发表机构
Carleton University; University of Alberta; The Hong Kong Polytechnic University（卡尔顿大学; 阿尔伯塔大学; 香港理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种无监督机动感知声学故障检测框架，结合Noise2Noise去噪与机动条件自编码器，利用重建误差检测无人机故障，并在嵌入式平台上实现实时检测。
AI 中文摘要
本文提出了一种面向自主无人机的机动感知声学故障检测框架，该框架将受Noise2Noise启发的深度学习去噪与机动条件重建相结合。本工作的一个关键实际约束是，标记故障飞行数据难以收集且可能不安全；因此，所提出的框架遵循无监督学习范式，仅需标称飞行录音即可进行训练。在飞行环境中，从无人机获取的声学信号受到环境噪声和结构化、机动相关的气动效应引起的变异性影响。为同时解决这些挑战，开发了一种两阶段学习架构。在第一阶段，受Noise2Noise启发的去噪模型在不需要干净参考信号的情况下衰减随机声学噪声，同时保留与故障相关的频谱-时间结构。在第二阶段，使用包括无人机类型和飞行方向在内的机动相关标签训练机动条件卷积自编码器（maneuver-CCAE），以模拟不同操作条件下的标称声学行为。随后，使用重建误差作为异常分数进行故障检测。实验结果表明，所提出的机动感知条件将ROC曲线下面积（AUC）从$\AUCaeOnly$（无条件基线）提高到$\AUCfull$（完整模型），验证了机动依赖建模的关键作用。完整框架部署在NVIDIA Jetson Orin Nano Super嵌入式平台上，集成于ROS2管道中，在TensorRT半精度（FP16）后端下实现了每音频段约$20\\,\text{ms}$的端到端故障检测延迟，证实了机载无人机健康监测的实时可行性。
英文摘要
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.
