基于改进LSTM模型的航空安全预测方法研究

doi:10.12305/j.issn.1001-506X.2022.02.26

Abstract

Abstract:

Accurate aviation safety prediction is the premise of efficient safety early warning. Aviation accidents are not only caused by complex mechanism, but also have hysteresis effect, which makes it more difficult to excavate the time-series information of safety samples in depth. Based on this, an aviation safety forecast method based on improved long short-term memory (LSTM) model is proposed. Firstly, cause indicators are optimized based on the correlation coefficient heat maps. Then the model parameters of LSTM model are optimized by the step search and Adam algorithm. Finally, taking the accident data of a certain transport aircraft in 2019 as an example, select a variety of time-series forecasting model commonly used as a control. According to the experimental results, compared with the existing methods, the prediction error of the proposed method can be reduced by more than 28%, with excellent generalization ability and robustness.

Key words: aviation safety, neural network, long short-term memory (LSTM), multi-layers, multi-step prediction

CLC Number:

V328

Hang ZENG, Hongmei ZHANG, Bo REN, Lijie CUI, Jiangnan WU. Aviation safety prediction method research based on improved LSTM model[J]. Systems Engineering and Electronics, 2022, 44(2): 569-576.

Figures/Tables 13

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References 31

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周次	外来影响因素	设备设施因素	环境因素	管理因素	人为因素	强制报告事件
1	0.192 3	0.000 0	0.000 0	0.000 0	0.119 0	0.000 0
2	0.333 3	0.182 9	0.333 3	0.232 1	0.166 7	0.442 0
3	0.384 6	0.148 6	0.058 8	0.178 6	0.285 7	0.507 2
⋮	⋮	⋮	⋮	⋮	⋮	⋮
47	0.168 7	0.605 1	0.196 1	0.142 9	0.309 5	0.289 9
48	0.204 8	0.651 3	0.156 9	0.250 0	0.261 9	0.644 9

致因事件	相关性评价指标
致因事件	Pearson	Spearman	Kendall
外来影响因素	0.836	0.853	0.751
设备设施因素	0.728	0.706	0.478
环境因素	0.189	0.193	0.157
管理因素	0.124	0.135	0.108
人为因素	0.875	0.906	0.792

常变量	隐含节点数	批尺寸
常变量	隐含节点数	1	2	3	…	9	10
隐层数：1 步长：4 训练次数：200	3	9.852 0	9.167 6	9.003 3	…	8.458 0	8.424 9
	4	7.876 2	6.832 8	6.757 9	…	6.653 4	6.690 6
	5	9.052 8	7.073 2	5.904 0	…	3.326 4	3.028 5
	⋮	⋮	⋮	⋮	⋱	⋮	⋮
	11^*	3.878 2	1.661 3	1.535 7	…	1.476 9	1.541 8
	12	4.699 6	2.464 9	1.459 5	…	1.456 7	1.542 7

常变量	隐层数	批尺寸
常变量	隐层数	1	2	3	…	9	10
步长：4 训练次数：200	1	3.878 2	1.661 3	1.535 7	…	1.476 9	1.541 8
	2	4.027 5	1.481 8	1.271 1	…	1.463 7	1.501 0
	3	5.177 8	1.229 2^**	1.315 2	…	1.355 3	1.508 9
	⋮	⋮	⋮	⋮	⋱	⋮	⋮
	9	13.058 9	11.656 4	11.201 2	…	9.850 7	9.730 1
	10	13.893 8	12.335 2	9.787 1	…	6.212 8	6.144 7

模型	测试样本点
模型	样本点1	样本点2	样本点3	样本点4
ML-LSTM	0.028 4	0.145 4	0.031 2	0.072 1
GRU	0.143 6	0.229 5	0.005 3	0.143 6
RNN	0.143 4	0.119 0	0.094 3	0.143 4
LSTM	0.059 3	0.213 9	0.054 2	0.047 5
BP	0.010 9	0.405 0	0.248 7	0.261 4
RBF	0.110 3	0.114 7	0.350 9	0.073 2
ARIMA	0.048 2	0.243 0	0.197 3	0.086 8

Aviation safety prediction method research based on improved LSTM model

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