Robustness of Forecast-Based Monitoring Schemes

Iou-Tsyr Tseng

54 citations0/0 files storedJournal of Quality Technology

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Advanced Statistical Process Monitoring Advanced Control Systems Optimization Fault Detection and Control Systems

Abstract

Forecast-based monitoring schemes for monitoring autocorrelated data are two stage processes. The first step is to determine an appropriate time-series model for the process data. The process is then monitored using control charts applied to the one-step-ahead forecast residuals obtained from the model in the first step. Past evaluations of forecast-based monitoring schemes have assumed the process model and its associated parameters are known without error. This paper investigates the impact of estimation error of model parameters on the performance of control charts applied to forecast residuals. It is shown that these schemes are sensitive to estimation error. The direction of the estimation error is also shown to be important.

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References

1954CONTINUOUS INSPECTION SCHEMES 1978Time Series Analysis: Forecasting and Control.1990Exponentially Weighted Moving Average Control Schemes: Properties and Enhancements 1991Some Statistical Process Control Methods for Autocorrelated Data 1989Design of Exponentially Weighted Moving Average Schemes 1991Statistical process control procedures for correlated observations 1982Fast Initial Response for CUSUM Quality-Control Schemes: Give Your CUSUM A Head Start 1997Introduction to Statistical Quality Control

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