The use of Flow Models for Automated Plant Diagnosis
Generate an AI Snapshot to get a quick, structured summary of this paper.
A concise AI-generated summary of the paper will appear here once you click Generate AI Snapshot.
TL;DR
Automatic, computerized diagnosis can be based on several different search strategies, e.g. a search for a match between a pattern of measured data and some stored symptom patterns, or a search to locate a change in the system’s functional state with reference to a stored model of normal or specified plant state.
Abstract
Automatic, computerized diagnosis can be based on several different search strategies, e.g. a search for a match between a pattern of measured data and some stored symptom patterns, or a search to locate a change in the system's functional state with reference to a stored model of normal or specified plant state. The latter strategy has a number of basic advantages: it is independent of the prediction and analysis of specific faults and events; the reference for search, the normal state, can be derived from actual plant operation by the computer; the strategy can be based on invariate relations such as conservation laws; etc.