@inproceedings{Leng:2015:10.1016/j.ifacol.2015.09.730,
author = {Leng, D and Thornhill, NF},
doi = {10.1016/j.ifacol.2015.09.730},
pages = {1457--1464},
publisher = {Elsevier},
title = {Process Disturbance Cause & Effect Analysis Using Bayesian Networks},
url = {http://dx.doi.org/10.1016/j.ifacol.2015.09.730},
year = {2015}
}

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TY  - CPAPER
AB  - 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.
AU  - Leng,D
AU  - Thornhill,NF
DO  - 10.1016/j.ifacol.2015.09.730
EP  - 1464
PB  - Elsevier
PY  - 2015///
SN  - 1474-6670
SP  - 1457
TI  - Process Disturbance Cause & Effect Analysis Using Bayesian Networks
UR  - http://dx.doi.org/10.1016/j.ifacol.2015.09.730
UR  - http://hdl.handle.net/10044/1/34203
ER  - 

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