Rule-based versus probabilistic approaches to the diagnosis of faults in wastewater treatment processes. Chong, H. & Walley, W. Artificial Intelligence in Engineering, 10(3):265-273, 8, 1996. ![Rule-based versus probabilistic approaches to the diagnosis of faults in wastewater treatment processes [link]](https://bibbase.org/img/filetypes/link.svg "link")
Website doi abstract bibtex The need for computer-based diagnostic tools in wastewater management is outlined. Rule-based and probabilistic approaches to the development of diagnostic expert systems are critically reviewed, and it is demonstrated that the rule-based approach has serious limitations which make it unsuitable for diagnostic tasks under conditions of uncertainty. It is shown that Bayesian belief networks (BBNs), a probabilistic approach, has none of these limitations and is well-suited to diagnosis under uncertainty. The theory and application of BBNs are outlined and illustrated by a simple example based on a wastewater treatment plant. A brief case study is presented of the development of a full-scale BBN for the diagnosis of faults in a wastewater treatment plant. It is concluded that BBNs are far superior to rule-based systems in their ability to diagnose faults in complex systems like wastewater treatment processes, whose behaviour is inherently uncertain.
@article{
title = {Rule-based versus probabilistic approaches to the diagnosis of faults in wastewater treatment processes},
type = {article},
year = {1996},
keywords = {Bayesian belief networks,causal belief networks,diagnosis,expert systems,rulebased systems,uncertainty,wastewater treatment},
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abstract = {The need for computer-based diagnostic tools in wastewater management is outlined. Rule-based and probabilistic approaches to the development of diagnostic expert systems are critically reviewed, and it is demonstrated that the rule-based approach has serious limitations which make it unsuitable for diagnostic tasks under conditions of uncertainty. It is shown that Bayesian belief networks (BBNs), a probabilistic approach, has none of these limitations and is well-suited to diagnosis under uncertainty. The theory and application of BBNs are outlined and illustrated by a simple example based on a wastewater treatment plant. A brief case study is presented of the development of a full-scale BBN for the diagnosis of faults in a wastewater treatment plant. It is concluded that BBNs are far superior to rule-based systems in their ability to diagnose faults in complex systems like wastewater treatment processes, whose behaviour is inherently uncertain.},
bibtype = {article},
author = {Chong, H.G. and Walley, W.J.},
doi = {10.1016/0954-1810(96)00003-9},
journal = {Artificial Intelligence in Engineering},
number = {3}
}
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