Characterizing discrete event timing relationships for fault monitoring of manufacturing systems

The timing and sequencing relationships of changes (events) in discrete sensors and actuators can be used to determine whether a manufacturing system is operating as expected. In this paper, we present a method of learning inter-event timing relationships using observations from a correctly operating system. The observed sample statistics characteristic of correct system operation are used to create a confidence space of possible timing relationships (acceptable delay intervals) of the underlying system. Given a relative cost of false alarms vs. missed detections, the timing relationships can be chosen to minimize the worst case total of the false alarm and missed detection costs over the confidence space