Dr. Mohammad Noori, professor of mechanical engineering, and his colleagues have recently published the following journal paper in the Journal of Structural Health Monitoring, the leading Journal in this field:
Ahmed Silik, Mohammad Noori, Wael A. Altabey, Ji Dang, Ramin Ghiasi, Zhishen Wu, “Optimum wavelet selection for nonparametric analysis toward structural health monitoring for processing big data from sensor network: A comparative study,” J. Structural Health Monitoring, 2022, Vol. 21 (3), pp 803-825.
Journal Paper Abstract
A critical problem encountered in structural health monitoring of civil engineering structures, and other structures such as mechanical or aircraft structures, is how to convincingly analyze the nonstationary data that is coming online, how to reduce the high-dimensional features, and how to extract informative features associated with damage to infer structural conditions. Wavelet transform among other techniques has proven to be an effective technique for processing and analyzing nonstationary data due to its unique characteristics. However, the biggest challenge frequently encountered in assuring the effectiveness of wavelet transform in analyzing massive nonstationary data from civil engineering structures, and in structural health diagnosis, is how to select the right wavelet. The question of which wavelet function is appropriate for processing and analyzing the nonstationary data in civil engineering structures has not been clearly addressed, and no clear guidelines or rules have been reported in the literature to show how the right wavelet is chosen. Therefore, this study aims to address an important question in this regard by proposing a new framework for choosing a proper wavelet that can be customized for massive nonstationary data analysis, disturbances separation, and extraction of informative features associated with damage.
