极限学习机
航天飞机热防护系统
热的
核(代数)
热保护
计算机科学
人工智能
工程类
机械工程
结构工程
材料科学
数学
人工神经网络
复合材料
物理
组合数学
气象学
作者
Chao Zhang,Xu Cheng,Tang Wuqiang,Yupeng Zhang,Chongcong Tao,Jinhao Qiu
标识
DOI:10.1177/1045389x241272985
摘要
The thermal protection structure (TPS) of the aircraft is highly susceptible to impact events from foreign objects, which may cause significant risk to the safe flight of the aircraft. This paper presents an impact localization method based on a hybrid kernel extreme learning machine (HKELM). The impact signal is firstly processed by the Short Term/Long Term Average (STA/LTA) ratio method and the time of arrival (TOA) of the guided wave is further extracted by the Akaike information criterion (AIC) method, which is used as the input of the neural network. The impact position is used as the output of the neural network to train the HKELM model. The hyperparameters of the HKELM are optimized by using the immune particle swarm algorithm (IPSO). Meanwhile, the local outlier factor (LOF) algorithm is used to detect the abnormal data, and a data reconstruction method based on the correlation coefficient is proposed to correct abnormal data. Finally, the localization results demonstrate the validity of the anomaly detection algorithm and the IPSO-HKELM network model.
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