一种面向长航时固定翼无人机推进系统的状态预测方法

李思成; 王连清; 李志永; 王国昌; 葛凯华; 陈俊锋; 谭荣清

doi:10.11999/JEIT260188

一种面向长航时固定翼无人机推进系统的状态预测方法

doi: 10.11999/JEIT260188 cstr: 32379.14.JEIT260188

李思成^{1, 2, 3,},
王连清^{3, 4, ,},
李志永^{1, 2, ,},
王国昌^{1, 2},
葛凯华⁵,
陈俊锋⁶,
谭荣清^{1, 2}

1.
中国科学院空天信息创新研究院北京 100094
2.
中国科学院大学电子电气与通信工程学院北京 100049
3.
国防科技大学智能科学学院长沙 410073
4.
信息支援部队工程大学指挥控制保障系武汉 430030
5.
中国航天空气动力技术研究院创新与应用中心北京 100074
6.
中国人民解放军92728部队上海 200436

详细信息

作者简介:
李思成：男，硕士生，研究方向为固定翼无人机状态预测、时序数据建模与深度学习，邮箱：　15533151020@163.com

王连清：男，副教授，研究方向为飞行器状态监测、故障诊断及健康管理，邮箱：　lianqingw@126.com

李志永：男，副研究员，研究方向为大数据分析及应用，邮箱：　zhiyongli@aircas.ac.cn

王国昌：男，硕士生，研究方向为大数据分析及应用

葛凯华：男，高级工程师，研究方向为飞行器推进系统试验测试、状态监测与故障诊断

陈俊锋：男，高级工程师，研究方向为无人机系统应用、飞行状态监测与故障预警

谭荣清：男，研究员，研究方向为激光技术及应用

通讯作者:
王连清　lianqingw@126.com

李志永　zhiyongli@aircas.ac.cn

中图分类号: TP183
计量
- 文章访问数: 31
- HTML全文浏览量: 16
- PDF下载量: 5
- 被引次数: 0
出版历程
- 收稿日期: 2026-02-12
- 修回日期: 2026-07-08
- 录用日期: 2026-07-08
- 网络出版日期: 2026-07-23

A State Prediction Method for Long-Endurance Fixed-Wing UAV Propulsion Systems

LI Sicheng^{1, 2, 3
,},
WANG Lianqing^{3, 4
, ,},
LI Zhiyong^{1, 2
, ,},
WANG Guochang^{1, 2},
GE Kaihua⁵,
CHEN Junfeng⁶,
TAN Rongqing^{1, 2}

1.
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
2.
School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
3.
College of Intelligent Science, National University of Defense Technology, Changsha 410073, China
4.
Department of Command and Control Support, Information Support Force Engincering University, Wuhan 430030, China
5.
Innovation and Application Center, China Academy of Aerospace Aerodynamics, Beijing 100074, China
6.
Unit 92728, Chinese People’s Liberation Army, Shanghai 200436, China

摘要

摘要: 面向长航时固定翼无人机推进系统关键状态预测的工程应用需求，该文优化设计出一种融合多尺度嵌入与分组通道注意力的时序卷积模型(Grouped Squeeze-and-Excitation Multi-scale Temporal Convolutional Networks, GEMS-TCN)。该模型采用多尺度嵌入层自适应提取短期波动与长期趋势的混合时序表征，通过分组通道注意力实现变量内与跨变量特征的层次化加权。基于实飞数据集的实验结果表明，GEMS-TCN在推进系统16维关键状态的单步预测精度优越，测试集中平均绝对误差(Mean Absolute Error, MAE)、均方误差(Mean Squared Error, MSE)分别达到0.171、0.069，且在不同特征及昼夜工况下均保持一致的精度优势，为固定翼无人机推进系统多维状态预测提供了切实可行且性能优异的实现方案。
- 固定翼无人机 /
- 状态预测 /
- 时序卷积网络 /
- 多尺度嵌入 /
- 分组通道注意力
Abstract: Objective Accurate single-step prediction of key propulsion-system states is essential for early fault warning and autonomous health management of long-endurance fixed-wing unmanned aerial vehicles (UAVs). During high-altitude missions lasting more than 24 h, electrical and thermal variables in the propulsion system exhibit strong coupling, multi-time-constant dynamics, and pronounced day-night regime shifts. These characteristics cause short-term disturbances and long-term drifts to coexist, and hinder adaptive feature weighting under time-varying variable sensitivities. General-purpose time-series predictors may therefore fail to meet the accuracy and robustness requirements of multivariate propulsion-state prediction. To address these challenges, a Grouped Squeeze-and-Excitation Multi-scale Temporal Convolutional Network (GEMS-TCN) is developed by enhancing a modern pure-convolution forecasting backbone with multi-scale embedding and grouped channel attention. The aim is to obtain accurate 10 s-ahead single-step forecasts for 16 key propulsion states from 72-dimensional flight telemetry while satisfying the real-time inference requirement of the 1 Hz telemetry cycle. Methods Real flight telemetry from a representative long-endurance fixed-wing UAV is used for model construction and evaluation. The data are sampled at 1 Hz for 9 consecutive days, yielding 806,629 time steps and 72 variables. (Fig.2) Sixteen propulsion-related key states, including the bus voltage, control-unit temperature, winding temperature, and power-device temperature of four motors, are selected as prediction targets, and all 72 variables are used as inputs. (Table 1) The raw data are processed through time indexing, interquartile range (IQR)-based anomaly handling, and interpolation, and are then chronologically divided into training, validation, and test sets at a ratio of 8:1:1 to avoid information leakage. (Fig.4) (Fig.5) GEMS-TCN uses a multi-scale embedding layer with parallel one-dimensional convolutions to extract temporal patterns at different receptive-field scales. Stacked GEMS-TCN blocks combine depthwise temporal convolution, grouped convolutional feed-forward networks, and Grouped Squeeze-and-Excitation (GroupSE) modules to recalibrate intra-variable and cross-variable channel responses hierarchically. (Fig.1) The models are trained with Adam and mean squared error (MSE) loss, and are evaluated using mean absolute error (MAE), MSE, and symmetric mean absolute percentage error (SMAPE). PatchTST, ModernTCN, FEDformer, DLinear, and TimesNet are used as comparison models, with ablation and robustness experiments conducted for further verification. Results and Discussions On the full 16-dimensional target set, GEMS-TCN achieves a test-set MAE of 0.171 and an MSE of 0.069. (Table 4) Compared with TimesNet, the strongest baseline in overall trend tracking, GEMS-TCN reduces MSE by 28.1% while maintaining comparable MAE and SMAPE, indicating stronger suppression of large prediction deviations. (Table 4) Stable accuracy is obtained in both daytime and nighttime segments, with MAE/MSE values of 0.189/0.085 and 0.150/0.051, respectively, demonstrating robustness to diurnal operating-condition changes. (Table 4) The prediction trajectories show tighter alignment at thrust-transition points, reduced overshoot, and fewer spurious spikes, while low-bias tracking is preserved for slowly varying nighttime temperature profiles. (Fig.7) Ablation results show that multi-scale embedding and GroupSE provide complementary improvements, and their combination achieves the best overall performance among the ablation settings. (Table 6) Under 1% and 5% random dropouts and 60 s continuous missing intervals, the MSE remains below 0.07, indicating tolerance to practical data-loss scenarios. (Table 5) In addition, GEMS-TCN contains 27.88 M parameters and achieves an inference latency of 0.90 ms per sample, which is well below the 1 s sampling interval. Conclusions GEMS-TCN provides a practical convolution-based solution for multivariate propulsion-state prediction in long-endurance fixed-wing UAVs. By integrating multi-scale temporal embedding with hierarchical group-wise channel recalibration, the proposed method jointly represents rapid fluctuations and slow evolution, and better captures multi-time-constant dynamics and multivariate coupling in propulsion telemetry. Real-flight experiments demonstrate stable prediction performance across diurnal regimes, state categories, and data-missing scenarios. Ablation results further confirm the effectiveness and complementarity of multi-scale embedding and GroupSE. These findings indicate that structure-aware modeling tailored to propulsion-state evolution can support health monitoring, early fault warning, and autonomous health management of long-endurance fixed-wing UAVs.
- Fixed-wing UAV /
- State prediction /
- Temporal convolutional network /
- Multi-scale embedding /
- Grouped channel attention

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图 1 GEMS-TCN模型架构图

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图 2 固定翼无人机推进系统数据采集示意图

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图 3 各特征组与1#电机关键特征的相对互信息热图

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图 4 数据预处理流程图

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图 5 样本构造示意图

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图 6 GEMS-TCN模型关键超参数单因素敏感性分析(指标选用MAE、MSE，其余训练参数设置保持一致)

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图 7 1#电机特征预测结果对比图

注：测试集数据总采集时长约24小时，图中黄色、蓝色和灰色区分别表示测试集中白天时段、黑夜时段及模糊过渡时段。

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表 1 16维待预测关键特征与故障预警任务关系表

1#-4#电机特征组	单位	物理含义	与故障预警任务的关系
母线电压	V	电机驱动侧直流供电水平以及负载扰动下的母线稳定性	异常下降或波动表征电池接近截止放电阈值、剩余航时下降及供电链路异常，是续航预测与任务级预警的重要先兆^[26]
控制单元温度	℃	电机控制电子单元的热负荷水平与散热状态	异常升高常对应故障、开关异常或散热退化，可用于推进系统早期告警与故障隔离^[3]
绕组温度	℃	电机定子绕组的热积累程度和绝缘系统受热水平	持续升高通常意味着过流、绕组绝缘退化或退化向电机侧传递，可作为电机热失效与性能衰退的关键预警量^[27]
功率器件温度	℃	电机驱动器功率半导体器件的热应力水平	异常升高直接指向功率器件退化、开关频率异常与效率下降，严重时可导致失推，是驱动级健康管理的重要监测量^[3]

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表 2 数据集72维特征分组表

序号	特征组	数据项
1	推进系统	1#-4#电机实际转速、指令转速、母线电压、母线电流、D/Q轴电流、解算力矩、转子位置、控制单元温度、绕组温度、功率器件温度
2	供电系统	1#-8#单体电池电压
3	飞行控制	指示空速、真空速、地速
4	大气与环境	气压高度、海拔高度、左右两侧大气静温
5	辅助特征	帧计数器；1#-4#电机电机序号、电机系统状态、通讯状态字

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表 3 实验环境配置

硬件	配置	软件	配置
CPU	Intel Xeon Gold 6330	操作系统	Ubuntu 22.04
GPU	NVIDIA A100 PCIe 40GB	编程框架	PyTorch 2.0.0

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表 4 模型预测精度对比

方法	特征	MAE			MSE			SMAPE
方法	特征	白天	夜晚	全天	白天	夜晚	全天	白天	夜晚	全天
PatchTST	A	0.406	0.322	0.371	0.303	0.174	0.249	0.115	0.025	0.078
	B	0.269	0.261	0.265	0.128	0.105	0.117	0.001	0.001	0.001
	C	0.546	0.417	0.494	0.476	0.264	0.389	0.188	0.065	0.131
	W	0.482	0.352	0.427	0.435	0.211	0.338	0.144	0.012	0.093
	P	0.326	0.256	0.298	0.175	0.117	0.153	0.127	0.023	0.086
ModernTCN	A	0.330	0.260	0.303	0.230	0.130	0.190	0.110	0.019	0.071
	B	0.278	0.218	0.253	0.152	0.091	0.126	0.001	0.001	0.001
	C	0.221	0.198	0.211	0.092	0.068	0.082	0.148	0.043	0.097
	W	0.491	0.320	0.425	0.453	0.189	0.349	0.159	0.012	0.100
	P	0.331	0.306	0.323	0.221	0.170	0.204	0.133	0.022	0.087
FEDformer	A	0.224	0.142	0.186	0.226	0.091	0.162	0.088	0.016	0.058
	B	0.204	0.157	0.182	0.153	0.083	0.120	0.001	0.001	0.001
	C	0.140	0.116	0.130	0.092	0.076	0.085	0.133	0.040	0.087
	W	0.355	0.153	0.263	0.517	0.106	0.324	0.109	0.006	0.072
	P	0.196	0.144	0.170	0.141	0.097	0.119	0.109	0.016	0.072
DLinear	A	0.247	0.194	0.224	0.128	0.071	0.103	0.100	0.017	0.065
	B	0.184	0.175	0.179	0.095	0.071	0.084	0.001	0.001	0.001
	C	0.215	0.188	0.204	0.081	0.059	0.072	0.149	0.043	0.098
	W	0.358	0.214	0.296	0.243	0.087	0.174	0.132	0.008	0.083
	P	0.231	0.198	0.216	0.093	0.067	0.081	0.119	0.017	0.077
TimesNet	A	0.178	0.122	0.152	0.126	0.061	0.096	0.085	0.014	0.055
	B	0.173	0.153	0.163	0.108	0.079	0.094	0.001	0.001	0.001
	C	0.126	0.098	0.114	0.068	0.050	0.060	0.133	0.036	0.085
	W	0.263	0.126	0.200	0.257	0.066	0.167	0.102	0.005	0.067
	P	0.148	0.111	0.130	0.071	0.050	0.061	0.104	0.014	0.068
GEMS-TCN(Ours)	A	0.189	0.150	0.171	0.085	0.051	0.069	0.092	0.015	0.059
	B	0.155	0.151	0.153	0.081	0.066	0.073	0.001	0.001	0.001
	C	0.153	0.125	0.141	0.053	0.036	0.045	0.139	0.037	0.089
	W	0.254	0.174	0.220	0.133	0.055	0.097	0.114	0.006	0.072
	P	0.191	0.149	0.171	0.074	0.047	0.061	0.113	0.015	0.073
注：特征列中A代表1#-4#电机完整16维特征，而B、C、W、P分别代表1#-4#电机的母线电压、控制单元温度、绕组温度以及功率器件温度。

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表 5 模型鲁棒性对比(MSE)

模型	无扰动	数据掉点(1%)	数据掉点(5%)	高斯噪声(0.05)	高斯噪声(0.1)	连续缺失(60s)
PatchTST	0.2493	0.2493	0.2496	2.4646	9.1779	0.2493
ModernTCN	0.1903	0.1903	0.1904	2.1375	8.0099	0.1903
FEDformer	0.1623	0.1623	0.1622	2.0433	7.7212	0.1623
DLinear	0.1026	0.1026	0.1028	2.0310	7.8473	0.1026
TimesNet	0.0956	0.0956	0.0956	2.0407	7.8970	0.0956
GEMS-TCN(Ours)	0.0693	0.0694	0.0697	2.2460	8.8151	0.0693

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表 6 不同模块对预测精度的影响

模型	Multi-Scale Embedding	GroupSE	MAE	MSE
ModernTCN	×	×	0.303	0.190
Model1	√	×	0.199	0.085
Model2	×	√	0.219	0.098
Model3	√	√	0.171	0.069

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