An Innovative Deep Architecture for Flight Safety Risk Assessment Based on Time Series Data

Abstract

With the development of the integration of aviation safety and artificial intelligence, research on the combination of risk assessment and artificial intelligence is particularly important in the field of risk management, but searching for an efficient and accurate risk assessment algorithm has become a challenge for the civil aviation industry. Therefore, an improved risk assessment algorithm (PS-AE-LSTM) based on long short-term memory network (LSTM) with autoencoder (AE) is proposed for the various supervised deep learning algorithms in flight safety that cannot adequately address the problem of the quality on risk level labels. Firstly, based on the normal distribution characteristics of flight data, a probability severity (PS) model is established to enhance the quality of risk assessment labels. Secondly, autoencoder is introduced to reconstruct the flight parameter data to improve the data quality. Finally, utilizing the time-series nature of flight data, a long and short-term memory network is used to classify the risk level and improve the accuracy of risk assessment. Thus, a risk assessment experiment was conducted to analyze a fleet landing phase dataset using the PS-AE-LSTM algorithm to assess the risk level associated with aircraft hard landing events. The results show that the proposed algorithm achieves an accuracy of 86.45% compared with seven baseline models and has excellent risk assessment capability. KEYWORDSSafety engineering; risk assessment; time series data; autoencoder; LSTM Nomenclature LSTM Long short-term memory network PS-AE-LSTM Improved risk assessment algorithm AE Autoencoder PS Probability severity VANETs Vehicular Ad-hoc networks