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  • Aerospace systems are expected to remain in service well beyond their designed life. Consequently, maintenance is an important issue. A novel method of implementing artificial neural networks and acoustic emission sensors to form a structural health monitoring (SHM) system for aerospace inspection routines was the focus of this research. Simple structural elements, consisting of flat aluminum.
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Mechanical stress creates electrical potentials on the walls of the sample. Electrical voltage is a measurable quantity for such effects and generally this is created by redistribution of electric charges, piezoelectric phenomena and by friction during crack walls sliding. There are two sources of acoustic emission events: tensile and shearing processes. Acoustic emission events appear either when new cracks are generated or by shearing process in existing cracks.

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We suppose, that electric charge redistribution is a main effect, which is manifested in our experiments. Electric charge redistribution is related to time dependent electric dipole moment and then electromagnetic field must be generated. Measured voltage has two components, DC and AC given by crack walls vibration and mechanical parameters of measured sample. High frequency component is of the order form 1 to 6 MHz and give information on crack length. We estimate the length of the crack of the order of 1 mm. Sensitivity of the value of electrical potential on crack orientation with respect to parallel plate capacitor can be used to study orientation of sample's layers.

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Low frequency component below 500 kHz give information on mechanical vibration spectrum and this component is correlated to acoustic emission signals. For our samples, this frequency was of the order of 100 kHz. The second time constant, which is given by parameters of mechanical vibrations and electrical conductivity is dependent on humidity.