Statistical process control using level crossings

Thomas R. Willemain, George C. Runger

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


Conventional SPC methods do not adequately cope with autocorrelated processes. We develop a new method based on the random run lengths created when a process crosses a threshold level. We combine information on runs above and below the threshold into a statistic that can be treated as iid Normal and monitored with traditional SPC methods, such as Shewhart charts. The method is simpler than alternative approaches based on ARMA modeling, works without modification for both lid and autocorrelated processes, is robust to the marginal distribution of the data, and performs well compared to Shewhart charts based on ARMA residuals.

Original languageEnglish (US)
Pages (from-to)7-20
Number of pages14
JournalJournal of Statistical Computation and Simulation
Issue number1
StatePublished - Dec 1 1994
Externally publishedYes


  • ARMA models
  • Autocorrelation
  • Shewhart chart
  • level crossing
  • statistical process control

ASJC Scopus subject areas

  • Statistics and Probability
  • Modeling and Simulation
  • Statistics, Probability and Uncertainty
  • Applied Mathematics


Dive into the research topics of 'Statistical process control using level crossings'. Together they form a unique fingerprint.

Cite this