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Ingeniería
automovilística |
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El tratamiento de señales mediante wavelets es una de
las técnicas más modernas y potentes con un amplio rango de
aplicaciones. En particular se han demostrado numerosas utilidades
relacionadas con la ingeniería automovilística. El análisis de
vibraciones de motores, de mediciones de flujo de gas, sistemas de
escape, etc., resultan en muchos casos adecuados utilizando la
transformada wavelet donde otras técnicas fallan. Esta nueva herramienta
está siendo desarrollada durante los últimos años con diversos
resultados que se exponen en las revistas de investigación de ingeniería
mecánica de mayor impacto internacional. Extraemos a continuación
algunas referencias al respecto:
“The Discrete Wavelet Transform is a new way to analyze gas flow measurements in engine cylinders...” (Wiktorsson et alt., Wavelet analysis of in-cylinder LDV velocity measurements, The 1996 International Fall Fuels and Lubricants Meeting). “The Fast Wavelet Transform is a powerful new tool which can be used for vibration analysis and condition monitoring of advanced rotating machinery...” (Bonel y Nikolajsen, Introduction to harmonic wavelet analysis of machine vibrations, American Soc. of Mechanical Eng., 1997). “A new method of machinery fault diagnosis based on wavelet analysis is presented...This method has been applied to the detection of diesel engine malfunctions...” (Liu y Ling, On the selection of informative wavelets for machinery diagnosis, Mechanical Systems and Signal Processing, 1999). “The vibration signals of a machine always carry the dynamic information of the machine...Wavelet analysis is an effective tool for signal processing and feature extraction...” (Lin y Liangsheng, Feature extraction based on Morlet wavelet and its application for mechanical fault diagnosis, Journal of sound and vibration, 2000). “...an intelligent signal analysis system employing the wavelet transformation in the solution of vehicle engine diagnosis problems...” (Guo, Grossman y Murphey (Ford Motor Co, Adv Diagnost Design, Michigan, USA), Automotive signal diagnostics using wavelets and machine learning, IEEE Transactions on vehicular technology, 2000). |