Diagnostic Dependency Model
- A single or multi-dimensional dependency model that
collectively represents the relationships between testable events and the
agents (functions or failure modes) responsible for those events. Because they
must abstractly and conditionally account for dependencies beyond simple
functionality, diagnostic models are sometimes difficult to correlate directly
to a specific drawing of a system, device or process.
Mode Diagnostic Model
-A diagnostic model whose agents are based
exclusively on failure space. Although a failure mode diagnostic model may
include elements based on negative failure space, it lacks the ability to
correlate members of the two spaces. Although failure mode diagnostic models
provide a useful link between FMECA analysis and run-time diagnostics, their
usefulness as a tool for influencing a diagnostic design is severely limited
(since failure mode diagnostic models cannot be developed until relatively late
in the design process, when specific failure modes have been identified).
Mode Diagnostic Dependency Model
- A failure mode diagnostic model that has been represented using a
single or multi-dimensional dependency model.
Diagnostic Dependency Model
- A functional diagnostic model that has been represented using a
single or multi-dimensional dependency model.
- A diagnostic model whose agents are based exclusively on function space.
Although a functional diagnostic model may include elements based on negative
function space, it lacks the ability to correlate members of the two spaces.
Functional diagnostic models are particularly useful as a tool for influencing
a diagnostic design during early design phases (since functional descriptions
of a design can be developed before the implementation specifics have been
worked out). Functional diagnostic models can then be supplemented with
lower-level data (sometimes imported directly from CAD/CAE databases) as it
becomes available. The biggest disadvantages of functional diagnostic models
are that they are not easily mapped to FMECA data and that they must sometimes
be translated into failure mode diagnostic models before they can be used to
implement run-time diagnostics.
- An extension of diagnostic dependency modeling that allows for the
representation of the relationships between components, functions and tests at
multiple levels of a design's hierarchy. Because the relationships between
higher-level (parent) and lower-level (child) functions are modeled, for
example, these models can be used to support hierarchical diagnostic inference
(for example, when a parent function is proven good, all of its child functions
can be inferred to be good; conversely, when all of a function's children have
been proven good, then the parent can be inferred to be good).
- An extension of diagnostic dependency modeling that allows the
inter-relationships between tests, functions and failures to be captured within
a single representation of a system, device or process. Although a hybrid
diagnostic model draws agents from both function and failure spaces, it is more
sophisticated than a classic diagnostic dependency model in that it also
represents the inter-relationships between functions and failure modes (rather
than only the relationships between these agents and tests). Because of this,
these models can be used to support hybrid diagnostic inference. Hybrid
Diagnostic Models embrace the advantages of both functional and failure mode
diagnostic models, allowing the same model to be utilized for early
diagnostic design influence, FMECA development/assessment, diagnostic
performance predictions, and run-time diagnostic development.
- A dependency model in which individual agents
(functions or failure modes) may appear in multiple first-order or
nth-order dependency statements in a diagnostic model (representing
different signals) with differing event dependencies. This approach, which does
not necessarily require a multi-dimensional dependency model, provides the
ability to trace signals through a path of components without involving every
failure mode in the path. Although the term Multi-Signal Modeling appeared in
the late 1990s, it represents a modeling approach that has been in use since
the inception of dependency modeling in the 1950s. The biggest drawback of this
approach is that multi-signal models are often time-consuming to develop and
consist of large amounts of low-level data that is not easy to modify
as the design changes. This effectively relegates modeling to a role of
diagnostic performance prediction (typically performed relatively late in the
design process), rather than early diagnostic design influence (which demands
models that can be updated iteratively with relative ease). One
alternative to multi-signal modeling is the more proactive approach of
Passive-Active Flow Modeling.
Model - A
model that represents elements of a system, device or process without
representing the relationships between those elements. Although easier to
develop than topological models, non-topological models (when used for
diagnostic applications) force the user to model test coverage explicitly.
Because of this, non-topological models are mostly useful for documenting
legacy or fully-developed diagnostic designs, since the lack of topology
renders these models more or less useless as an aid for determining test
coverage. Non-topological models are sometimes also used for modeling "black
box" devices, when engineering details are not available.Although a
non-topological model can be represented in dependency model format (so, for
example, the model could be utilized by a model-based diagnostic engineering
tool), the resulting model will consist of a set of unrelated first-order
dependency statements. In other words, there would be no upstream or downstream
relationships between the different elements in the model. Like topological
models, non-topological models are not diagnostic models in and of themselves,
since they contain no information about tests.
- An alternative to Multi-Signal Model that begins with a Topological
Model and then, by propagating input signals along the various signal flow
paths using active and passive propagation, generates a full representation of
signal propagation. When combined with Test Overlay Modeling, Passive-Active
Flow Modeling can generate the same complex dependency models that
are produced by Multi-Signal Modeling, yet without the time-consuming signal
definition task that renders most Multi-Signal Modeling efforts unsuitable for
diagnostic design influece. Moreover, because Passive-Active Flow Modeling
involves automatic signal propagation, it can be easily utilized with a variety
of graphic representational schemes. This means that the graphical
representation of a model can more closely resemble a schematic, management
diagram, or picture of the system, device or process—thereby facilitating
communication with engineers, managers, and customers/end users.
An extension of diagnostic models (dependency or otherwise), in which
predictive algorithms exist to support diagnosing failures before they occur.
- A simplistic dependency model in which the diagnostic model is
comprised exclusively of first-order dependency statements in which individual
agent (function or failure mode) dependencies are always associated with the
same first-order event dependencies. With this approach, signals
are always dependent upon all upstream agents; furthermore, the number of
signals of which each agent can be a dependency is constrained by the modeled
topology. Single-Signal Models are typically only used for trivial classroom
examples and can rarely be applied successfully to real-world applications.
Model - A model that
represents only connectivity and parts, usually imported from a CAD/CAE tool
using a net list or a transfer format such as EDIF. The pin-outs of parts are
usually identified, although their flow direction (input, output,
bidirectional) may not be. Also, power and ground pins are not always
enumerated within the structural model. Lacking signal flow, a structural model
is not a dependency model.
- A diagnostic dependency model containing an overlay of short-hand
test definitions upon a topological model. Each test definition (which contains
information such as the test location, monitored stimuli, test symmetry,
interference handling and exceptions) are applied as constraints upon the
signal flow represented within the Topological Model, resulting in a full-order
dependency statement for that test. The full set of dependency statements
derived in this manner collectively comprise a Diagnostic Dependency
Model. When combined with Passive-Active Flow Modeling, Test Overlay Modeling
can generate the same complex models produced by Multi-Signal Modeling—with a
fraction of the effort. Furthermore, if the Topological Model is supplemented
with information relating failure modes to their affected functions, then Test
Overlay Modeling can be used to generate a Hybrid Diagnostic Model. Test
Overlay Modeling is an extremely effective way of reducing the time needed to
develop and update detailed, low-level Diagnostic Dependency Models (thus
allowing these models to be feasibly employed within an iterative design
Model - A
model that supplements a structural model with information about signal flow
(both between components and within components). A high-quality
CAD/CAE import can often derive a topological model directly from engineering
data, provided that flow information or additional part libraries are available
upon which to create the flow. Although a topological model can be represented
in dependency model format, it is not in and of itself a diagnostic model,
since it contains no information about testing.