An Application of Qualitative Reasoning to Knowledge Acquisition in Process Diagnosis. Automatic Rule Generation by Qualitative Simulation

Abstract

AbstractThe problems with recent approaches in qualitative and causal reasoning are that: (1) in qualitative modeling, they do not discuss on what types of mathematical models their qualitative equations should be based. This allows the misuse of static models for the qualitative equations to discuss changes in the transient state; and (2) they do not define causality on their qualitative model.This paper defines the “inherent causality” which requires “time reference.” With this causality, causal reasoning is carried out by verifying that any change is made by the causality. The verification is done by consumption of dt = + for each step. To make such causal reasoning possible, the base model (i.e., the dynamical model which describes causality from what makes the change to what is changed) is selected carefully. Also, the qualitative simulation algorithm utilizing the inherent causality is developed. The power of the causality and simulation algorithm is demonstrated on the examples of a buffer tank. AQUA, a knowledge acquisition tool for process diagnosis domain, is implemented using the qualitative simulation. AQUA uses the qualitative simulation in generating fault‐syndrome associations.