Product design and diagnosis are, today, worlds apart. Despite
strong areas of overlap at the ontological level, traditional
design process theory and practice does not recognize diagnosis
as a part of the modeling process chain; neither do diagnosis
knowledge engineering processes reference design modeling tasks
as a source of knowledge acquisition. This paper presents the
DAEDALUS knowledge engineering framework as a methodology
for integrating design and diagnosis tasks, models, and modeling
environments around a common Domain Ontology and Product Models
Library. The approach organizes domain knowledge around the
execution of a set of tasks in an enterprise product engineering
task workflow. Each task employs a Task Application which uses
a customized subset of the Domain Ontology—the Task
Ontology—to construct a graphical Product Model. The Ontology
is used to populate the models with relevant concepts (variables)
and relations (relationships), thus serving as a concept
dictionary-style mechanism for knowledge sharing and reuse across
the different Task Applications. For inferencing, each task
employs a local Problem-solving Method (PSM), and a Model-PSM
Mapping, which operate on the local Product Model to produce
reasoning outcomes. The use of a common Domain Ontology across
tasks and models facilitates semantic consistency of variables
and relations in constructing Bayesian networks for design and
diagnosis.
The approach is motivated by inefficiencies encountered in
cleanly exchanging and integrating design FMEA and diagnosis
models. Demonstration software under development is intended
to illustrate how the DAEDALUS framework can be applied
to knowledge sharing and exchange between Bayesian network-based
design FMEA and diagnosis modeling tasks. Anticipated limitations
of the DAEDALUS methodology are discussed, as is its
relationship to Tomiyama's Knowledge Intensive Engineering
Framework (KIEF). DAEDALUS is grounded in formal knowledge
engineering principles and methodologies established during
the past decade. Finally, the framework is presented as one
possible approach for improved integration of generalized design
and diagnostic modeling and knowledge exchange.