Constructing tolerance intervals for the number of defectives using both high-and low-resolution data

Hsiuying Wang, Fugee Tsung

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

Defect inspection is important in many industries, such as in the manufacturing and pharmaceutical industries. Existing methods usually use either low-resolution data, which are obtained from less precise measurements, or high-resolution data, which are obtained from more precise measurements, to estimate the number of defectives in a given amount of goods produced. In this study, a novel approach is proposed that combines the two types of data to construct tolerance intervals with a desired average coverage probability. A simulation study shows that the derived tolerance intervals can lead to better performance than a tolerance interval that is constructed based on only the low-resolution data. In addition, a real-data example shows that the tolerance interval based on only the low-resolution data is more conservative than the tolerance intervals based on both high-resolution and low-resolution data.

Original languageEnglish
Pages (from-to)354-364
Number of pages11
JournalJournal of Quality Technology
Volume49
Issue number4
DOIs
StatePublished - 1 Oct 2017

Keywords

  • Binomial Distribution
  • Confidence Interval
  • Coverage Probability
  • Data Fusion
  • Tolerance Interval

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