One of the most sensitive problems regarding the application of SHM (Structural Health Monitoring) is found in the
aeronautical segment. This field presents the necessity of monitoring small structural changes representing damage, due
both to economic aspects and safety. In this contribution two helicopter blade structures (pertaining to a civil and a
military helicopter) are studied. In both cases, two types of damage are inserted, namely holes and cracks. Through the
impedance-based structural health monitoring method, an identification procedure using cluster analysis techniques was
performed aiming at distinguishing these two types of damage. Then, a meta-model based on a probabilistic neural
network was built for fault position identification.
In the last few years both the fundamentals and the materials have significantly changed in the design of engineering
structures. In space structures, for example, metallic components have been intensely replaced by composite and fiber
made ones to reduce weight and increase transportation and assembling skills. Impedance-based Structural Health
Monitoring is a major concern in this context because different types and categories of damage can affect various areas
along the structure. The interpretation of damage signatures is an important challenge to be overcome. As a
consequence, erroneous damage identification is quite common. This contribution focus on damage prediction in a
tubular space structure by using a methodology that is able to reduce the possibility of misinterpretation in the
monitoring procedure. For this aim, an optimization technique using genetic algorithms is applied to the complete
damage signature to determine the best frequency range to be investigated in each problem. Then, a meta-model is built
to characterize damage in the structure.
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