Monitoring and diagnosis of continuous dynamic systems using semiquantitative simulation

Abstract

Operative diagnosis, or of a physical system in operation, is essential for systems that cannot be stopped every time an anomaly is detected, such as in the process industries, space missions, and medicine. Compared to maintenance where the system is off-line and arbitrary points can be probed, operative is limited mainly to sensor readings, and begins while the effects of a fault are still propagating. Symptoms change as the system's dynamic behavior unfolds. This research presents a design for and of deterministic continuous dynamic systems based on the paradigms of monitoring as model corroboration and diagnosis as model modification in which a semi-quantitative model of a physical system is simulated in synchrony with incoming sensor readings. When sensor readings disagree with predictions, variant models are created representing different fault hypotheses. These models are then simulated and either corroborated or refuted as new readings arrive. The set of models changes as new hypotheses are generated and as old hypotheses are exonerated. In contrast to methods that base on a snapshot of behavior, this simulation-based approach exploits the system's time-varying behavior for diagnostic clues and exploits the predictive power of the model to forewarn of imminent hazards. The design holds several other advantages over existing methods: (1) semiquantitative models provide greater expressive power for states of incomplete knowledge than differential equations, thus eliminating certain modeling compromises; (2) semiquantitative simulation generates guaranteed bounds on variables, thus providing dynamic alarm thresholds and thus fewer fault detection errors than with fixed-threshold alarms; (3) the guaranteed prediction of all valid behaviors eliminates the missing prediction bug in diagnosis; (4) the branching-time description of behavior permits recognition of all valid manifestations of a fault (and of interacting faults); (5) hypotheses based on predictive semiquantitative models are more informative because they show the values of unseen variables and can predict future consequences; and (6) fault detection degrades gracefully as multiple faults are diagnosed over time.