@Article{sdhm.2023.025989,
AUTHOR = {Wei Li, Benjian Zou, Yuxiang Luo, Ning Yang, Faye Zhang, Mingshun Jiang, Lei Jia},
TITLE = {Impact Damage Identification of Aluminum Alloy Reinforced Plate Based on GWO-ELM Algorithm},
JOURNAL = {Structural Durability \& Health Monitoring},
VOLUME = {17},
YEAR = {2023},
NUMBER = {6},
PAGES = {485--500},
URL = {http://www.techscience.com/sdhm/v17n6/54635},
ISSN = {1930-2991},
ABSTRACT = {As a critical structure of aerospace equipment, aluminum alloy stiffened plate will influence the stability of spacecraft in orbit and the normal operation of the system. In this study, a GWO-ELM algorithm-based impact damage
identification method is proposed for aluminum alloy stiffened panels to monitor and evaluate the damage condition of such stiffened panels of spacecraft. Firstly, together with numerical simulation, the experimental simulation to obtain the damage acoustic emission signals of aluminum alloy reinforced panels is performed, to establish
the damage data. Subsequently, the amplitude-frequency characteristics of impact damage signals are extracted
and put into an extreme learning machine (ELM) model to identify the impact location and damage degree,
and the Gray Wolf Optimization (GWO) algorithm is employed to update the weight parameters of the model.
Finally, experiments are conducted on the irregular aluminum alloy stiffened plate with the size of 2200 mm ×
500 mm × 10 mm, the identification accuracy of impact position and damage degree is 98.90% and 99.55% in
68 test areas, respectively. Comparative experiments with ELM and backpropagation neural networks (BPNN)
demonstrate that the impact damage identification of aluminum alloy stiffened plate based on GWO-ELM algorithm can serve as an effective way to monitor spacecraft structural damage.},
DOI = {10.32604/sdhm.2023.025989}
}