Conditional expected approach based monitoring of mean of type-I censored Weibull lifetimes

Incorporating censored data analysis plays a pivotal role in contemporary research and practical applications, offering a profound understanding of the importance of censored observations. The literature commonly utilizes modified exponentially weighted moving average control charts to monitor subtle shifts in process parameters assuming complete data. However, censoring in real-life scenarios poses challenges as these charts are designed for complete data and may lose accuracy and reliability. This article introduces a Modified EWMA control chart (MEM_C) for monitoring mean level changes in Weibull lifetimes from type-I censored data using conditional expected values. Monte Carlo simulations are employed to obtain numerical results. The performance of MEM_C is evaluated using average run length measures, comparing it with the conditional expected values based EWMA control chart (EWMA_C). The study shows that the MEM_C is particularly sensitive for monitoring small to moderate shifts and outperforms the EWMA_C chart. An application of the proposed chart in assessing the rust-resistant capability of a painting process is also provided.