Topic
c-chart
About: c-chart is a research topic. Over the lifetime, 94 publications have been published within this topic receiving 2407 citations.
Papers published on a yearly basis
Papers
[...]
TL;DR: Design and implementation procedures for counted data CUSUM's (these are sometimes called C USUM's for attributes) are described, which are easy to design and implement and can be used to detect both increases and decreases in the count level.
Abstract: Cumulative Sum (CUSUM) control schemes are widely used in industry for process and measurement control. Most CUSUM applications have been for continuous variables. There have been fewer uses of CUSUM control schemes when the response is a count such as the number of defects per unit or the occurrence of an accident. This article describes design and implementation procedures for counted data CUSUM's (these are sometimes called CUSUM's for attributes). These CUSUM's are easy to design and implement; they can be used to detect both increases and decreases in the count level. Enhancements to the CUSUM scheme, including the fast initial response (FIR) feature and the robust CUSUM are discussed. These enhancements speed up the detection of changes in the count level and guard against the effects of atypical or outlier observations.
408 citations
TL;DR: In this article, the cumulative quantity control chart (CQC-chart) is introduced for monitoring high-yield processes with low defect rates, which can be used no matter whether the process defect rate is low or not.
Abstract: Two commonly used statistical quality control charts, the c-chart and u-chart, are unsatisfactory for monitoring high-yield processes with low defect rates. To overcome this difficulty, a new type of control chart called the cumulative quantity control chart (CQC-chart) is introduced in this paper. The CQC-chart can be used no matter whether the process defect rate is low or not, and when the process defect rate is low or moderate the CQC-chart does not have the shortcoming of the c- and u-charts of showing up false alarm signals too frequently. The CQC-chart does not require rational subgrouping of samples (which is necessary for the c- and u-charts), and is appropriate for monitoring automated manufacturing processes.
169 citations
TL;DR: In this paper, three modified exponentially weighted moving average (EWMA) control charts are developed for monitoring the Poisson counts of a production process and the average run length (ARL) and the probability function of the run length of these modified control charts can be computed exactly using results from the Markov Chain theory.
Abstract: In certain production processes, it is necessary or more convenient to use counts of defects or conformance per unit of measurement to indicate whether a production process is in control or not. Counts of this kind are often well fitted by a Poisson distribution. Three modified exponentially weighted moving average (EWMA) control charts are developed in this paper for monitoring the Poisson counts. The average run length (ARL) and the probability function of the run length of these modified control charts can be computed exactly using results from the Markov Chain theory. These modified control charts are demonstrated to be generally superior than the Shewhart control chart based on ARL consideration. Tables of in-control ARLs of these modified control charts are given to assist the implementation of these modified control charts. The implementation and design of these EWMA control charts are discussed. The use of these modified EWMA control charts is illustrated with an example.
105 citations
TL;DR: In this paper, a more efficient alternative to the standard p chart approach, i.e., the construction of a moving average control chart for fraction non-conforming, is discussed.
Abstract: The p chart is often used to monitor the fraction of non-conforming products. However, the p chart is slow in detecting sustaining shifts of small magnitude in the level of fraction non-conforming. This paper discusses a more efficient alternative to the standard p chart approach, i.e. the construction of a moving average control chart for fraction non-conforming, p. Approximations by means of mathematical calculations and the corresponding simulation results for the average run length profiles show that the performance of the new approach is superior to that of the standard approach. The new approach is easy to implement and hence might be attractive and useful to practitioners. Examples are also given to show how the proposed procedure is put to work in real situations. Copyright © 2004 John Wiley & Sons, Ltd.
69 citations
Book•
27 Sep 2001
TL;DR: This paper presents guidelines for making Control Chart Theory and the I Chart for Time-Ordered Data Useful and Variables Data: Basics of Statistics and Graphs, a guide to using Attribute Data.
Abstract: 1. Objectives and Preliminary Information. 2. Variables Data: Basics of Statistics and Graphs. 3. The Run Chart for Time-Ordered Variables Data. 4. Control Chart Theory and the I Chart for Time-Ordered Data. 5. The Xbar & s Chart. 6. Process Capability. 7. Using Attribute Data: The c Chart and the u Chart. 8. Using Attribute Data: The p Chart. 9. Transformations (An Advanced Topic). 10. Guidelines for Making Control Charts Useful. Appendix 1. Appendix 2. Appendix 3. Appendix 4.
66 citations
Related Topics (5)
Performance Metrics
| Year | Papers |
|---|---|
| 2021 | 1 |
| 2020 | 2 |
| 2019 | 1 |
| 2018 | 4 |
| 2017 | 4 |
| 2016 | 5 |