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An Innovative Deep Architecture for Aircraft Hard Landing Prediction Based on Time-Series Sensor Data

Tong, C. ; Yin, X. ; Li, J. ; Zhu, T. ; Lv, R. ; Sun, L. ; Rodrigues, J. R.

Applied Soft Computing Journal Vol. 73, Nº -, pp. 344 - 349, December, 2018.

ISSN (print): 1568-4946
ISSN (online):

Journal Impact Factor: 2,810 (in 2014)

Digital Object Identifier: 10.1016/j.asoc.2018.07.061

Abstract
This paper proposes an innovative deep architecture for aircraft hard landing prediction based on Quick Access Record (QAR) data. In the field of industrial IoT, the IoT devices collect IoT data and send these data to the open IoT cloud platform to process and analyze. The prediction of aircraft hard landing is one kind of typical IoT application in aviation field. Firstly, 15 most relevant landing sensor data have been chosen from 260 parameters according to the theory of both aeronautics and feature engineering. Secondly, a deep prediction model based on Long Short-Term Memory (LSTM) have been developed to predict hard landing incidents using the above-mentioned selected sensor data. And then, we adjust the model structure and conduct contrastive experiments. Finally, we use Mean Square Error (MSE) as the evaluation criteria to select the most optimal model. Experimental results prove its better performance with higher prediction accuracy on QAR datasets compared with the state-of-the-art, indicating that this model is effective and accurate for hard landing prediction, which helps to guarantee passengers’ safety and reduce the incidence of landing accidents. Besides, the proposed work is conducive to making an innovation for building and developing the industrial IoT systems in aviation field.