Abstract
In the chemical and other related industries, there has been a large push to produce higher quality products, to reduce product rejection rates, and to satisfy increasingly stringent safety and environmental regulations. Process operations that were at one time considered acceptable are no longer adequate. To meet the higher standards, modern chemical processes contain a large number of variables operating under closed loop control. The standard process controllers (PID controllers, model predictive controllers, etc.) are designed to maintain satisfactory operations by compensating for the effects of disturbances and changes occurring in the process. While these controllers can compensate for many types of disturbances, there are changes in the process which the controllers cannot handle adequately. These changes are called faults. More precisely, a fault is defined as an unpermitted deviation of at least one characteristic property or variable of the system [95].
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© 2000 Springer-Verlag London
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Russell, E.L., Chiang, L.H., Braatz, R.D. (2000). Introduction. In: Data-driven Methods for Fault Detection and Diagnosis in Chemical Processes. Advances in Industrial Control. Springer, London. https://doi.org/10.1007/978-1-4471-0409-4_1
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DOI: https://doi.org/10.1007/978-1-4471-0409-4_1
Publisher Name: Springer, London
Print ISBN: 978-1-4471-1133-7
Online ISBN: 978-1-4471-0409-4
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