Distributed Monitoring of Hybrid Systems: A model-directed approach

This paper presents an efficient online mode estimation algorithm for a class of sensor-rich, distributed embedded systems, the so-called hybrid systems. A central problem in distributed diagnosis of hybrid systems is efficiently monitoring and tracking mode transitions. Brute-force tracking algorithms incur cost exponential in the numbers of sensors and measurements over time and are impractical for sensor-rich systems. Our algorithm uses a model of system’s temporal discrete-event behavior such as a timed Petri net to generate a prior so as to focus distributed signal analysis on when and where to look for mode transition signatures of interest, drastically constraining the search for event combinations. The algorithm has been demonstrated for the online diagnosis of a hybrid system, the Xerox DC265 printer.