Journal ArticleDOI
Design of exponential control charts based on average time to signal using a sequential sampling scheme
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TLDR
The average time to signal (ATS) is used instead of the average run length to evaluate the performance of TBE charts, since the ATS involves both the number and the time of samples inspected until a signal occurs.Abstract:
Exponential charts based on time-between-events (TBE) data are widely investigated and applied in various fields. The average time to signal (ATS) is used instead of the average run length to evaluate the performance of TBE charts, since the ATS involves both the number and the time of samples inspected until a signal occurs. An ATS-unbiased exponential control chart is proposed when the in-control parameter is known. Considering the need in practice to start monitoring a production process as soon as possible, a sequential sampling scheme is adopted and the in-control parameter is estimated by an unbiased and consistent estimator. Some specific guidelines to stop updating control limits are obtained from the relationship between the phase I sample size and the actual false alarm rate. Finally, two real examples are given to illustrate the implementation and efficiency of the proposed method.read more
Citations
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Journal ArticleDOI
An Overview of Control Charts for High‐quality Processes
TL;DR: Time-between-events control charts detect an out-of-control situation without great loss of sensitivity as compared with existing charts, and draw a precise conclusion from the statistical point of view.
Journal ArticleDOI
Design of Gamma Charts Based on Average Time to Signal
TL;DR: For the phase I monitoring, a new ATS-unbiased design with unknown parameters is developed, and a sequential sampling scheme is adopted to start process monitoring as soon as possible.
Journal ArticleDOI
Time‐between‐events control charts for an exponentiated class of distributions of the renewal process
TL;DR: This article presents some new control charts based on the renewal process, where a class of absolutely continuous exponentiated distributions is assumed for the time between events, including the generalized exponential, generalized Rayleigh, and exponentiated Pareto distributions.
Journal ArticleDOI
Monitoring of time between events with a double generally weighted moving average control chart
Journal ArticleDOI
High quality process monitoring using a class of inter-arrival time distributions of the renewal process
Sajid Ali,Antonio Pievatolo +1 more
TL;DR: A new TBE control chart is introduced, based on the renewal process, where the distribution of the TBE belongs to a parametric class of absolutely continuous distributions, which includes some well-known and commonly used lifetime distributions, i.e., exponential, Rayleigh, Weibull, Burr type XII, Pareto and Gompertz.
References
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Journal ArticleDOI
Research Issues and Ideas in Statistical Process Control
TL;DR: An overview of current research on control charting methods for process monitoring and improvement and a historical perspective and ideas for future research are given.
Journal ArticleDOI
X charts with variable sampling intervals
TL;DR: The variable sampling interval (VSI) chart as discussed by the authors uses a short sampling interval if the sample is close to but not actually outside the control limits, and a long sampling interval for the sample if it is closer to target.
Journal ArticleDOI
Counted Data CUSUM's
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.
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Some effective control chart procedures for reliability monitoring
TL;DR: A recent control scheme based on the cumulative quantity between observations of defects has been proposed which can be easily adopted to monitor the failure process for exponentially distributed inter-failure time and can detect process improvement even in a high-reliability environment.
Journal ArticleDOI
A note on the intervals between coal-mining disasters
TL;DR: The data given by Maguire, Pearson & Wynn (1952) on the time intervals between successive coal-mining disasters involving 10 or more men killed have been found to contain a number of errors.