of quality control in production process - Control charts - Part 6: EWMA control charts---This is a DRAFT version for illustration, not a final translation. Full copy of true-PDF in English version (including equations, symbols, images, flow-chart, tables, and figures etc.) will be manually/carefully translated upon your order. Statistical method of quality control in production process - Control charts - Part 6.EWMA control charts ICS 03.120.30 CCSA41 National Standards of People's Republic of China Production process quality control statistical method control chart Part 6.Exponentially Weighted Moving Average Control Chart Controlcharts-Part 6.EWMAcontrolcharts Published on 2022-03-09 2022-10-01 Implementation State Administration for Market Regulation Released by the National Standardization Administration directory Preface I Introduction III 1 Scope 1 2 Normative references 1 3 Terms and Definitions, Symbols, Abbreviations 1 3.1 Terms and Definitions 1 3.2 Symbol 1 3.3 Abbreviations 2 4 EWMA 2 for metering data 4.1 Overview 2 4.2 Understanding of Weighted Average 3 4.3 Control Limits for EWMA Charts 3 4.4 Construction of EWMA Control Chart 4 4.5 Example 6 5 Selection of Control Chart 8 5.1 Conventional Control Chart and EWMA Control Chart 8 5.2 Average chain length9 5.3 Selection of EWMA Control Chart Parameters 10 6 Procedures for implementing EWMA control charts 11 7 Sensitivity of EWMA chart to non-normal data 12 8 Advantages and Limitations 12 8.1 Advantage 12 8.2 Limitations 12 Appendix A (Informative) Application of EWMA Control Chart 13 Appendix B (normative) EWMA Control Chart 16 for Monitoring Rejection Rate Appendix C (normative) EWMA Control Chart for Monitoring Nonconformances 18 Appendix D (Informative) Validity of Control Charts 20 Reference 24 forewordThis document is in accordance with the provisions of GB/T 1.1-2020 "Guidelines for Standardization Work Part 1.Structure and Drafting Rules of Standardization Documents" drafted. This document is part 6 of GB/T 17989.GB/T 17989 has released the following parts. --- Control Charts Part 1.General Guidelines; --- Control Charts Part 2.General Control Charts; --- Control Charts Part 3.Acceptance Control Charts; --- Control Charts Part 4.Cumulative and Control Charts; --- Statistical method control charts for quality control in production processes - Part 5.Special control charts; --- Statistical method control chart for quality control of production process Part 6.Exponentially weighted moving average control chart; --- Statistical method control chart for quality control in production process Part 7.Multivariate control chart; --- Statistical method control chart for quality control in production process Part 8.Control method for short cycle and small batch; --- Statistical methods for quality control of production process control charts Part 9.Stationary process control charts. This document is modified to adopt ISO 7870-6.2016 "Control Charts - Part 6.Exponentially Weighted Moving Average Control Charts". This document has the following structural adjustments compared to ISO 7870-6.2016. --- In Chapter 1, the entire paragraph "recommended to use conventional control charts in the following situations" is put into Note 1, which is more in line with the narrative logic; --- Change Chapter 3 "Symbols and Abbreviations" to "Terms, Symbols and Abbreviations", add 3.1 Terms and Definitions, and give the terms used In the description, "Symbols and Abbreviations" are divided into "3.2 Symbols" and "3.3 Abbreviations". The technical differences between this document and ISO 7870-6.2016 and their reasons are as follows. --- Modify the symbol "N" in this document to "n", and its interpretation "the number of individuals in the sample (sample size)" to "subgroup size", and Other parts of this series of standards remain consistent; --- Modify the "true value of the standard deviation of the binomial distribution with P=p0" in the explanation of the symbol "σ0" in Chapter 3 to "the binomial distribution with probability p0" The true value of the distribution standard deviation", the explanation is more clear; --- Modify "When λ=1, the EWMA control chart is the mean value control chart" in Chapter 4 4.1 to "When λ=1, EWMA The control chart is the single-value X control chart", the original error is corrected. The following editorial changes have been made to this document. --- Change the standard name to "Production Process Quality Control Statistical Methods Control Chart Part 6.Exponential Weighted Moving Average Control picture"; --- Change the English of "centerline" in 3.2.2 to "centerline"; ---Convert the formula (8) of 4.3 in Chapter 4 "UCL=μ0-Lz (2-λ) " is modified to "UCL=μ0 Lz (2-λ) "; --- Modify the header "EWMA value" of 4.4 Table 1 in Chapter 4 to "EWMAzi"; --- Amend "point 28" in 4.5 of Chapter 4 to "points 29 and 30"; --- Modify "14.5 samples required" in 5.2 of Chapter 5 to "14.9 samples required"; --- Modify "EWMA control chart with ±3σn" in 5.2 of Chapter 5 to "EWMA control chart with ±3σ/n control" limit"; --- Deleted the note to 5.3.2 in Chapter 5; ---The formula (21) in Chapter 5 "δ1=min U1/4-μ0 σ0 ,μ0-L1/4 σ0 ” is modified to δ1=minUμ-μ0σ0,μ0-Lμσ0; --- Modify "δn=2" in 5.3.4 of Chapter 5 to "δ1n=2"; --- Modify the formula (A.2) of Appendix A "Lμ=TL-3σ0=99.5 3×0.1=99.8" to "Lμ=TL 3σ0=99.5 3×0.1=99.8”; --- Modify "Use Table 3" in C.2 of Appendix C to "Use Table 4". Please note that some content of this document may be patented. The issuing agency of this document assumes no responsibility for identifying patents. This document is proposed and managed by the National Standardization Technical Committee on the Application of Statistical Methods (SAC/TC21). This document was drafted by. Tsinghua University, Beihang University, China National Institute of Standardization, Shandong Institute of Standardization, Beijing Institute of Technology Industrial University, Zhongtong Bus Co., Ltd. The main drafters of this document. Sun Jing, Peng Sicheng, Yang Jun, Ding Wenxing, Wei Jie, Sun Liangquan, Xie Tianfa, Li Mengxin, Wu Guangyu.IntroductionControl charts are commonly used statistical tools in process control to monitor deviations in the process and keep the process stable. GB/T 17989 control Figure series standards are divided into the following 9 parts. --- Control Charts Part 1.General Guidelines. The purpose is to give the basic terms, principles and classification of control charts, and to select control charts guide. --- Control charts Part 2.General control charts. The purpose is to establish guidelines for process control using conventional control charts. --- Control charts Part 3.Acceptance control charts. The purpose is to establish guidelines for the use of acceptance control charts for process control, and to specify General procedures for determining subgroup sample sizes, action limits, and decision criteria are described. --- Control Charts Part 4.Cumulative and Control Charts. The purpose is to establish the application of cumulative and techniques for process monitoring, control and review Statistical methods for sex analysis. --- Statistical methods for quality control of production process control charts Part 5.Special control charts. The purpose is to establish the understanding and application of A guide to statistical process control with special control charts. --- Production process quality control statistical methods control charts Part 6.Exponentially weighted moving average control charts. The purpose is to establish A guide to understanding and applying exponentially weighted moving average (EWMA) charts for statistical process control. --- Statistical methods for quality control of production process control charts Part 7.Multivariate control charts. The purpose is to establish the construction and application of multiple A guide to statistical process control with meta-control charts and establishes routine methods for using and understanding multi-variable control charts for measurement data. --- Statistical methods of production process quality control control chart Part 8.Control methods for short cycle and small batches. The purpose is to establish When the subgroup size is 1, the conventional metrology control chart is used to detect the method of short cycle and small batch production process. --- Statistical methods for quality control of production process control charts Part 9.Stationary process control charts. The purpose is to establish the construction and application of A guide to controlling stationary processes with control charts. Conventional control chart is the most widely used statistical control method in process control, but it is relatively slow to monitor the small deviation of the process. Exponentially weighted moving average control charts can quickly detect small and medium excursions in the process in a timely manner. Conventional control charts are easy to use and can quickly detect large process excursions, however they are not effective in monitoring small, medium and large process excursions. Amplitude offset. In many cases, process changes occur slowly and gradually (especially in the case of continuous production). In order to prevent serious deviation of the process This change needs to be detected early. There are two ways to improve the effectiveness of routine control charts for monitoring small and moderate excursions. ---The easiest way is to increase the size of the extracted subgroups, but it is often not economical. When productivity is low, time consuming or detection When the cost is too high, it is not feasible to extract subgroups whose subgroup size is greater than 1, and this method cannot be used. --- In order to detect abnormal fluctuations in the production process, the previous detection results can be taken into account in the process control. General control The chart only considers the sample observations independently, ignoring the information contained in the entire series of observations, resulting in conventional control charts Insensitive to small deviations in the monitoring process. Methods that incorporate previous observations can improve the effectiveness of monitoring small excursions. When slow, gradual shifts in a process need to be monitored, special control charts that take into account past data are often a better choice. Below is In this case, a more effective control method than a conventional control chart. a) Cumulative sum control chart (CUSUM control chart). GB/T 17989.4 specifies this in detail. Compared to the X chart, the cumulative sum Control charts are more sensitive to shifts in the process mean between 0.5 and 2 standard deviations. Cumulative sum control chart for sample-by-sample mean and set The deviation between the set targets is accumulated. Even if there is only a persistent small shift in the process mean, there will be a considerable deviation cumulative sum. Therefore, the cumulative sum control chart is more suitable for the process that is difficult to find in the X control chart with persistent small shifts. b) Exponentially Weighted Moving Average Chart (EWMA Chart). This document will describe this. Exponentially Weighted Moving Average Control Charts are similar in form to regular control charts. The difference between the two is that the monitoring object of the conventional control chart is the sample-by-sample mean, The monitoring object of the exponentially weighted moving average control chart is the weighted average of the current sample mean and the previous sample mean. EWMA charts are often used to monitor small shifts in the process mean. Compared with conventional control charts, EWMA control charts can be more flexible Sensitively found deviations within the range of 0.5 to 2 standard deviations, but could not find large deviations in the mean in time. When the subgroup size is 1, it is recommended Use EWMA control charts. In order to ensure the timely detection of small and large excursions in the process, it is recommended to select the EWMA control chart with a small λ value and the conventional control chart at the same time. use. The EWMA chart can only monitor the process mean, if you need to monitor the discrete trend of the process, you need to use other methods. Production process quality control statistical method control chart Part 6.Exponentially Weighted Moving Average Control Chart1 ScopeThis document gives guidelines for understanding and applying exponentially weighted moving average (EWMA) control charts for statistical process control. EWMA A control chart is a statistical process control technique used to monitor the small fluctuations of the process mean. Moderate volatility. The EWMA control chart uses an exponentially weighted moving average of the sample mean of all historical data to measure the process mean. Evaluate. EWMA weights the samples in a geometrically decreasing manner from near to far from the current position, and the samples closer to the current position are weighted The greater the weight, the greater the influence, while the farther the sample weight is, the smaller the influence is, and the specific weight depends on the smoothing parameter (λ). This document applies when control charts are used to monitor small fluctuations in the process mean. NOTE 1.The main objective of an EWMA chart is the same as a conventional control chart. The relevant content of the conventional control chart has been described in GB/T 17989.2.the following situations Conventional control charts should be used. --- lower productivity; --- The process of sampling and testing is complex and time-consuming; ---The detection cost is too high; ---There are security risks. Note 2.Quality workers can use a set of individual observations obtained in the production line to make individual control charts, rather than using a sample system composed of multiple observations. Make a mean control chart. This option is necessary when the cost of detecting samples composed of multiple observations is prohibitive and difficult to achieve. For example. customer complaints Or the number of product returns is often counted on a monthly basis, and quality managers need to use them to make graphs to reflect quality problems. This type of control chart also applies Quality managers use this type of control chart to monitor small fluctuations in product quality (e.g. due to Equipment wear and tear, resulting in a gradual decline in product quality).2 Normative referencesThe contents of the following documents constitute essential provisions of this document through normative references in the text. Among them, dated citations documents, only the version corresponding to that date applies to this document; for undated references, the latest edition (including all amendments) applies to this document. GB/T 17989.1 Control Drawings Part 1.General Guidelines (GB/T 17989.1-2020, ISO 7870-1.2014, MOD) 3 Terms and Definitions, Symbols, Abbreviations 3.1 Terms and Definitions Terms and definitions defined in GB/T 17989.1 apply to this document. 3.2 Symbols The following symbols apply to this document. 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