An integrated impact localization method for thermal protection structure based on hybrid kernel extreme learning machine

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.