Automatica (Journal of IFAC)
Readings in model-based diagnosis
Readings in model-based diagnosis
Detection of abrupt changes: theory and application
Detection of abrupt changes: theory and application
Studies in hybrid systems: modeling, analysis, and control
Studies in hybrid systems: modeling, analysis, and control
Diagnosis of large active systems
Artificial Intelligence
Fault Detection and Diagnosis in Distributed Systems: An Approach by Partially Stochastic Petri Nets
Discrete Event Dynamic Systems
Building Hybrid Observers for Complex Dynamic Systems Using Model Abstractions
HSCC '99 Proceedings of the Second International Workshop on Hybrid Systems: Computation and Control
A model-based approach to reactive self-configuring systems
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Diagnosis of continuous valued systems in transient operating regions
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
On-board Component Fault Detection and Isolation Using the Statistical Local Approach
Automatica (Journal of IFAC)
The glass cockpit [flight deck automation]
IEEE Spectrum
Diagnosis of Physical Systems with Hybrid Models Using Parametrized Causality
HSCC '01 Proceedings of the 4th International Workshop on Hybrid Systems: Computation and Control
Monitorability of stochastic dynamical systems
CAV'11 Proceedings of the 23rd international conference on Computer aided verification
Runtime monitoring of stochastic cyber-physical systems with hybrid state
RV'11 Proceedings of the Second international conference on Runtime verification
A game-theoretic approach to fault diagnosis and identification of hybrid systems
Theoretical Computer Science
Bridging control and artificial intelligence theories for diagnosis: A survey
Engineering Applications of Artificial Intelligence
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This paper reports on an on-going project to investigate techniques to diagnose complex dynamical systems that are modeled as hybrid systems. In particular, we examine continuous systems with embedded supervisory controllers that experience abrupt, partial or full failure of component devices. We cast the diagnosis problem as a model selection problem. To reduce the space of potential models under consideration, we exploit techniques from qualitative reasoning to conjecture an initial set of qualitative candidate diagnoses, which induce a smaller set of models. We refine these diagnoses using parameter estimation and model 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.