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Data-Driven Methods for Fault Detection and Diagnosis in Chemical Processes

Data-Driven Methods for Fault Detection and Diagnosis in Chemical Processes

Paperback

Series: Advances in Industrial Control

Technology & EngineeringGeneral Computers

ISBN10: 1447111338
ISBN13: 9781447111337
Publisher: Springer Nature
Published: Nov 1 2012
Pages: 192
Weight: 0.67
Height: 0.45 Width: 6.14 Depth: 9.21
Language: English
Early and accurate fault detection and diagnosis for modern chemical plants can minimise downtime, increase the safety of plant operations, and reduce manufacturing costs. The process-monitoring techniques that have been most effective in practice are based on models constructed almost entirely from process data. The goal of the book is to present the theoretical background and practical techniques for data-driven process monitoring. Process-monitoring techniques presented include: Principal component analysis; Fisher discriminant analysis; Partial least squares; Canonical variate analysis.

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