Statistical process control using level crossings

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.