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Assessing a binary measurement system: A new plan using targeted verification with conditional sampling and baseline information

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Date

2019-11

Authors

Severn, Daniel Ernest
Steiner, Stefan H.
MacKay, R. Jock

Advisor

Journal Title

Journal ISSN

Volume Title

Publisher

Elsevier

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.

Description

The final publication is available at Elsevier via https://doi.org/10.1016/j.measurement.2019.06.019. © 2019. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/

Keywords

baseline data, binary measurement system assessment, conditional sampling, targeted verification

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