On-line fault detection and supervision in the chemical process industries: selected papers from the IFAC symposium, Newark, Delaware, USA, 22-24 April 1992
Published for the International Federation of Automatic Control by Pergamon Press, Apr 13, 1993 - Technology & Engineering - 316 pages
The papers presented at this Symposium were grouped into five major sections: Strategies for the detection and diagnosis of process faults; Modeling, validation, and interpretation of process trends; Supervision and control of chemical plants; Neural networks in process supervision and fault diagnosis. Industrial applications in process supervision and fault diagnosis; The fifty-two papers in this volume cover both theoretical and practical/implementational aspects of systems in the process control industry.
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STRATEGIES FOR THE DETECTION
Structured Residuals for Fault Isolation Disturbance Decoupling and Modelling
Detection of Incipient Process Faults using Approximate Parametric Models
39 other sections not shown
abnormal adaptive alarm algorithm analysis application approach Artificial Intelligence artificial neural networks Automatic behavior blocks cause Chemical Process Industries cluster constraints control system decoupling described detection and diagnosis Detection and Supervision developed deviation diagnosis system disturbances dynamic Engineering estimation evaluation example expert system failure fault diagnosis Fieldbus Figure flow functions fuzzy graph identify IFAC On-line Fault inference Kalman filter knowledge base knowledge-based leak linear matrix measurement method model-based monitoring neural networks nodes nonlinear normal object observer On-line Fault Detection operating optimization output paper machine parameters parity equations pattern recognition performance plant pressure problem procedure process computer process fault production qualitative reactor real-time regime representation represented residual robust rules scale-space scheme sensor shown in Fig signal simulation steam steam reformer structure supervised learning tank techniques temperature threshold tion trajectory unknown input valid values vector wavelet