Robust AFT-based monitoring procedures for reliability data. Issue 1 (2nd January 2020) - Asadzadeh, Shervin; Baghaei, Arash
ABSTRACT: Many monitoring approaches have been proposed to control reliability-related quality characteristics in single-stage processes. However, in practice, processes are made up of several sub-processes, so the most important feature of multi-stage processes, called cascade property, should be considered by establishing the relationship between the quality characteristics of interest. Moreover, outliers are commonly present in historical data set. In this paper, modelling and monitoring reliability data in the presence of outliers have been addressed. To this end, the accelerated failure time models have been integrated with robust regression techniques to find the accurate relationship among variables. The robust regression method, called M-estimator, has been used in order to dampen the detrimental impact of destructive outliers. Subsequently, a novel cumulative sum (CUSUM) and two exponentially weighted moving average control charts have been constructed for the sake of optimal monitoring of Weibull-distributed quality variable with dependent influential covariate. The performance of the proposed control schemes has been evaluated via extensive simulation studies. The results reveal that the CUSUM control chart outperforms its counterparts. Finally, a real case study in bag weaving industry has been considered to show the application of the proposed CUSUM control chart.