GLOBAL TECHNOLOGY CONNECTION, INC. — Department of Defense SBIR Phase I: AF151-167

Description

ABSTRACT: Global Technology Connection, Inc. (GTC) in collaboration with their academic and industrial partners proposes to develop a software tool called Scheduling Algorithm for Maintenance using Prognostic Life Estimates (SAMPLE). This technology will aid maintainers in the creation of aircraft fleet maintenance schedules that incorporate prognostic information. Pre-planned work order packages will be generated by preventative, unplanned, and condition-based (prognostics) maintenance systems in SAMPLE. These maintenance work order packages will be scheduled depending on the desired aircraft mission schedule (from mission plans/schedules); user selected optimization parameters; available maintenance resources; prognostic attributes; work order, job, and resource rules & constraints; and the previous schedule. SAMPLE optimization parameters will be adjusted by the maintenance scheduler to generate various valid maintenance schedules for the work orders. The maintenance team will then select the most appropriate SAMPLE generated maintenance schedule to execute. Phase I will focus upon the development and validation testing of SAMPLE to demonstrate the feasibility of the approach. In Phase II, the system engineering documentation for SAMPLE will be finalized along with a prototype scheduling tool will be created and demonstrated. Phase III will concentrate on creating a commercial version of SAMPLE following extensive testing through a pilot program.; BENEFIT: Unplanned maintenance affects the performance of maintenance schedules causing increased cost and loss of aircraft availability. Incorporating prognostic information on critical systems allows one to anticipate future failures based on the monitored condition. Thus, prognostic information can help order needed parts and resources ahead of time so that maintenance is more efficient. It also helps aircraft mission planners update their schedules. There are many operators of large fleets of vehicles and equipment that can benefit from a maintenance scheduler that utilizes prognostic data. Companies such as UPS, FedEx, taxis, truck shipment companies, etc. could benefit from this improved approach to maintenance. Government services such as police could also benefit by scheduling patrols to better handle emergency situations. Computerized maintenance management software developers could also incorporate this solution into their products.