Fuzzy Similarity Classifier as Damage Index: Temperature Effect and CompensationReportar como inadecuado




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1 UPC - Technical University of Catalonia 2 UC San Diego - University of California San Diego

Abstract : The effects of ambient operational temperature variability on the measured dynamics response of structures have been addressed in several studies. It is intuitive that temperature variation may change the material-geometric properties or boundary conditions of a structure and therefore may affect the damage detection performance. Then we consider the ability of a Fuzzy similarity classifier as a feature when the temperature is changing, it will be shown that temperature change might have more significant effect rather than the simulated damage on this feature, which leads to false positive decisions. Therefore, it is vital to compensate the effect of temperature to achieve a desirable result. To do this, the temperature effect is compensated and it is shown the compensation increases the performance of damage detection using the Fuzzy similarity index. To support claims mentioned above, this work involves experiments with composite plate equipped with PZT transducers. To simulate the effect of temperature the specimen is subjected to temperature change between -25C and 60C.

Keywords : Normalization Feature extraction Pattern recognition





Autor: Fahit Gharibnezhad - Luis Eduardo Mujica - Jose Rodellar - Michael Todd -

Fuente: https://hal.archives-ouvertes.fr/



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