Meta-Data Mining for Optimized Aircraft Repair and Overhaul
ABSTRACT: The aim of this project is the development of advanced software modeling tools for data mining, maintenance support, and structural health monitoring prognostics. The project will develop new modeling, optimization tools and algorithm concepts that provide database search and correlations facilitating intelligent decision making processes for maintenance, repair and overhaul work practices and schedules. Ultimately, such a support tool will act upon current databases, meta-data and repair practices to arrive at considerable personnel, parts and other resources savings and shorter repair time horizons within the maintenance, repair, and overhaul (MRO) environment. An aircraft maintenance and repair work scope optimizer, as a decision support tool, will utilize dynamic data and meta-data information and knowledge to provide the repair work force with a daily work package that accommodates contingencies via dynamic re-planning. Such decision support tool will be orderly, repeatable and be tightly controlled. The key experimental and research results developed in the Phase I base effort have demonstrated the requested utility and effectiveness of developing algorithms and multiple concept reasoning modules which are robust enough to independently organize and analyze textural narratives and maintenance documents to a high level of accuracy. Complex, non-linear conceptual associations and links discovered within hundreds of thousands of independent text maintenance documents demonstrate the benefits of using advanced AI techniques to identify similarity groupings and common maintenance associations within a single, dynamic information repository, called the"INFORMATION CUBE". BENEFIT: Such a tool would have high value for data mining and maintenance scheduling of high value commercial items such as aircraft, bridges, vehicles, ships and buildings, and data mining of high value information such as medical records.
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