Hg. Chong et Wj. Walley, RULE-BASED VERSUS PROBABILISTIC APPROACHES TO THE DIAGNOSIS OF FAULTSIN WASTE-WATER TREATMENT PROCESSES, Artificial intelligence in engineering, 10(3), 1996, pp. 265-273
The need for computer-based diagnostic tools in wastewater management
is outlined. Rule-based and probabilistic approaches to the developmen
t of diagnostic expert systems are critically reviewed, and it is demo
nstrated that the rule-based approach has serious limitations which ma
ke it unsuitable for diagnostic tasks under conditions of uncertainty.
It is shown that Bayesian belief networks (BBNs), a probabilistic app
roach, has none of these limitations and is well-suited to diagnosis u
nder 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 BB
N for the diagnosis of faults in a wastewater treatment plant. It is c
oncluded that BBNs are far superior to rule-based systems in their abi
lity to diagnose faults in complex systems like wastewater treatment p
rocesses, whose behaviour is inherently uncertain.