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This paper describes a methodology that has been developed to apply basic concepts of pattern recognition to isolate “events” in any type of time history data. The results obtained from this methodology can be used for a variety of engineering applications. In this study, it has been applied to estimate and compare the cumulative structural fatigue damage from single bump excitations versus resonance in Class 8 tractors based on consumer highway data. Using the basic concepts of pattern recognition, which include statistical methods based on correlation functions, windowing techniques and root mean square values, a similarity search has been performed to extract and classify known consequential time history traces (events) from the set of acquired data. The advantage of this model is seen in extracting events whose exact time traces are not known.

A methodology has been developed to generate military vehicle driving cycles for use in vehicle simulation models. This methodology is based upon the mission profile for a vehicle, which is typically given within a vehicle's specifications and lists the types of terrains that the vehicle is likely to encounter. A simplistic vehicle powertrain and road load model and the Bekker vehicle-soil interaction model are used to estimate the vehicle performance over each type of terrain. Two types of driving cycles are generated within a Graphical User Interface developed within MATLAB using the results of the vehicle models: Linear modes driving cycles, and Real-world driving cycles.