基于LightGBM-SHAP的民机硬着陆可解释预测

doi:10.16265/j.cnki.issn1003-3033.2024.10.1123

中国安全科学学报 ›› 2024, Vol. 34 ›› Issue (10): 134-142.doi: 10.16265/j.cnki.issn1003-3033.2024.10.1123

基于LightGBM-SHAP的民机硬着陆可解释预测

肖国松¹^,²(), 刘嘉琛¹^,³, 张元珊⁴, 董磊¹^,²^,^**, 陈曦¹^,²

¹ 中国民航大学民航航空器适航审定技术重点实验室,天津 300300
² 中国民航大学科技创新研究院,天津 300300
³ 中国民航大学安全科学与工程学院, 天津 300300
⁴ 中国商飞民用飞机试飞中心, 上海 201323

收稿日期:2024-06-10 修回日期:2024-08-15 出版日期:2024-10-28
通信作者:
^** 董磊(1983—),男,天津人,博士,副研究员,主要从事民机安全性评估与适航审定技术等方面的研究。E-mail:l-dong@cauc.edu.cn。
作者简介:
肖国松 (1982—),男,湖南衡阳人,硕士,实验师,主要从事航空器适航审定技术、航空发动机故障诊断及预测等方面的研究。E-mail: gsxiao@cauc.edu.cn。
陈曦，助理研究员
基金资助:
中央高校基本科研业务费(3122024037); 民用航空器适航审定技术重点实验室开放基金资助(SH2023101701)

Explainable prediction for hard landing of civil aircraft based on LightGBM-SHAP

XIAO Guosong¹^,²(), LIU Jiachen¹^,³, ZHANG Yuanshan⁴, DONG Lei¹^,²^,^**, CHEN Xi¹^,²

¹ Key Laboratory of Civil Aircraft Airworthiness Technology, Civil Aviation University of China, Tianjin 300300, China
² Science and Technology Innovation Research Institute, Civil Aviation University of China, Tianjin 300300, China
³ College of Safety Science and Engineering, Civil Aviation University of China, Tianjin 300300, China
⁴ COMAC Flight Test Center, Shanghai 201323, China

Received:2024-06-10 Revised:2024-08-15 Published:2024-10-28

摘要/Abstract

摘要：

为预防民用飞机的硬着陆超限事件,首先,收集包含动力学变量、系统性能和其他工程参数的机载快速存取记录器(QAR)数据,开展机场航段聚类、样本平衡、统计特征提取等数据处理活动;然后,基于轻量级梯度提升机(LightGBM)模型预测民机硬着陆事件,并与极限梯度提升(XGBoost)、决策树(DT)、长短期记忆网络(LSTM)模型进行综合对比;最后,利用Shapley可加性解释(SHAP)算法进一步分析硬着陆事件的致因机制及各飞行参数特征对模型预测结果的影响。结果表明: 所提方法不仅显示出良好的硬着陆事件预测性能,准确率、正确率和召回率分别达到99%,92%和88%,还可针对具体航段对硬着陆预测模型的决策过程提供定量的、可视化的解释信息。

关键词: 轻量级梯度提升机(LightGBM), 民用飞机, 硬着陆, 快速存取记录器(QAR)数据, 机器学习, 可解释

Abstract:

In order to prevent hard landing overrun events of civil aircraft, first, data including kinematics, system performance and other engineering parameters was collected from QAR. Then QAR data processing activities such as the airport segment clustering, sample balancing and statistical feature extraction were carried out. Subsequently, LightGBM model was used to predict the hard landing events of civil aircraft, and compared with extreme gradient boosting (XGBoost), decision tree (DT) and long short-term memory (LSTM) models. Finally, the shapley additive explanation (SHAP) algorithm was employed to identify the causal mechanisms of hard landing events and to analyze the impact of various flight parameters on the model's prediction results. The result demonstrates that the proposed model not only exhibits high accuracy and precision in predicting hard landing events (accuracy, correctness and recall reaching 99%, 92% and 88%, respectively), but also provides quantitative and visual explanation information for the decision-making process of hard landing prediction for specific flight segments.

Key words: lightweight gradient boosting machine (LightGBM), civil aircraft, hard landing, quick access recorder (QAR) data, machine learning, explainable

中图分类号:

X949

肖国松, 刘嘉琛, 张元珊, 董磊, 陈曦. 基于LightGBM-SHAP的民机硬着陆可解释预测[J]. 中国安全科学学报, 2024, 34(10): 134-142.

XIAO Guosong, LIU Jiachen, ZHANG Yuanshan, DONG Lei, CHEN Xi. Explainable prediction for hard landing of civil aircraft based on LightGBM-SHAP[J]. China Safety Science Journal, 2024, 34(10): 134-142.

图/表 16

图1

表1

表2

着陆机场的K-均值航段聚类算法

输入:数据集 $D = {u 1, u 2, … u N}$ ,其中, $u n$ 表示第n个航段的着陆位置,航段(样本)数量为N
从样本集中随机选择一个航段作为初始质心 $β 1$ 令 $C 1 = {x 1}$ for $j = 2,3, …, N$ 计算航段 $u j$ 到每个质心 $μ i$ 的距离: $d j i = ‖ u j - μ i ‖ 2$ ; if最近距离 $a r g m i n i ∈ {1,2, …, d j i}$ > 4.8 km then 形成一个新的簇 $C s = {u j}$ ; else 将航段 $u j$ 分配到距离最近的簇中; end if end for for $i = 1,2, …, d i j$
计算簇中所有样本的均值: $β' i = 1 \| C i \| ∑ u ∈ C i u i$ ; 将质心 $β i$ 更新为 $β' i$ ; end for 直到所有航段都被分配到相应的簇

表2

图2

图3

图4

表3

表4

表5

模型评价指标及含义

评价指标	计算公式	符号含义
A	$T P + T N T P + T N + F P + F N$	$T P$ 为硬着陆预测正确的数量; $T N$ 为正常着陆预测正确的数量; $F P$ 为正常着陆被错误预测为硬着陆的数量; $F N$ 为硬着陆被错误预测为正常着陆的数量;P_h为硬着陆预测结果的概率;P_n为正常着陆预测结果的概率
P	$T P T P + F P$
R	$T P T P + F N$
F₁分数	$2 × P × R P + R$
AUC	P (P_h> P_n)

表5

表6

图5

图6

图7

图8

表7

图9

参考文献 22

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类型	参数	采样率/ (次·s^-1)
动力学参数	计算空速(Computed Airspeed, CAS)/kn	4
	垂直加速度(Vertical Acceleration, VRTG)/g	8
	下降率(Altitude Rate, ALTR)/ (ft·min^-1)	4
	偏航角(Drift Angle, DA)/(°)	4
	攻角(Angle Of Attack, AOA)/(°)	4
	气压矫正高度(Baro correct Altitude, BAL)/ft	4
	磁航向(Magnetic Heading, MH)/(°)	4
	横向加速度(Cross Track Acceleration, CTAC)/g	16
操纵参数	主驾驶杆位置(Control Column Position Capt, CCPC)/Counts	2
	最大选择高度(Selected Altitude, ALTS)/ft	1
	选择垂直速度(Selected Vertical Speed, VSPS)/(ft·min^-1)	1
	选择马赫数(Selected Mach, MNS)	1
	方向舵踏板位置(Rudder Pedal Position, RUDP)	2
	推力手柄角度(Power Lever Angle, PLA)/(°)	4
环境参数	风向(Wind Direction, WD)/(°)	4
环境参数	风速(Wind Speed, WS)/kn	4
配置参数	扰流板位置(Roll Spoiler, SPL)/(°)	1
	方向舵位置(Rudder Position, RUDD)/(°)	2
	襟翼位置(T.E. Flap Position, FLAP)	1
	副翼位置(Aileron position, AIL)/(°)	1
	俯仰安定面位置(Pitch Trim Position, PTRM)/(°)	1

参数	max	min	Mean	Median	VAR	STD
AIL	86.621 19	78.131 12	83.880 15	83.716 16	1.745 762	3.047 684
AOA	2.636 699 9	-4.790 007 6	-2.576 91	-2.812 48	1.409 389	1.986 378
CAS	120.937 5	108.25	116.678 3	116.812 5	2.474 43	6.122 802
CTAC	0.044 942	-0.028 3	0.012 701	0.017 586	0.020 799	0.000 433
MH	-60.483 4	-63.131 1	-61.945 6	-61.955 6	0.690 906	0.477 352

模型参数	默认值	搜索范围	优化值
学习率	0.1	[0.01, 0.1]	0.05
最大深度	—	[3, 5]	5
叶子节点个数	30	(15, 30, 50, 100)	30

样本平衡前/后	P/%	A/%	R/%	F₁分数	AUC
样本平衡前	92	5	15	0.07	0.58
样本平衡后	99	92	88	0.90	0.99

参数特征	类型	出现次数
max_RUDD	配置参数	28
min_PTRM	配置参数	26
max_ALTS	操纵参数	22
var_PTRM	配置参数	20
min_ALTS	操纵参数	20
min_RUDD	配置参数	18
var_DA	动力学参数	18
var_WD	环境参数	17
var_RUDP	操纵参数	17
std_MNS	操纵参数	16

基于LightGBM-SHAP的民机硬着陆可解释预测

Explainable prediction for hard landing of civil aircraft based on LightGBM-SHAP

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