Search

A method based on entropy-based criteria is present to choose the optimal decomposition of Wavelet Packets Analysis (WPA) for damage detection in composite materials. The structural damage indexes constructed based on energy spectrum variation of the structural vibration responses decomposed using WPA before and after the occurrence of structural damage usually generate a complete binary tree to calculate its elements. Date mining is carried out in this paper by adoption entropy as the criteria to choose the optimal decomposition tree. In the decomposition process, only the sub-signals which contain main information of the original signal are decomposed to generate next level sub-signals. New damage index is constructed based on the optimal decomposition. Then the dimension of the damage index is reduced while still keeping its sensitive to damage. Whether Artificial Neural Network (ANN) or genetic algorithm (GA) is used in the further process of telling structural damage status from damage index, this reduction will make remarkable time saving.

Sensor data are the basis for health assessment of complex structural systems. Careful selection and logical layout of sensors is critical to enable the high reliability of system health assessment. This paper presents a methodology how to use a minimum number of sensors, and what locations of them should be placed, so that the voltage signals received from the sensor can be used to detect both presence and extent of damage. In this study, an optimization procedure is developed using Genetic Algorithm (GA) to determine the location of piezoelectric sensor for damage detection in a composite wingbox. A new damage index using all differences in voltage signals decomposed by wavelet transform is proposed. Results show that the proposed method is available at determining number and location of sensors for structural damage detection using piezoelectric patch sensors.

The paper presents an effective damage detection method of complex composite structures. It can be carried out through the experimental modal analysis of the damaged structure. The method using the improved Cross Modal Strain Energy (CMSE) technique and Niche GA has many advantages compared with other damage detection methods. The CMSE method can use any modes of the structure and the modes don’t need to be normalized or consistent in scale. The Niche GA improves the efficiency of the calculation and enhances the capacity of identifying structural damage localization. The model is the composite material airfoil case. The numerical results show that the method proposed in this paper is successful for damage detection of complex structures.

A vibration-based approach to detect crack damage in a cantilever composite wingbox is studied using the improved Hilbert-Huang Transform (HHT). The improved HHT is composed of HHT with Wavelet Packet Transform (WPT) and a simple but effective method for intrinsic mode function (IMF) selection. For different damage status, in order to obtain structural dynamic responses, which imply plentiful damage information, the composite wing boxes were excited by a contrived square wave signal. Then, the dynamic responses of intact wingbox and damaged wingbox are disposed using improved HHT. Finally, a feature index vector of structural damage, i.e. the ariation quantity of instantaneous energy, is constructed. The obtained results show that the proposed damage feature index vector is more sensitive to small damage than those in traditional signal processing.