With the extensive application of sensor technology, airlines accumulate a lot of flight data during fleet operations. Quick Access Recorder (QAR) is an important basis for aircraft state estimation and safety assessment, it records the operating status of various aircraft systems. However, the current application of QAR is mainly reflected in the investigation and verification of accidents, but it fails to apply such continuous real-time data to safety monitoring, risk prediction, and early warning. Therefore, this paper proposes a data-driven integrated safety risk warning model based on deep learning. Combined with the characteristics of QAR data and aircraft system failures, the failure mode and impact analysis (FMEA), cause chain analysis (CCA), long and short-term memory (LSTM), and other methods are used to describe the development of risks caused by system failures and predict the trend of flight parameters of each system, improve the ability to predict the risk and risk severity of the aviation system. Taking the Aircraft Refrigeration System (ARS) as the case object, the safety risk warning model is validated to calculate the risk level may face and find out the hidden hazard sources of civil aircraft operation in advance, to improve the safety level of civil aircraft operation

Modern civil aircraft is designed for a greater workload. While the volume of air traffic has grown exponentially over the past two decades, the total number of accidents has been flat or slightly higher than past averages. However, the fatality and property losses caused by aviation accidents are unbearable even if they happen only once. As reported by a Statistical summary from Boeing in 2019, nearly 40% of the deaths occurred during the final approach and landing. A deep learning-based model for accident-type prediction during approach and landing was established, which explores the implied association between accident types and accident factors from the risk factors affecting the occurrence of unsafe events. First, based on the investigation reports of civil aircraft fatal accidents in the recent 43 years, 20 common accident risk factors were extracted by the human factors analysis and classification system (HFACS) method. Implicit association of accident factors was excavated by association rules and 6 key factors were extracted from 20 factors. Back Propagation Neural Network (BPNN), Radial Basis Function Neural Network (RBFNN), and Elman Neural Network (ElmanNN) applicable to the classification were selected for multiple learning and training. The classification results showed that the comprehensive prediction accuracy of accident types reaches 86.7%, which can effectively determine the types of accidents that may occur during the approach and landing phases of civil aircraft, make emergency measures in advance, and incorporate key accident factors into the assessment scope of civil aircraft safety management system to ensure the aviation operation safety.

Purpose: The purpose of this paper is to identify the key influencing factors of aviation accidents and to predict the aviation accidents caused by the factors.Design/methodology/approach: This paper proposes an improved gray correlation analysis (IGCA) theory to make the relational analysis of aviation accidents and influencing factors and find out the critical causes of aviation accidents. The optimal varying weight combination model (OVW-CM) is constructed based on gradient boosted regression tree (GBRT), extreme gradient boosting (XGBoost) and support vector regression (SVR) to predict aviation accidents due to critical factors.Findings: The global aviation accident data from 1919 to 2020 is selected as the experimental data. The airplane, takeoff/landing and unexpected results are the leading causes of the aviation accidents based on IGCA. Then GBRT, XGBoost, SVR, equal-weight combination model (EQ-CM), variance-covariance combination model (VCW-CM) and OVW-CM are used to predict aviation accidents caused by airplane, takeoff/landing and unexpected results, respectively. The experimental results show that OVW-CM has a better prediction effect, and the prediction accuracy and stability are higher than other models.Originality/value: Unlike the traditional gray correlation analysis (GCA), IGCA weights the sample by distance analysis to more objectively reflect the degree of influence of different factors on aviation accidents. OVW-CM is built by minimizing the combined prediction error at sample points and assigns different weights to different individual models at different moments, which can make full use of the advantages of each model and has higher prediction accuracy. And the model parameters of GBRT, XGBoost and SVR are optimized by the particle swarm algorithm. The study can guide the analysis and prediction of aviation accidents and provide a scientific basis for aviation safety management.

The engine bleed air system (BAS) is one of the important systems for civil aircraft, and its operating state directly affects the operational safety of the aircraft. The effective risk warning of ...

The engine bleed air system (BAS) is one of the important systems for civil aircraft, and fault prediction of BAS is necessary to improve aircraft safety and the operator's profit. A dual-stage two-phase attention-based encoder-decoder (DSTP-ED) prediction model is proposed for BAS normal state estimation. Unlike traditional ED networks, the DSTP-ED combines spatial and temporal attention to better capture the spatiotemporal relationships to achieve higher prediction accuracy. Five data-driven algorithms, autoregressive integrated moving average (ARIMA), support vector regression (SVR), long short-term memory (LSTM), ED, and DSTP-ED, are applied to build prediction models for BAS. The comparison experiments show that the DSTP-ED model outperforms the other four data-driven models. An exponentially weighted moving average (EWMA) control chart is used as the evaluation criterion for the BAS failure warning. An empirical study based on Quick Access Recorder (QAR) data from Airbus A320 series aircraft demonstrates that the proposed method can effectively predict failures.

The aero-engine is the heart of an airplane. Predicting the remaining useful lifetime (RUL) of an aero-engine bears great significance, not only for improving the reliability and safety of the aero-engine but also for ensuring aircraft safety and performance. However, both issues, namely the selection of uncorrelated parameters for RUL estimation and the lack of a standard theoretical methodology for Health Index (HI) construction, inevitably impact the prediction accuracy. Here, we proposed an improved similarity-based RUL prediction method considering the degradation degree of multiple condition monitoring parameters for aero-engines. This method includes the improved minimum-redundancy maximum-relevancy approach for the quantitative selection of key parameters, and the similarity matching approach which takes into account the degradation degree of multiple parameters instead of constructing HI. The effectiveness of the proposed method was evaluated on turbine engine datasets. Experimental results show that (1) compared with other feature selection methods, the root mean square error (RMSE) of the proposed method is reduced by 12.3%; (2) compared with other RUL prediction methods, the RMSE of the proposed method is smaller than most methods but without a complicated training process. The results demonstrate that the proposed method achieves highly competitive prediction performance. By employing the proposed method, it is possible to significantly reduce the risk of engine failures, thereby improving safety and economic efficiency.