Assessing a binary measurement system: A new plan using targeted verification with conditional sampling and baseline information. (November 2019) - Severn, Daniel E.; Steiner, Stefan H.; MacKay, R. Jock
Highlights: Estimate misclassification rates for non-destructive pass/fail inspection system. Efficient plans using available data, repeated measurement & conditional sampling. Demonstrated improvement to existing assessment plans. Abstract: We investigate efficient plans to assess the misclassification error rates of a binary measurement system used as an in-line inspection protocol. We assume that parts can be inspected repeatedly and that each part has its own (latent) misclassification rate. We propose a three-phase assessment plan. Phase I consists of data from recent inspection history. In Phase II, we select a sample of failed parts that we re-measure multiple times with the binary measurement system of interest. In Phase III, we verify a carefully selected subsample of the parts from Phase II with the aid of a binary gold standard measurement system. We show that the proposed plan is a substantial improvement over existing assessment plans in terms of cost and/or precision.