• The Korean Journal of Applied Statistics
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• v.29 no.5
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• pp.823-833
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• 2016

The lifetime is a key characteristic of product quality. It is best to obtain the lifetime data of all samples, but they are often censored due to time or expense limitations. In this paper, we propose a binomial cumulative sum (CUSUM) chart to monitor the mean of type I right-censored Weibull lifetime data, for a xed value of the Weibull shape parameter. We compare the performance of the proposed binomial CUSUM chart with CUSUM charts studied previously using the steady-state average run length (ARL). The results show that the performance of the binomial CUSUM chart is better when the censoring rate is high and/or the sample size is small.

• The Korean Journal of Applied Statistics
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• v.34 no.5
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• pp.735-744
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• 2021

Maintaining the lifetime of a product is one of the objectives of quality control. In real processes, most samples are constructed with censored data because, in many situations, we cannot measure the lifetime of all samples due to time or cost problems. In this paper, we propose two cumulative sum (CUSUM) control charting procedures to monitor the mean of type I right-censored lognormal lifetime data. One of them is based on the likelihood ratio, and the other is based on the binomial distribution. Through simulations, we evaluate the performance of the two proposed procedures by comparing the average run length (ARL). The overall performance of the likelihood ratio CUSUM chart is better, especially this chart performs better when the censoring rate is low and the shape parameter value is small. Conversely, the binomial CUSUM chart is shown to perform better when the censoring rate is high, the shape parameter value is large, and the change in the mean is small.

• Journal of Korean Society for Quality Management
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• v.25 no.3
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• pp.62-73
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• 1997

Previous VSI control chart works have been done on quality variable whose distribution is normal. But there are many situations in which hte assumption of not a, pp.opriate. Also, in many industrial processes, the interest is to monitor the number of defectives. In this paper, we will take the existing properties of VSI control chart developed for the normal distribution and a, pp.y them to the np-chart based on the discrete binomial distribution. We will consider the CUSUM chart for the number of defectives. Here, the interesting object is to compute the VSI ATS for CUSUM control chart using Markov chain a, pp.oach and to compare FSI ATS and VSI ATS.