Impact monitoring of smart composite laminates using neural network and wavelet analysis

Low-velocity impact damage is a major concern in the design of structures made of advanced laminated composites, because such damage is mostly hidden inside the laminates and cannot be detected by visual inspection. It is necessary to develop the impact monitoring techniques providing on-line diagnostics of smart composite structures susceptible to impacts. In this paper, we discuss the process for impact location detection in which the generated acoustic signals are detected by PZT using the improved neural network paradigms. To improve the accuracy and reliability of a neural network based impact identification method, the Levenberg-Marquardt algorithm and the generalization methods were applied. This study concentrates not only on the determination of the location of impacts from sensor data, but also the implementation of time-frequency analysis such as the Wavelet Transform (WT) to measure the characteristic frequencies of acoustic emission waves for the determination of the occurrence and the estimation of impact damage.