**Note:** A zipped up postscript version
of this paper is also available.

**Pieter J. Mosterman -
Gautam Biswas
Center for Intelligent Systems
Vanderbilt University
Nashville, TN 37235
U. S. A.**

Automated fault diagnosis in complex systems quickly detects and
isolates component failures. To this end, system models can be
effectively and efficiently exploited by mapping deviating measurement
values onto a functional description of the system. Functional
relations between measurements are then used in a *candidate generation*
stage to find all sets of possible deviating component parameters that
explain the measurement values.
Typically, this initial set of hypothesized faults contains
a large number of candidates and to isolate the true fault, future system
behavior is *predicted* from the system model for
each of the possible faults.
Continued *monitoring* of these predictions then
prunes the initial set of fault hypotheses till the true fault is identified.
This interaction of candidate generation, prediction, and monitoring
is referred to as *model based diagnosis*.
Given an underlying modeling formalism that is applicable
across physical domains (e.g., electrical, mechanical), the
generic diagnosis algorithms can be combined with a model of a specific system
to quickly generate a diagnosis engine.
If abrupt faults occur, fault detection and isolation (FDI) can be
efficiently performed based on *transients* in system behavior.
This requires a dynamic model of the system. To support compositionality,
topological models are well suited for diagnosis. Furthermore,
because diagnosis is basically a search process, to keep computational
complexity low, it is important for
diagnosis models to incorporate as many constraints as possible.
All of these requirements are elegantly addressed by the *bond graph*
modeling formalism which is based on energy exchange, *power*, between
system components. Therefore, it captures phenomena in
a number of physical domains,
e.g., as in electro-mechanical systems.
Moreover, bond graphs allow for algorithmic assignment of causality to
derive a set of dependency relations of the system. This paper
shows how the temporal aspects of these relations can be systematically
captured by a *temporal causal graph*. The use of bond graphs inherently
requires *conservation of energy* and *continuity of power* to
be satisfied, and, therefore, the derived temporal causal graph provides a
well constrained model. We show how this graph results in efficient
diagnosis of a complex, nonlinear, model of a secondary
cooling system in fast breeder reactors.

- 1 Introduction
- 2 Background
- 3 Bond Graphs for Diagnosis
- 4 The Temporal Causal Graph
- 5 Diagnosis System Implementation
- 6 The Liquid Sodium Cooling System
- 7 Conclusions
- References
- About this document ...

Tue Jul 15 11:26:35 CDT 1997