Diagnosis of Physical Systems With Hybrid Models Using Parametrized Causality
Pieter J. Mosterman
Institute for Robotics and Mechatronics
DLR Oberpfaffenhofen
Abstract
Efficient algorithms exist for fault detection and isolation of physical
systems based on functional redundancy. In a qualitative approach,
this redundancy can be captured by a temporal causal graph (TCG),
a directed graph that may include temporal information.
However, in a detailed continuous model,
time constants may be present that are beyond the bandwidth
of the data acquisition system, which leads to incorrect fault
isolation because of a difference in observed and modeled behavior.
To solve this, the modeled time constants can be taken to be
infinitely small, which results in a model with mixed continuous/discrete,
hybrid behavior that is difficult to analyze because the causality
of the directed graph may change.
In this paper, to avoid the combinatorial explosion when using
a bank of TCGs in parallel, causal paths are parametrized by the state of
local switches. The result is a hybrid model that produces
parametrized predictions that can be efficiently
matched against observed behavior.
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