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arXiv · 2603.21133

High-Endurance UCAV Propulsion System: A 1-D CNN-Based Real-Time Fault Classification for Tactical-Grade IPMSM Drive

Tahmin Mahmud
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Abstract

High-performance propulsion for mission-critical applications demands unprecedented reliability and real-time fault resilience. Conventional diagnostic methods (signal-based analysis and standard ML models) are essential for stator/rotor fault detection but suffer from high latency and poor generalization across variable speeds. This paper proposes a 1-D Convolutional Neural Network (CNN) framework for real-time fault classification in the HPDM-350 interior permanent magnet synchronous motor (IPMSM). The proposed architecture extracts discriminative features directly from high-frequency current and speed signals, enabling sub-millisecond inference on embedded controllers. Compared to state-of-the-art long short term memory (LSTM) and classical ML approaches, the 1-D CNN achieves a superior weighted F1-score of 0.9834. Validated through high-fidelity magnetic-domain MATLAB/Simscape models, the method demonstrates robust performance across a +-2700 RPM envelope, providing a lightweight solution for mission-critical electric propulsion systems.

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Tahmin Mahmud. 2026-03-22. High-Endurance UCAV Propulsion System: A 1-D CNN-Based Real-Time Fault Classification for Tactical-Grade IPMSM Drive. https://doi.org/10.1109/idcd69431.2026.11519419

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