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Run Rules and Their False-Alarm Cost

Reference for western-electric-rules-checker · 52 lines

Zones

text
 +3 sigma  ---------- UCL
   Zone A   (2 to 3 sigma)
 +2 sigma
   Zone B   (1 to 2 sigma)
 +1 sigma
   Zone C   (0 to 1 sigma)
   centre   ---------- CL
   Zone C, B, A mirrored below
 -3 sigma  ---------- LCL

Sigma here is the sigma of the plotted statistic: for an X-bar chart, sigma_x / sqrt(n) = (UCL - CL) / 3.

Rule sets

Western ElectricNelsonPattern
WE1: 1 point beyond zone A1Single point beyond 3 sigma
WE2: 2 of 3 in zone A or beyond, same side5Shift starting
WE3: 4 of 5 in zone B or beyond, same side6Smaller shift
WE4: 8 in a row on one side2 (9 in a row)Sustained shift
-3: 6 in a row increasing or decreasingTrend
-4: 14 in a row alternatingOver-adjustment, two streams
-7: 15 in a row in zone CStratification
-8: 8 in a row outside zone C, both sidesMixture

we_rules.py reports WE1-WE3 under their WE names in both modes and adds N2, N3, N4, N7, N8 with --nelson.

False alarms

Rules appliedIn-control false-alarm probability per pointAverage run length (ARL0)
WE1 only0.27%~370
WE1-WE4 together~1.1%~92
All eight Nelson rules~1.5-2%~50-65

More rules detect small shifts sooner but raise false alarms. Practical choice:

  • Phase I (establishing limits): all rules, to find every special cause.
  • Phase II on a stable process: WE1-WE4; add the trend rule where wear is expected.
  • Range / S charts: use WE1 and runs above the centre only; zone rules assume symmetry the R distribution lacks.
  • Attribute charts with small n p-bar: WE1 and same-side runs; zone rules are unreliable.

Prerequisites

Rules assume independent, roughly normal plotted values and limits from a stable baseline. Autocorrelated data (chemical processes, continuous sensors) trigger runs constantly: subgroup less often, chart residuals, or use EWMA/CUSUM.

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