Automated wavelet-based damage identification in sandwich structures using modal curvatures

Recently, damage assessment of composite structures being in operation has been one of crucial problems in industries such as aircraft, aerospace, automotive, etc. Following this, rapid development of non-destructive testing methods has been observed over the last decades. One of the promising approaches is vibration-based one, which in general is based on identification of a damage using singularities in modal shapes of vibration. The identification procedure requires application of advanced signal processing techniques, where the wavelet transform is used the most commonly. Due to the high sensitivity of wavelet-based approach to changes of parameters of an analysis, e.g. a type of applied wavelet and its order, it was decided to develop an algorithm, which allows automating this process by using multi-objective meta-optimization of the mentioned parameters. The presented approach allows selecting optimal parameters for wavelet-based damage identification procedure without neither a priori knowledge on values of wavelet parameters, nor the parameters of an optimization algorithm. The proposed approach was tested on composite sandwich structures with damaged core as well as damaged face sheets. Obtained results confirm the effectiveness of automated damage identification mainly in the context of the high convergence to the optimal solution

Analysis of the Vibration Signal Using Time-Frequency Methods

Condition monitoring (CM) is an essential technique used for monitoring various systems and system components and is supplemented with other techniques, such as video monitoring. Due to the fact that the acceleration signal is suitable for acquisition and is of non-stationary nature, CM is used to monitor various parameters of a monitored system. In order to process the acquired vibration signal and draw valid conclusions a time-frequency analysis has to be performed. In this paper, the advantage of the wavelet application is shown with an example and manifolds of wavelets reported in this application are reviewed. The case study implies that the optimal wavelets are db8 and db7 and the optimal level of decomposition is the fifth level