Average run lengths for cusum control charts applied to residuals

George C. Runger · Thomas R. Wlllemain · Sharad S. Prabhu

89 citations0/0 files storedCommunication in Statistics- Theory and Methods

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Advanced Statistical Process Monitoring Probability and Statistical Research Fault Detection and Control Systems

Abstract

A common approach to building control charts for autocorrelated data is to apply classical SPC to the residuals from a time series model of the process. However, Shewhart charts and even CUSUM charts are less sensitive to small shifts in the process mean when applied to residuals than when applied to independent data. Using an approximate analytical model, we show that the average run length of a CUSUM chart for residuals can be reduced substantially by modifying traditional chart design guidelines to account for the degree of autocorrelation in the data.

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 89 citations exist upstream.

References

1977Time Series Analysis: Forecasting and Control 1972An Approach to the Probability Distribution of Cusum Run Length 1982Combined Shewhart-CUSUM Quality Control Schemes 1991Statistical process control procedures for correlated observations 1994Run-Length Distributions of Special-Cause Control Charts for Correlated Processes 1974The Effect of Serial Correlation on the Performance of CUSUM Tests 1993Performance of CUSUM Control Schemes for Serially Correlated Observations 1975The Effect of Serial Correlation on the Performance of CUSUM Tests II

Cited by

2009CUSUM Monitoring of First-Order Integer-Valued Autoregressive Processes of Poisson Counts 2003Design of Exponentially Weighted Moving Average Control Charts for Autocorrelated Processes With Model Uncertainty 2002Support vector machines for recognizing shifts in correlated and other manufacturing processes 2002Model‐based control chart for autoregressive and correlated data 2011Effective recognition of control chart patterns in autocorrelated data using a support vector machine based approach

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

Imported from OpenAlex (CC0) — nothing here is generated.