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

Volume 48 Issue 9

Sep. 2026

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Article Navigation > Journal of Electronics & Information Technology > 2026 > 48(9): 3987-3998

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, 2026, 48(9): 3987-3998. 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, 2026, 48(9): 3987-3998. 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, 2026, 48(9): 3987-3998. 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 Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China
4.
College of Data Intelligence, Information Support Force Engincering University, Wuhan 430010, 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

Publish Date: 2026-09-18

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 exceeding 24 h, electrical and thermal variables in the propulsion system exhibit strong coupling, multi-time-constant dynamics, and pronounced day-night operating-condition changes. These characteristics cause short-term fluctuations and long-term trends to coexist, making adaptive feature weighting difficult under time-varying variable sensitivities. Therefore, general-purpose time-series forecasting models often fail to satisfy the accuracy and robustness requirements of multivariate propulsion-system state prediction. To address these challenges, a Grouped Squeeze-and-Excitation Multi-scale Temporal Convolutional Network (GEMS-TCN) is proposed by enhancing a modern convolution-based forecasting backbone with Multi-scale Embedding (MsE) and Grouped Channel Attention. The proposed model performs accurate 10 s-ahead single-step prediction of 16 key propulsion-system states from 72-dimensional flight telemetry while satisfying the real-time inference requirement of the 1 Hz telemetry cycle. Methods Real-flight telemetry collected from a representative long-endurance fixed-wing UAV is used for model development and evaluation. The dataset is sampled at 1 Hz over nine consecutive days and contains 806 629 time steps and 72 variables (Fig. 2). Sixteen propulsion-system states, including the bus voltage, control-unit temperature, winding temperature, and power-device temperature of four motors, are selected as prediction targets, whereas all 72 variables are used as model inputs (Table 1). The raw data are processed through time indexing, interquartile range (IQR)-based anomaly detection, and interpolation, and are then chronologically divided into training, validation, and test sets at a ratio of 8:1:1 to prevent information leakage (Figs. 4 and 5). GEMS-TCN employs an MsE layer with parallel one-dimensional convolutions to extract temporal features at multiple receptive-field scales. Stacked GEMS-TCN blocks integrate Depthwise Convolution (DWConv), Convolutional Feed-Forward Networks (ConvFFNs), and Grouped Squeeze-and-Excitation (GroupSE) modules to hierarchically recalibrate intra-variable and cross-variable channel responses (Fig. 1). The models are trained using the Adam optimizer with the Mean Squared Error (MSE) loss function. Performance is evaluated using the Mean Absolute Error (MAE), MSE, and Symmetric Mean Absolute Percentage Error (SMAPE). PatchTST, ModernTCN, FEDformer, DLinear, and TimesNet are used as benchmark models. Ablation and robustness experiments are conducted for further validation. Results and Discussions On the complete 16-dimensional prediction task, GEMS-TCN achieves a test-set MAE of 0.171 and an MSE of 0.069 (Table 4). Compared with TimesNet, the strongest baseline for trend tracking, GEMS-TCN reduces the MSE by 28.1% while maintaining comparable MAE and SMAPE, demonstrating improved suppression of large prediction errors (Table 4). Stable prediction accuracy is maintained under both daytime and nighttime operating conditions, with MAE/MSE values of 0.189/0.085 and 0.150/0.051, respectively, demonstrating robustness to operating-condition changes (Table 4). The predicted trajectories exhibit more accurate alignment at thrust-transition points, reduced overshoot, and fewer spurious spikes, while maintaining low-bias tracking of slowly varying nighttime temperature profiles (Fig. 7). Ablation experiments show that MsE and GroupSE each improve prediction performance, and their combination achieves the best overall performance, confirming that the two modules provide complementary benefits (Table 6). Under 1% and 5% random data dropouts and 60 s continuous data loss, the MSE remains below 0.07, demonstrating robustness to practical data-loss scenarios (Table 5). Furthermore, GEMS-TCN contains 27.88 M parameters and achieves an inference latency of 0.90 ms per sample, which is substantially shorter than the 1 s telemetry sampling interval. Conclusions GEMS-TCN provides an effective convolution-based framework for multivariate propulsion-system state prediction in long-endurance fixed-wing UAVs. By integrating MsE with hierarchical channel recalibration implemented through GroupSE, the proposed method simultaneously represents rapid fluctuations and slow temporal evolution, thereby improving the representation of multi-time-constant dynamics and multivariate coupling in propulsion-system telemetry. Real-flight experiments demonstrate stable prediction performance across daytime and nighttime operating conditions, different state categories, and data-loss scenarios. Ablation experiments further verify the effectiveness and complementarity of MsE and GroupSE. These results indicate that the proposed structure-aware modeling strategy provides effective support for propulsion-system 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)

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