Safety is an eternal issue in the civil aviation transportation. Once a civil aviation accident occurs, it will cause great casualties and economic losses. In order to ensure the civil aviation safety, the hazard identification and prediction of civil aircraft should be effectively and accurately realized. The civil aircraft uses Aircraft Communications Addressing and Reporting System (ACARS) to interact with the ground during flight. The data generated by ACARS has a simple structure and strong timeliness. In view of the advantages of ACARS data, a hazard identification and prediction method based on support vector machine optimized by particle swarm optimization (PSO-SVM) and long short term memory (LSTM) neural networks which uses ACARS report as analysis data is proposed. First, in order to reduce the identification and prediction time cost, the SVM-based recursive feature elimination method with cross-validation algorithm (SVM-RFECV) is used to select the characteristic parameters. Then, the SVM optimized by PSO is used to identify hazard based on the selected parameters. According to the identification results, the LSTM is used to predict the trend of the selected parameters to realize hazard prediction. An A13 report of APU generated by ACARS is selected as analysis data for hazard identification and predication in this paper. The analysis results show that the proposed identification method based on PSO-SVM and SVM-RFECV has high identification speed and accuracy. The proposed prediction method based on LSTM has the best prediction performance. The proposed method can effectively identify hazards and accurately predict the trend of parameters to improve the safety of aircraft. INDEX TERMS Aviation safety, hazard identification, hazard prediction, deep learning, long short term memory (LSTM).

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The aerospace industry is striving to reduce the aircraft operating costs while maintaining required safety level. Emerging technologies such as the structural health monitoring to reduce long-term maintenance cost and increase aircraft availability are promoted by the manufacturers. To successfully integrate the structural health monitoring technology into the current maintenance process of modern commercial aviation, a clear definition of the structural-health-monitoring-based maintenance operational concept and the system level requirements is required. This article proposed a structural health monitoring operational concept and the associated maintenance cost modeling and risk assessment methods for the implementation of the structural health monitoring in commercial aviation industry. The developed methodology provides a tool to determine the optimal scheduled structural health monitoring inspection interval and repair decision thresholds for approved scheduled structural health monitoring task. A simulated case study is carried out to demonstrate the structural health monitoring operational concept and how an optimal maintenance strategy can be determined using the proposed methodology. Preliminary results show that the integration of the structural health monitoring into the existing maintenance process can reduce the maintenance cost compared to that of the current practice using the traditional Non-Destructive Evaluation (NDE) techniques while maintaining the risk below an acceptable level.

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