---
title: "Model-Based Monitoring of Dynamic Systems"
authors:
  - "Daniel Dvorak"
  - "Benjamin Kuipers"
doi: ""
url: "https://ijcai.org/proceedings/1989/198"
pdf: "https://ijcai.org/proceedings/1989/0198.pdf"
venue: "Proceedings of the Eleventh International Joint Conference on Artificial Intelligence"
year: 1989
pages: "1238-1242"
track: "Knowledge Representation"
keywords:
---

# Model-Based Monitoring of Dynamic Systems

Daniel Dvorak, Benjamin Kuipers

*Proceedings of the Eleventh International Joint Conference on Artificial Intelligence*, pages 1238-1242.

## Abstract

Industrial process plants such as chemical refineries and electric power generation are examples of continuous-variable dynamic systems (CVDS) whose operation is continuously monitored for abnormal behavior. CVDSs pose a challenging diagnostic problem in which values are continuous (not discrete), relatively few parameters are observable, parameter values keep changing, and diagnosis must be performed while the system operates. We present a novel method for monitoring CVDSs which exploits the system's dynamic behavior for diagnostic clues. The key techniques are: modeling the physical system with dynamic qualitative/ quantitative models , inducing diagnostic knowledge from qualitative simulations, continuously comparing observations against fault-model predictions, and incrementally creating and testing multiple-fault hypotheses. The important result is that the diagnosis is refined as the physical system's dynamic behavior is revealed over time .