On-line Fault Detection and Supervision in the Chemical Process Industries, 1995: A Postprint Volume from the IFAC Workshop, Newcastle Upon Tyne, UK, 12-13 June 1995

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A. J. Morris, E. B. Martin
International Federation of Automatic Control, 1996 - Technology & Engineering - 237 pages
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Paperback. These proceedings contain the papers from the IFAC Workshop on On-Line Fault Detection and Supervision in the Chemical Process Industries held in Newcastle-upon-Tyne, UK, 12-13 June 1995. The Workshop provided an ideal forum for academic and industrial researchers to discuss their experience and research results in this field. Topics covered included: Multivariate Methods, Neural Network Approaches, Supervision, Control & Diagnosis, Expert Systems, Learning & Dependable Systems, Performance & Fault Detection, Model Based Systems and Applications.

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Contents

PLENARY PAPER
1
Confidence Bounds for Multivariate Process Performance Monitoring Charts
23
E B MARTIN A J MORRIS M C PAPAZOGLOU
33
T FUJIWARA T TSUSHI H NISHITANI
39
The 1995 Situation
45
Process Fault Diagnosis using a SelfAdaptive Neural Network with OnLine Learning Capabilities
69
Novel OnLine Identification Procedure using Artificial Neural Network
75
AKHMETOV Y DOTE
83
Towards a Theory of Dynamic Consistency Relations
129
EXPERT SYSTEMS
143
A KORDON P DHURJATI
149
Automatic Model Generation for ModelBased Fault Detection in Process Plants
155
Machine Learning Fault Models of Industrial RFCC Processes
161
S H YANG X Z WANG C McGREAVY
167
Fault Detection and Isolation in the Directional Residual Approach
181
K YIN J J GERTLER
187

G HESSEL W SCHMJTT F P WEISS
89
ModelBased Validation of a DOx Sensor
94
SUPERVISION CONTROL AND DIAGNOSIS
95
Detection of Distorted Pattern using Dynamic Time Warping Algorithm and Application
101
K GOLLMER C POSTEN
107
OnLine Detection of Multiple Faults in an Experimental Batch Process
117
A MEDL F PERSCHL G SCHMIDT
123
ModelBased Alarm Filtering for Pilot Plants
207
OnLine Fault Detection and Diagnosis using Fuzzy Models
213
Model Predictive Averaging Level Control using Disturbance Prediction
219
Using Recursive State Estimation for Singular Systems Application to Unknown Input
225
Identification of Nonlinear Dynamic Systems using Hybrid Neural Networks
231
HH TUNG DN LUAN CC
237

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