Assessment of a Binary Measurement System in Current Use

摘要

Binary measurement systems that classify parts as pass or fail are widely used in industry, especially for systematic inspection in high-volume processes. In this context, we are likely to have available a large number of previously measured passed and failed parts. To support production and quality improvement, it is important to assess the misclassification rates, e.g., the probability of failing a conforming part or passing a nonconforming part. We may also want to estimate the unknown conforming rate. Here we focus on the assessment of a binary measurement system when no gold-standard measurement system is available. The standard assessment plan is to repeatedly measure a sample of parts and use a latent class model. We demonstrate the substantial benefit of supplementing the standard plan with the available data from the previously measured parts. We propose new sampling plans and compare them with the standard plan with respect to the precision of the estimators of the misclassification rates. We also give recommendations for planning an assessment study when we can sample from a population of previously measured parts.