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Activity Number:
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403
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Type:
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Invited
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Date/Time:
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Wednesday, August 1, 2007 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Quality and Productivity
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| Abstract - #308109 |
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Title:
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An Optimal Filter Design Approach to Statistical Process Control
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Author(s):
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Daniel W. Apley*+ and Chang-Ho Chin
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Companies:
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Northwestern University and Kyung Hee University
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Address:
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Dept. of Industrial Engineering, Evanston, IL, 60208,
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Keywords:
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Statistical Process Control ; Control Charts ; Autocorrelation ; Linear Filtering ; Markov Chains
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Abstract:
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Many control charts can be viewed as charting the output of a linear filter applied to process data, with an alarm sounded when the filter output falls outside a set of control limits. We generalize this concept by considering a linear filter in its most general time-invariant form. We provide a strategy for optimizing the filter coefficients in order to minimize the out-of-control ARL, while constraining the in-control ARL to some desired value. The optimal linear filters exhibit a number of interesting characteristics, in particular when the process data are autocorrelated. In many situations, they also substantially outperform an optimally designed exponentially weighted moving average (EWMA) control chart.
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