MRT specializes in a dynamic systems theory approach to vehicle health monitoring (VHM). By using the transient response of a system one can get a better assessment of structural health than by just looking a modes and mode shapes. Using Pareto optimization theory and multi-objective genetic algorithms, MRT's talented engineers have solved problems such as how to optimally place sensors to detect and estimate damage (two subtly different problems) along with optimal trade-offs of detection vs. cost and weight. They have also worked out how to quickly localize damage from data to aid in directed Non-Destructive Inspections (NDI).

Engineering Design Optimization
Using Pareto optimization theory and multi-objective genetic algorithms, MRT's engineers have developed a tool to aide in multi-objective engineering designs. Pareto optimal surfaces describe ALL optimal design solutions allowing the engineer to see the design trade-offs. This tool has been successfully used in placing sensors on a physical structure to minimize cost and weight while maximizing the ability to detect and estimate damage.

Over the past few years MRT has demonstrated their design techniques on several different platforms including wing spars, helicopter landing gears, and various other structural components of aircraft. We have been able to show that with proper design optimization that smaller amounts of damage can be detected sooner while using fewer sensors.

Results of detecting mass removal shown by optimal vs. heuristic sensors on a Black Hawk Drag Beam

Screen shot of results of Optimal Sensor Group (2 sensors) vs. the Heuristic Group (4 sensors)

Results of detecting cut progression and bolt loosening shown by optimal vs. heuristic sensors on a Kiowa Warrior Roof Strap Beam

Plot of Cut Progression Results on Kiowa Warrior Roof Strap Beam

Plot of Bolt Loosening Results on Kiowa Warrior Roof Strap Beam