Representation Of Process System Knowledge Through Component Constraint Descriptions


constraint satisfaction; Constraints; knowledge acquisition; knowledge representation; process systems; relations


Automated development of models for use in computer simulations of engineered systems (e.g. electronic, power, thermal, process systems, etc.) can represent a significant advantage to system designers and troubleshooters. The fact that most modern systems have been designed in Computer-Aided Design (CAD) environments represents a unique opportunity for automatically generating a model from the electronic representation of a system. Models generally require definition of the system structure (i.e. component connectivity) and of the behavioral description of its components. With some exceptions, determination of the system connectivity from a CAD representation is a relatively uncomplicated procedure. However, the assignment of a functional behavior to each component of the system depicted in the CAD representation is a significant problem. This is because behavioral information is usually not included in the CAD representation of the system, as it is not required by the typical users of the CAD graphic output. The overall issue addressed in this paper, therefore, is the determination of the correct behavioral attributes for the components making up the modeled system. This will be addressed through the identifcation and matching of system components to elements of an external base of generic component knowledge. The components' behavioral representation (i.e. transfer function) will be set equal to that of its matching element in this external database. The compatibility of a hard-to-identify system component with its (known) neighboring components can be used to shed some light on its identity. Component compatibility can also be used to determine correct connectivity when that defined by the CAD database is incorrect. This compatibility can be determined through the use of domain (system) knowledge, in the form of system theory and/or practice. The representation of this knowledge as a series of constraints is the focus of this paper. Verification of this technique using a testbed system is also reported here. © 1993.

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Engineering Applications of Artificial Intelligence





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43949170803 (Scopus)

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