A new statistical approach to automated quality control in manufacturing processes

Michael W. Milo, Michael Roan, Bradley Harris

Research output: Contribution to journalArticlepeer-review

24 Scopus citations


Automated quality control is a key aspect of industrial maintenance. In manufacturing processes, this is often done by monitoring relevant system parameters to detect deviations from normal behavior. Previous approaches define "normalcy" as statistical distributions for a given system parameter, and detect deviations from normal by hypothesis testing. This paper develops an approach to manufacturing quality control using a newly introduced method: Bayesian Posteriors Updated Sequentially and Hierarchically (BPUSH). This approach outperforms previous methods, achieving reliable detection of faulty parts with low computational cost and low false alarm rates (∼0.1%). Finally, this paper shows that sample size requirements for BPUSH fall well below typical sizes for comparable quality control methods, achieving True Positive Rates (TPR) >99% using as few as n = 25 samples.

Original languageEnglish (US)
Pages (from-to)159-167
Number of pages9
JournalJournal of Manufacturing Systems
StatePublished - 2015

All Science Journal Classification (ASJC) codes

  • Software
  • Control and Systems Engineering
  • Hardware and Architecture
  • Industrial and Manufacturing Engineering


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