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Related Experiment Video

Updated: Sep 9, 2025

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

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Published on: October 28, 2022

Edge-Based Real-Time Fault Detection in UAV Systems via B-Spline Telemetry Reconstruction and Lightweight Hybrid AI.

Manuel J C S Reis¹, António J D Reis²

¹Engineering Departement/IEETA, Quinta de Prados, University of Trás-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal.

Sensors (Basel, Switzerland)

|August 28, 2025

Summary

This study introduces a real-time fault detection system for unmanned aerial vehicles (UAVs) that works onboard edge devices. It ensures safe flight operations by accurately identifying system anomalies even with irregular telemetry data.

Keywords:

B-spline interpolation LSTM autoencoder UAV telemetry anomaly detection edge computing embedded systems hybrid AI models multivariate time series real-time signal reconstruction sensor data irregularities

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Last Updated: Sep 9, 2025

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Published on: August 27, 2021

Area of Science:

Aerospace Engineering
Computer Science
Artificial Intelligence

Background:

Unmanned aerial vehicles (UAVs) require advanced onboard diagnostics for safe operation, especially with unreliable telemetry.
Existing systems struggle with irregular, non-uniform sensor data common in real-world UAV missions.

Purpose of the Study:

To develop a real-time, onboard fault detection framework for UAVs capable of handling disrupted telemetry.
To optimize the framework for deployment on resource-constrained edge computing platforms.

Main Methods:

Implemented B-spline interpolation for robust sensor data reconstruction from noisy or asynchronous inputs.
Utilized a hybrid anomaly detection model combining Long Short-Term Memory (LSTM) autoencoder and Isolation Forest.
Deployed the entire pipeline on embedded systems like Raspberry Pi 4 and NVIDIA Jetson Nano.

Main Results:

Achieved end-to-end inference latency below 50 milliseconds for onboard processing.
Demonstrated a fault detection accuracy of 93.6% using real flight logs and synthetic fault injection.
Showcased significant resilience to telemetry dropouts and sampling irregularities.

Conclusions:

The developed framework enables autonomous, sensor-based health monitoring for UAVs.
The system is feasible for onboard deployment, enhancing UAV safety and reliability.
The approach is applicable to other real-time cyber-physical systems requiring robust anomaly detection.