Process Disturbance Cause & Effect Analysis Using Bayesian Networks
File(s)
Author(s)
Leng, D
Thornhill, NF
Type
Conference Paper
Abstract
Process disturbances can propagate over entire plants and it can be difficult to locate their root causes from observed effects. Bayesian Networks offer a way to represent unit operations, processes and whole plants as probabilistic models which can be used to infer and rank likely causes from observed effects. This paper presents a methodology to use deterministic steady-state process models to derive Bayesian Networks based on alarm event detection. An example heat recovery network is used to illustrate the model building and inferential procedures.
Date Issued
2015-09-02
Date Acceptance
2015-09-02
Citation
IFAC Proceedings Volumes (IFAC-PapersOnline), 2015, 48 (21), pp.1457-1464
ISSN
1474-6670
Publisher
Elsevier
Start Page
1457
End Page
1464
Journal / Book Title
IFAC Proceedings Volumes (IFAC-PapersOnline)
Volume
48
Issue
21
Copyright Statement
© 2015 Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/
Source
9th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes SAFEPROCESS 2015
Publication Status
Published
Start Date
2015-09-02
Finish Date
2015-09-04
Coverage Spatial
Paris, France