Abstract
The effectiveness of the data-driven measures depends on the characterization of the process data variations. There are two types of variations for process data: common cause and special cause [245]. The common cause variations are those due entirely to random noise (e.g., associated with sensor readings), whereas special cause variations account for all the data variations not attributed to common cause. Standard process control strategies may be able to remove most of the special cause variations, but these strategies are unable to remove the common cause variations, which are inherent to process data. Since variations in the process data are inevitable, statistical theory plays a large role in most process monitoring schemes.
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© 2001 Springer-Verlag London
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Chiang, L.H., Russell, E.L., Braatz, R.D. (2001). Multivariate Statistics. In: Fault Detection and Diagnosis in Industrial Systems. Advanced Textbooks in Control and Signal Processing. Springer, London. https://doi.org/10.1007/978-1-4471-0347-9_2
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DOI: https://doi.org/10.1007/978-1-4471-0347-9_2
Publisher Name: Springer, London
Print ISBN: 978-1-85233-327-0
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