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

TL;DR

This paper introduces a 1-D CNN-based fault classification method for high-endurance UCAV propulsion systems, enabling real-time, accurate detection of faults in IPMSM drives with low latency and high robustness.

Contribution

It presents a novel 1-D CNN framework that directly extracts features from high-frequency signals for real-time fault detection in IPMSM drives, outperforming existing ML models.

Findings

Achieved a weighted F1-score of 0.9834, surpassing LSTM and classical ML methods.

Demonstrated robustness across a wide RPM range of +-2700 RPM.

Enabled sub-millisecond inference on embedded controllers.

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.