Abstract
A control chart named as the hybrid double exponentially weighted moving average (HDEWMA) to monitor the mean of Weibull distribution in the presence of type-I censored data is proposed in this study. In particular, the focus of this study is to use the conditional median (CM) for the imputation of censored observations. The control chart performance is assessed by the average run length (ARL). A comparison between CM-DEWMA control chart and CM-based HDEWMA control chart is also presented in this article. Assuming different shift sizes and censoring rates, it is observed that the proposed control chart outperforms the CM-DEWMA chart. The effect of estimation, particularly the scale parameter estimation, on ARL is also a part of this study. Finally, a practical example is provided to understand the application and to investigate the performance of the proposal in practical scenarios.
Highlights
We often deal with the detection of assignable causes in the lifetime data, especially in medical and industrial experiments
To assess the performance of conditional median (CM)-hybrid double exponentially weighted moving average (HDEWMA) and CMDEWMA charts considering known and estimated parameter cases under different censoring rate, the average run length (ARL) performance assessment measure is used in this study
Conclusion is article introduces a conditional median- (CM-)based hybrid double exponentially weighted moving average (DEWMA) chart, and its performance is evaluated in detail including a comparison with the CM-based DEWMA for monitoring the mean of the Weibull process in the presence of type-I censoring
Summary
We often deal with the detection of assignable causes in the lifetime data, especially in medical and industrial experiments. The limitations of time and of cost lead to limited data collection often called censored data To monitor such experiments for possible presence of assignable causes of variation and to improve process quality, the traditional control charts, e.g., Shewhart charts, have very poor performance. E first CEV-based Shewhart type control chart was introduced by Steiner and Mackay [4]. Following the CEV idea, Lu and Tsai [7] and Tsai and Lin [8] proposed EWMA charts for monitoring type-I censored data assuming the gamma and Gompertz models, respectively. E aim of this article is to introduce a hybrid CM-based double exponentially weighted moving average (DEWMA) control chart for monitoring the type-I censored Weibull data. A real-life application is provided in Section 5 while Section 6 provides the concluding critiques
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