# ALFA: A Dataset for UAV Fault and Anomaly Detection

arXiv: https://arxiv.org/abs/1907.06268 · HTML: https://mlsanity.com/paper/1907.06268/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jul 2019.

## Built on by (most cited first, 8 of 8)

- [Rare event modeling with self-regularized normalizing flows: what can we learn from a single failure?](https://mlsanity.com/paper/2502.21110/) (2025)
- [Anomaly Detection for Unmanned Aerial Vehicle Sensor Data Using a Stacked Recurrent Autoencoder Method with Dynamic Thresholding](https://mlsanity.com/paper/2203.04734/) (2022)
- [Anomaly Detection of UAV State Data Based on Single-class Triangular Global Alignment Kernel Extreme Learning Machine](https://mlsanity.com/paper/2302.09320/) (2023)
- [Predicting UAV Type: An Exploration of Sampling and Data Augmentation for Time Series Classification](https://mlsanity.com/paper/2403.00565/) (2024)
- [Effective Intrusion Detection for UAV Communications using Autoencoder-based Feature Extraction and Machine Learning Approach](https://mlsanity.com/paper/2410.02827/) (2024)
- [Diffuse to Detect: A Generalizable Framework for Anomaly Detection with Diffusion Models Applications to UAVs and Beyond](https://mlsanity.com/paper/2510.22928/) (2025)
- [An Efficient Privacy-preserving Intrusion Detection Scheme for UAV Swarm Networks](https://mlsanity.com/paper/2511.22791/) (2025)
- [LogiC-Diff: Embedding Security Properties Into AI-Enabled Cyber-Physical Systems](https://mlsanity.com/paper/2609.38381/) (2026)

## Abstract (arXiv)

We present a dataset of several fault types in control surfaces of a fixed-wing Unmanned Aerial Vehicle (UAV) for use in Fault Detection and Isolation (FDI) and Anomaly Detection (AD) research. Currently, the dataset includes processed data for 47 autonomous flights with 23 sudden full engine failure scenarios and 24 scenarios for seven other types of sudden control surface (actuator) faults, with a total of 66 minutes of flight in normal conditions and 13 minutes of post-fault flight time. It additionally includes many hours of raw data of fully-autonomous, autopilot-assisted and manual flights with tens of fault scenarios. The ground truth of the time and type of faults is provided in each scenario to enable evaluation of the methods using the dataset. We have also provided the helper tools in several programming languages to load and work with the data and to help the evaluation of a detection method using the dataset. A set of metrics is proposed to help to compare different methods using the dataset. Most of the current fault detection methods are evaluated in simulation and as far as we know, this dataset is the only one providing the real flight data with faults in such capacity. We hope it will help advance the state-of-the-art in Anomaly Detection or FDI research for Autonomous Aerial Vehicles and mobile robots to enhance the safety of autonomous and remote flight operations further. The dataset and the provided tools can be accessed from https://doi.org/10.1184/R1/12707963.