Vibration Parameters for Impact Detection of Composite Panel: A Neural Network Based Approach

The need for reliable methodologies for structural monitoring is certainly a current line of research in many engineering sectors. The detection of the impact on composite materials is in fact a recent subject of study, aimed at safeguarding the mechanical integrity and improving the useful life of structural components. In such a context, the work deals with evaluation of the use of neural algorithms for localizing the position of the impacts on composite structures. Starting from FE (finite element) simulations, representative of the dynamic response of a CFRP (Carbon Fiber Reinforced Polymer) panel as a benchmark, the approach has been finally validated experimentally by modal parameters identification.

Single Output and Algebraic Modal Parameters Identification of a Wind Turbine Blade: Experimental Results

This paper describes the evaluation of a single output, online, and time domain modal parameters identification technique based on differential algebra and operational calculus. In addition, an analysis of the frequency response function (FRF) of the system is conducted in a specific set up, emulating its nominal or operational conditions to determine the influence of the non-linearities over the dynamic behavior of the system in those particular magnitudes of deformations; thus, this influence is quantified by a numerical index. This methodology is applied to a wind turbine blade submitted to wind tunnel experiments. The natural frequencies and modal damping ratios of six bending modes associated with the blade are estimated using real-time velocity measurements from one single point of the blade. A comparison with the usual impact hammer modal testing is performed to evaluate and establish the proposed approach’s main contributions. The developed modal parameter identification algorithms are implemented to run into a standard personal computer (PC) where the data acquisition system’s measurements are conditioned and processed. The results show the performance and the fast parametric estimation of the proposed algebraic identification approach.