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

LI Sicheng; WANG Lianqing; LI Zhiyong; WANG Guochang; GE Kaihua; CHEN Junfeng; TAN Rongqing

doi:10.11999/JEIT260188

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LI Sicheng, WANG Lianqing, LI Zhiyong, WANG Guochang, GE Kaihua, CHEN Junfeng, TAN Rongqing. A State Prediction Method for Long-Endurance Fixed-Wing UAV Propulsion Systems[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260188

Citation:

LI Sicheng, WANG Lianqing, LI Zhiyong, WANG Guochang, GE Kaihua, CHEN Junfeng, TAN Rongqing. A State Prediction Method for Long-Endurance Fixed-Wing UAV Propulsion Systems[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260188

Citation:

LI Sicheng, WANG Lianqing, LI Zhiyong, WANG Guochang, GE Kaihua, CHEN Junfeng, TAN Rongqing. A State Prediction Method for Long-Endurance Fixed-Wing UAV Propulsion Systems[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260188

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

doi: 10.11999/JEIT260188 cstr: 32379.14.JEIT260188

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

Received Date: 2026-02-12
Accepted Date: 2026-07-08
Rev Recd Date: 2026-07-08

Available Online: 2026-07-23

Abstract

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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References(31)

References

[1]	PAL O K, SHOVON M S H, MRIDHA M F, et al. In-depth review of AI-enabled unmanned aerial vehicles: Trends, vision, and challenges[J]. Discover Artificial Intelligence, 2024, 4(1): 97. doi: 10.1007/s44163-024-00209-1.
[2]	RAI S, RAWAT A, and KUMAR A. Design and performance analysis of high-altitude UAVs: Trends, challenges, and innovations[J]. Discover Applied Sciences, 2025, 7(8): 833. doi: 10.1007/s42452-025-07357-8.
[3]	TANG Liang, SAXENA A, and YOUNSI K. Prognostics and health management for electrified aircraft propulsion: State of the art and challenges[J]. Journal of Engineering for Gas Turbines and Power, 2025, 147(4): 041018. doi: 10.1115/1.4066598.
[4]	ADAIKA Z, AL-HADDAD L A, GIERNACKI W, et al. Fault detection and diagnosis methodologies for unmanned aerial vehicles: State-of-the-art[J]. Journal of Intelligent & Robotic Systems, 2025, 111(2): 63. doi: 10.1007/s10846-025-02267-8.
[5]	GONG Wenquan, LI Bo, AHN C K, et al. Prescribed-time extended state observer and prescribed performance control of quadrotor UAVs against actuator faults[J]. Aerospace Science and Technology, 2023, 138: 108322. doi: 10.1016/j.ast.2023.108322.
[6]	ZHOU Laihong, JIN Hong, CHEN Ping, et al. Actuator fault detection method of quadrotor UAV based on dual channel inertial sensors[J]. Discover Applied Sciences, 2025, 7(7): 736. doi: 10.1007/s42452-025-07403-5.
[7]	LIANG Shaojun, ZHANG Shirong, HUANG Yuping, et al. Data-driven fault diagnosis of FW-UAVs with consideration of multiple operation conditions[J]. ISA Transactions, 2022, 126: 472–485. doi: 10.1016/j.isatra.2021.07.043.
[8]	ZHANG Yizong, LI Shaobo, ZHANG Ansi, et al. FW-UAV fault diagnosis based on knowledge complementary network under small sample[J]. Mechanical Systems and Signal Processing, 2024, 215: 111418. doi: 10.1016/j.ymssp.2024.111418.
[9]	GUO Kai, WANG Na, LIU Datong, et al. Uncertainty-aware LSTM based dynamic flight fault detection for UAV actuator[J]. IEEE Transactions on Instrumentation and Measurement, 2023, 72: 3502113. doi: 10.1109/TIM.2022.3225040.
[10]	CHU Xiaoyan, ZHOU Xu, BU Qixuan, et al. Sensor fault detection for UAVs using improved self-attention LSTM network with similarity space mapping[J]. IEEE Transactions on Instrumentation and Measurement, 2024, 73: 3528612. doi: 10.1109/TIM.2024.3450064.
[11]	WANG Shengdong, JIA Zhen, LIU Zhenbao, et al. Self-supervised contrast learning based UAV fault detection and interpretation with spatial-temporal information of multivariate flight data[J]. Expert Systems with Applications, 2025, 267: 126156. doi: 10.1016/j.eswa.2024.126156.
[12]	FANG Jie, LI Shaobo, ZHANG Yizong, et al. Fault diagnosis of UAV sensors based on multi-auxiliary task learning with few samples[J]. Journal of Computational Design and Engineering, 2025, 12(12): 142–160. doi: 10.1093/jcde/qwaf117.
[13]	张景森, 侯彪, 李志杰, 等. 结合姿态不变性特征和半监督复兴稠密生成对抗分类网络模型的飞控系统故障诊断方法[J]. 电子与信息学报, 2026, 48(1): 264–276. doi: 10.11999/JEIT250964. ZHANG Jingsen, HOU Biao, LI Zhijie, et al. A fault diagnosis method for flight control systems combining pose-invariant features and a semi-supervised RDC-GAN model[J]. Journal of Electronics & Information Technology, 2026, 48(1): 264–276. doi: 10.11999/JEIT250964.
[14]	潘金伟, 王乙乔, 钟博, 等. 基于统计特征搜索的多元时间序列预测方法[J]. 电子与信息学报, 2024, 46(8): 3276–3284. doi: 10.11999/JEIT231264. PAN Jinwei, WANG Yiqiao, ZHONG Bo, et al. Statistical feature-based search for multivariate time series forecasting[J]. Journal of Electronics & Information Technology, 2024, 46(8): 3276–3284. doi: 10.11999/JEIT231264.
[15]	杨真真, 徐奕, 万成业, 等. 融合多尺度频域适配器和双路注意力的时序预测[J]. 电子与信息学报, 2026, 48(4): 1795–1805. doi: 10.11999/JEIT251188. YANG Zhenzhen, XU Yi, WAN Chengye, et al. Multi-scale frequency adapter and dual-path attention for time series forecasting[J]. Journal of Electronics & Information Technology, 2026, 48(4): 1795–1805. doi: 10.11999/JEIT251188.
[16]	NIE Yuqi, NGUYEN N H, SINTHONG P, et al. A time series is worth 64 words: Long-term forecasting with transformers[C]. The Eleventh International Conference on Learning Representations, Kigali, Rwanda, 2023.
[17]	WU Haixu, HU Tengge, LIU Yong, et al. TimesNet: Temporal 2D-variation modeling for general time series analysis[C]. The Eleventh International Conference on Learning Representations, Kigali, Rwanda, 2023.
[18]	ZENG Ailing, CHEN Muxi, ZHANG Lei, et al. Are transformers effective for time series forecasting?[C]. Proceedings of the 37th AAAI Conference on Artificial Intelligence, Washington, USA, 2023: 11121–11128. doi: 10.1609/aaai.v37i9.26317.
[19]	LUO Donghao and WANG Xue. ModernTCN: A modern pure convolution structure for general time series analysis[C]. The Twelfth International Conference on Learning Representations, Vienna, Austria, 2024: 1–43.
[20]	SZEGEDY C, LIU Wei, JIA Yangqing, et al. Going deeper with convolutions[C]. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, USA, 2015: 1–9. doi: 10.1109/CVPR.2015.7298594.
[21]	HU Jie, SHEN Li, and SUN Gang. Squeeze-and-excitation networks[C]. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, USA, 2018: 7132–7141. doi: 10.1109/CVPR.2018.00745.
[22]	HOWARD A G, ZHU Menglong, CHEN Bo, et al. MobileNets: Efficient convolutional neural networks for mobile vision applications[EB/OL]. https://arxiv.org/abs/1704.04861, 2017. doi: 10.48550/arXiv.1704.04861.
[23]	HE Kaiming, ZHANG Xiangyu, REN Shaoqing, et al. Deep residual learning for image recognition[C]. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, USA, 2016: 770–778. doi: 10.1109/CVPR.2016.90.
[24]	IOFFE S and SZEGEDY C. Batch normalization: Accelerating deep network training by reducing internal covariate shift[C]. Proceedings of the 32nd International Conference on Machine Learning, Lille, France, 2015: 448–456.
[25]	LUO Wenjie, LI Yujia, URTASUN R, et al. Understanding the effective receptive field in deep convolutional neural networks[C]. Proceedings of the 30th International Conference on Neural Information Processing Systems, Barcelona, Spain, 2016: 4905–4913.
[26]	ALCIBAR J, AIZPURUA J I, ZUGASTI E, et al. A hybrid probabilistic battery health management approach for robust inspection drone operations[J]. Engineering Applications of Artificial Intelligence, 2025, 146: 110246. doi: 10.1016/j.engappai.2025.110246.
[27]	YANG Mengchen and PHUNG B T. Motor winding insulation degradation under repetitive voltage pulses[J]. IEEE Access, 2024, 12: 77658–77674. doi: 10.1109/ACCESS.2024.3406490.
[28]	ZHOU Tian, MA Ziqing, WEN Qingsong, et al. FEDformer: Frequency enhanced decomposed transformer for long-term series forecasting[C]. Proceedings of the 39th International Conference on Machine Learning, Baltimore, USA, 2022: 27268–27286.
[29]	WANG Yuxuan, WU Haixu, DONG Jiaxiang, et al. Deep time series models: A comprehensive survey and benchmark[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2026: 1–20. doi: 10.1109/TPAMI.2026.3690845.
[30]	KINGMA D and BA J. Adam: A method for stochastic optimization[C]. 3rd International Conference on Learning Representations, San Diego, USA, 2015.
[31]	SWEENEY A J and FU Qiang. Diurnal cycles of synthetic microwave sounding Lower-stratospheric temperatures from radio occultation observations, reanalysis, and model simulations[J]. Journal of Atmospheric and Oceanic Technology, 2021, 38(12): 2045–2059. doi: 10.1175/JTECH-D-21-0071.1.