A large number of researches addressed the problem of monitoring statistical processes using complete data. Nevertheless, in engineering applications, especially reliability engineering and lifetime test, we often observe the incomplete or censored sample. In this paper, we introduce three EWMA schemes for monitoring exponentially distributed processes based on type-II censored data. Our proposed procedure is capable of detecting a shift in both the origin (location) and scale parameters of a shifted (two-parameter) exponential distribution. As one-parameter exponential distribution is a particular case of the two-parameter exponential with the origin equals zero, the present paper will be useful to detect a shift in the location (origin) or scale or both from traditional one-parameter exponential processes. Also, one can achieve a complete sample situation as a particular case. Therefore, the current article is very general and inclusive. We provide the design parameters, such as control limits of the three schemes and evaluate the performance in terms of average run length characteristics. We investigate the effects of sample size and censoring design on the performance of the proposed schemes. We discuss some realistic examples to illustrate the implementation strategies of the proposed schemes.

大量研究解决了使用完整数据监视统计过程的问题。但是，在工程应用中，尤其是可靠性工程和寿命测试中，我们经常观察到不完整或经过审查的样本。在本文中，我们介绍了三种基于II型检查数据的EWMA方案，用于监视指数分布的过程。我们提出的程序能够检测偏移（两参数）指数分布的原点（位置）和比例参数的偏移。由于一参数指数分布是原点为零的两参数指数的一种特殊情况，因此，本文对于检测传统一参数指数过程中位置（原点）或比例或两者的偏移将很有用。也，作为一种特殊情况，可以实现完整的示例情况。因此，当前的文章非常笼统和包容。我们提供了设计参数，例如三种方案的控制极限，并根据平均行程长度特性评估了性能。我们研究了样本量和审查设计对所提出方案的性能的影响。我们讨论了一些现实的例子来说明所提出的方案的实施策略。