Abstract
The aim of statistical process control (SPC) techniques, when applied to control-loop operating data, is to identify and track certain variation within the loop, and thus highlight situations that show abnormal behaviour, i.e. statistically significant events or abnormalities. Indeed, understanding this variation may be a first step towards the improvement of controller performance. Since variation is present in any process, deciding when the variation is natural and when it needs correction is the key to monitor control performance using SPC. Today, SPC has become more than control charting alone; it is an umbrella term for the set of activities and methods for data analysis and quality control. This chapter contains a brief description of selected univariate and multivariate SPC techniques.
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Jelali, M. (2013). Statistical Process Control. In: Control Performance Management in Industrial Automation. Advances in Industrial Control. Springer, London. https://doi.org/10.1007/978-1-4471-4546-2_8
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DOI: https://doi.org/10.1007/978-1-4471-4546-2_8
Publisher Name: Springer, London
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