Federated Executable Architecture Technology is an enabling technology, supportive of the construction, execution and analysis of application-oriented simulations. Using HLA to link distributed simulations allows a hybrid system to be created that best simulates a particular scenario. Under control of a top-level Executable Architecture representation of a particular scenario or application, the various models of entities in the system interoperate to test, verify and validate the static architecture of the system. Various resolutions of models may be used throughout to provide appropriate simulation of doctrine and decisions as well as entities in the scenario. Messaging in the scenario between the entities and the decision swimlanes are modeled in appropriate federated networking simulators. As a whole, the ability to bring a static architecture to life through simulation allows optimization of the doctrine reflected in the architecture. We present the results of applying FEAT to simulation of a large-scale training exercise and show how it can be used to enhance the integration and composition of training events.
KEYWORDS: Wavelets, Neural networks, Signal processing, Wavelet transforms, Signal analyzers, Digital signal processing, Time-frequency analysis, Neurons, Fourier transforms, Prototyping
The analysis of gas turbine vibration is enhanced by the use of wavelet characterization and Wigner-Ville distribution processing to represent vibration features. The output of vibration sensors is digitized and the signal is processed by these means to identify signals associated with damage and progressive turbine wear. Wavelet processing provides fast transient detection useful in minimizing subsequent damage to turbine components through quick reaction. During turbine operation, short duration features appear, such as rotating stall conditions, that are well suited for detection with wavelet techniques. The Wigner-Ville distribution provides very accurate determination of vibration amplitudes in the nonstationary environment encountered in the use of gas turbines for vehicular propulsion. The Wigner-Ville distribution is described, and techniques for obtaining highly accurate amplitude information in the presence of noise and nonstationarity are presented. The wavelet transform is capable of making trade- offs between time and frequency resolutions, a property that makes it appropriate for the analysis for the analysis of nonstationary signals. Its ability to 'zoom in' on short lived high frequency phenomena is particularly attractive for the analysis of transients. Features of interest can be characterized form the evolution of the transform coefficients across distinct scales. Different types of wavelet transforms for an efficient time-frequency processing of the vibration signals are investigated. The resulting wavelet and Wigner features are used as inputs to a neural net which combine them with system health parameters. The result is a viable turbine monitor system, which can respond to long and short term events in a reliable and responsive manner.
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