Abstract: Machinery diagnostics and prognostics is an emerging engineering field that seeks to accurately determine the operational health of a machine without waiting for the machine to fail, performing maintenance that may not yet be required or, in the worst case, performing unnecessary maintenance that inadvertently causes other problems and hastens machine health deterioration. Accurate prediction of machine health (operability) enables operators to base machine maintenance on the machine's actual condition, in contrast to the common practice of time-based maintenance (e.g., perform maintenance every 100 hours). The “just-in-time” methodology of the machine health monitoring system (MHMS) of the present invention translates into significant cost savings by providing early warning of impending failures and thus reducing unanticipated catastrophic machine failures through preventative maintenance techniques (but no more or no less than is required) while simultaneously keeping false alarm rates low.

Abstract: A device and method for the real time optimization of scheduling for manufacturing and information transfer systems and similar applications. The device is a computer readable medium whose contents cause a computer system to generate an optimal solution to a scheduling problem in accordance with a method encoded on the medium. The method employs a filtering algorithm to schedule minimally-conflicting events. The remaining unscheduled events are partitioned into non-interactive sub-sets. Following partitioning, artificial intelligence is used to select one of a plurality of algorithms which is employed to provide an optimal scheduling solution for each sub-set of scheduling requests. The purpose of artificial intelligence is to recognize certain characteristics in request data comprising each sub-set of event scheduling requests and select an algorithm which is optimal for scheduling each particular sub-set.