Data-driven Methods for Fault Detection and Diagnosis in Chemical Processes by Evan L. Russell , Leo H. Chiang , Richard D. Braatz
English | PDF | 2000 | 193 Pages | ISBN : 1447111338 | 17.8 MB
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.