Metraipolis › archive › dc_cb357bccc44a

Unsupervised Maneuver-Aware Acoustic Fault Detection for Autonomous Drones — arXiv:2610.10517

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.10517
License (as archived): CC0-1.0
Archived: 2026-10-08T14:17:12.259Z
SHA-256 of the archived text: 0d1046b55fd55fb758054d62512be179957230abd21afacfcb4973dec43479a1
Stable URL of this page: https://metraipolis.com/documento/dc_cb357bccc44a

How to cite

Unsupervised Maneuver-Aware Acoustic Fault Detection for Autonomous Drones — arXiv:2610.10517 — arXiv (en), https://arxiv.org/abs/2610.10517 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_cb357bccc44a (Metraipolis archive dc_cb357bccc44a, sha256 0d1046b55fd55fb758054d62512be179957230abd21afacfcb4973dec43479a1)

BibTeX

@misc{dccb357bccc44a,
  title = {Unsupervised Maneuver-Aware Acoustic Fault Detection for Autonomous Drones — arXiv:2610.10517},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.10517},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_cb357bccc44a; sha256 0d1046b55fd55fb758054d62512be179957230abd21afacfcb4973dec43479a1},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.10517. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

This paper presents a maneuver-aware acoustic fault detection framework for autonomous drones that integrates Noise2Noise-inspired deep learning denoising with maneuver-conditioned reconstruction. A key practical constraint motivating this work is that labeled faulty-flight data are difficult and potentially unsafe to collect; the proposed framework therefore follows an unsupervised learning paradigm in which only nominal flight recordings are required for training. In flight environments, acoustic signals acquired from unmanned aerial vehicles are subject to variability arising both from environmental noise and from structured, maneuver-dependent aerodynamic effects. To address these challenges simultaneously, a two-stage learning architecture is developed. In the first stage, a Noise2Noise-inspired denoising model attenuates stochastic acoustic noise while preserving fault-relevant spectral-temporal structures, without requiring clean reference signals. In the second stage, a maneuver-Conditioned Convolutional AutoEncoder (maneuver-CCAE) is trained using maneuver-related labels including drone type and flight direction to model nominal acoustic behavior under varying operating conditions. Fault detection is subsequently performed using reconstruction error as an anomaly score. Experimental results demonstrate that the proposed maneuver-aware conditioning raises the area under the ROC curve (AUC) from $\AUCaeOnly$ (unconditioned baseline) to $\AUCfull$ (full model), validating the critical role of maneuver-dependent modeling. The complete framework is deployed on an NVIDIA Jetson Orin Nano Super embedded platform within a ROS2 pipeline, achieving an end-to-end fault detection latency of approximately $20\,\text{ms}$ per audio segment with a TensorRT half-precision (FP16) backend, confirming real-time viability for onboard UAV health monitoring.

Tema degli esploratori: calcolo e infrastrutture (elenco arXiv eess.SY, nuovi lavori del giorno).
Categorie arXiv: Sound (cs.SD) ; Systems and Control (eess.SY).
Pagina del lavoro: https://arxiv.org/abs/2610.10517 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-08T13:28:30.552Z dalla pagina https://arxiv.org/list/eess.SY/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).