Reference: McIlraith, S. A.; Biswas, G.; Clancy, D.; & Gupta, V. Towards Diagnosing Hybrid Systems. Knowledge Systems Laboratory, February, 1999.
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.
Notes: Working Notes of the AAAI 1999 Spring Symposium on Hybrid Systems and AI, 1999.
Full paper available as ps.