QUALITY PAPERA new rapid and robust multi-factorial screening for low-percentile use-rate accelerated reliability analysis

Abstract

PurposeThe study aims to provide a quick-and-robust multifactorial screening technique for early detection of statistically significant effects that could influence a product's life-time performance.Design/methodology/approachThe proposed method takes advantage of saturated fractional factorial designs for organizing the lifetime dataset collection process. Small censored lifetime data are fitted to the Kaplan–Meier model. Low-percentile lifetime behavior that is derived from the fitted model is used to screen for strong effects. A robust surrogate profiler is employed to furnish the predictions.FindingsThe methodology is tested on a difficult published case study that involves the eleven-factor screening of an industrial-grade thermostat. The tested thermostat units are use-rate accelerated to expedite the information collection process. The solution that is provided by this new method suggests as many as two active effects at the first decile of the data which improves over a solution provided from more classical methods.Research limitations/implicationsTo benchmark the predicted solution with other competing approaches, the results showcase the critical first decile part of the dataset. Moreover, prediction capability is demonstrated for the use-rate acceleration condition.Practical implicationsThe technique might be applicable to projects where the early reliability improvement is studied for complex industrial products.Originality/valueThe proposed methodology offers a range of features that aim to make the product reliability profiling process faster and more robust while managing to be less susceptible to assumptions often encountered in classical multi-parameter treatments.

Bayesian analysis of designed reliability improvement experiments with application to adaptive termination

“…Besseris 13 applied the super‐ranking technique to screen important factors which affect aluminum milling operations significantly, and improve product reliability via optimize the levels of important factors. Besseris and Tsarouhas 14 introduced a minimum rank‐sum method to identify experimental factors which affect 10th percentile lifetime. Rigdon et al.…”