An integrated model-based approach for real-time on-line diagnosis of complex systems



F. D. McKenzie; A. J. Gonzalez;R. Morris


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Abbreviated Journal Title

Eng. Appl. Artif. Intell.


automated diagnosis; model-based reasoning; model-based diagnosis; first-principles diagnosis; deep reasoning; Automation & Control Systems; Computer Science, Artificial Intelligence; Engineering, Multidisciplinary; Engineering, Electrical & Electronic


Model-based diagnostic programs have been shown to be useful in isolating unpredictable faults in various types of systems. Due to the complex nature of many of these systems, models used by these programs to represent monitored systems have traditionally imposed restrictions on domain representations. These restrictions can make it difficult (and often impossible) to model a domain whose behavior is global in nature. By global, is meant behavior that affects system variables in parts of the system not directly related to the component in question. Analog electrical circuits and hydraulic circuits are only a few examples of such global systems. Accurate modelling of the behavior of these global systems is very often essential for obtaining a correct diagnosis. In complex systems such as those typically found in the electrical power-distribution domain, global behavior can be observed when voltages and currents throughout an entire system are affected by local load fluctuations, transient disturbances, faults, or circuit re-configurations, even when these are in remote parts of the circuit. Traditional models used in diagnosis have not been able to easily reflect these global interactions, and as a result, monitoring and diagnostic capabilities of model-based systems dependent upon such models are significantly degraded. This paper presents an implementation that can correctly simulate power systems and other such complex systems by overcoming the problem of representing global behavior while preserving the diagnostic abilities of structure-function models in model-based reasoning methodologies. This paper describes the integration of robust models, within the conventional device-centered models. These robust models are mathematically accurate system models, normally used in quantitative simulation for the purpose of system analysis. If used within the conventional device-centered models, they can provide the functionality needed in a structure-function model-based diagnostic paradigm, and therefore eliminate the problem of representing global behaviors in diagnosis. This paper further describes a conflict-oriented diagnostic technique used in conjunction with robust models to obtain real-time on-line FDIR (Fault Diagnosis, Isolation, and Recovery). (C) 1998 Elsevier Science Ltd. All rights reserved.

Journal Title

Engineering Applications of Artificial Intelligence





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