Robust proportional hazard-based monitoring schemes for reliability data

Reliability of products is a key factor for successful businesses. In general, the existing monitoring schemes have poor performance as reliability data are often censored. Also, the products are manufactured in multistage processes where the outgoing quality gets affected by the previous stage quality. Besides this cascade property, historical data with outliers make the analysis even more complicated. This paper discusses exponentially weighted moving average (EWMA) and cumulative sum (CUSUM) control charts for monitoring a two-stage dependent process. In particular, the proportional hazard (PH) model is assumed for modeling the relationship of quality characteristics. Furthermore, to remove the detrimental effects of outliers on the results, robust regression method known as the M-estimation is used. Besides a real case study, the performance of the proposed monitoring approach is assessed through a comprehensive simulation study. The results suggested that the EWMA chart outperforms the CUSUM chart.