Sequential window diagnoser for discrete-event systems under unreliable observations

This paper addresses the issue of counting the occurrence of special events in the framework of partially-observed discrete-event dynamical systems (DEDS). Developed diagnosers referred to as sequential window diagnosers (SWDs) utilize the stochastic diagnoser probability transition matrices developed in along with a resetting mechanism that allows on-line monitoring of special event occurrences. To illustrate their performance, the SWDs are applied to detect and count the occurrence of special events in a particular DEDS. Results show that SWDs are able to accurately track the number of times special events occur.