| Citation: | CUI Jianguo, XU Lingyu, YU Mingyue, et al. Health state prediction technique for aircraft wing based on MEMD and ELM[J]. Journal of Beijing University of Aeronautics and Astronautics, 2017, 43(8): 1501-1508. doi: 10.13700/j.bh.1001-5965.2016.0610(in Chinese)
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Composite materials are more and more widely used in the modern aircraft structure. In order to effectively forecast the health state of aircraft wing, the multivariate empirical mode decomposition (MEMD) energy entropy and extreme learning machine (ELM) model are introduced into prediction method of aircraft wing health state. A certain type of aircraft's composite wing box section is taken as the specific research object. Then we carried out fatigue load tests after impacting it. The original strain information of aircraft's composite wing box section was obtained by fiber optic sensor acquisition system, and the health state of aircraft's composite wing box section was represented. Then, the original strain information was decomposed by MEMD, and the energy entropy was extracted from the band signal which is decomposed with MEMD as the feature. The energy entropy was then fused by dynamic principal component analysis (DPCA). The energy entropy after fusion was taken to construct the ELM prediction model. And we forecasted the structural damage of a certain aircraft's composite wing box section. Experimental research shows that the method can achieve the prediction of aircraft wing health state effectively and has a very good application prospect.
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