Towards Diagnosing Hybrid Systems.
Sheila McIlraith, Gautam Biswas, Daniel Clancy, Vineet Gupta.
Abstract
This paper reports on the findings of an on-going project to
investigate techniques to diagnose complex dynamic systems that are
modeled as hybrid systems. In particular, we examine continuous
systems with embedded supervisory controllers which experience abrupt,
partial or full failure of component devices. The problem we address
is: given a hybrid model of system behavior, a history of executed
controller actions, and a history of observations, including an
observation of behavior that is aberrant relative to the model of
expected behavior, determine what fault occurred to have caused the
aberrant behavior. Determining a diagnosis can be cast as a search
problem. Unfortunately, the search space is extremely large. To
reduce search space size and to identify an initial set of candidate
diagnoses, we propose to extend techniques originally applied to
qualitative diagnosis of continuous systems. We refine these
diagnoses using parameter estimation and data fitting techniques. As
a motivating case study, we have examined the problem of diagnosing
NASA's Sprint AERCam, a small spherical robotic camera unit with 12
thrusters that enable both linear and rotational motion.
@inproceedings{aercam-dx99,
author= "Sheila McIlraith and Gautam Biswas and Daniel Clancy and Vineet Gupta",
title= "Towards Diagnosing Hybrid Systems",
booktitle= "Proceedings of the 10th International Workshop on Principles of Diagnosis",
month= "May",
year= 1999
}
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