Self-starting monitoring of the progressive type II censoring data based on goodness-of-fit test

Abstract

ABSTRACT Censoring is a common occurrence in life testing and reliability analysis, primarily due to constraints such as time and cost. In this paper, we construct control charts using goodness-of-fit test statistics to monitor novel and effective progressive Type II censoring data. In practice, scenarios where the number of samples in Phase I is insufficient often arise, prompting the proposal of self-starting exponentially weighted moving average (EWMA) control charts based on goodness-of-fit test statistics to monitor such processes. Numerous Monte Carlo simulation experiments demonstrate the excellent performance of the control charts proposed in this paper under different settings. A breakdown time of an insulating fluid example is utilized to illustrate the proposed EWMA and self-starting EWMA control charts.