基于自适应边缘模型的移动机器人连续在线故障检测
Continuous Online Fault Detection for Mobile Robots via Adaptive Edge Models
- IRIT, Université Toulouse Capitole（图卢兹第一大学 IRIT 研究所）
- TwinswHeel（TwinswHeel 公司）
机构由 AI 辅助整理，请以论文原文为准。
中文总结 AI 辅助
本文提出教师-学生蒸馏框架，结合TSPulse离线模型与MiniRocket学生及RLS自适应，实现移动机器人边缘实时故障检测，显著提升VUS-PR分数并降低推理延迟。
中文摘要 AI 辅助
移动机器人需要鲁棒、实时的故障检测能力，并能在受限的边缘硬件上持续自适应。虽然深度时间序列模型在无监督异常检测方面表现出色，但其计算成本阻碍了高频机载执行。本文通过教师-学生蒸馏框架弥合了这一差距。一个离线基础模型（TSPulse）从注入故障的无标签时间序列中生成伪标签。一个轻量级MiniRocket学生模型，通过递归最小二乘估计器进行自适应，逼近这一复杂决策边界，以实现机载实时推理。在TSB-AD基准和物理移动机器人上的评估表明，学生模型实现了4.30毫秒的CPU推理延迟。在真实世界域偏移期间，在线自适应使学生模型能够从未知机械退化中恢复，将VUS-PR分数从0.26提升至0.75，且无灾难性遗忘。关键在于，一种不确定性引导的主动学习策略最小化操作员认知负荷，仅在遇到新故障分布时请求稀疏干预。这些结果验证了通过离线到在线蒸馏在资源受限机器人上部署最先进异常检测的可行性。
英文摘要
Mobile robots require robust, real-time fault detection capable of continuous adaptation on constrained edge hardware. While deep time-series models excel at unsupervised anomaly detection, their computational cost prohibits high-frequency onboard execution. This paper bridges this gap via a Teacher-Student distillation framework. An offline foundation model (TSPulse) generates pseudo-labels from unlabeled time series augmented with fault injections. A lightweight MiniRocket Student, adapted with a Recursive Least Squares estimator, approximates this complex decision boundary to execute real-time inference onboard. Evaluations on the TSB-AD benchmark and a physical mobile robot demonstrate the Student achieves a 4.30 ms CPU inference latency. During real-world domain shifts, online adaptation enables the Student to recover from unseen mechanical degradation, improving VUS-PR scores from 0.26 to 0.75 without catastrophic forgetting. Crucially, an uncertainty-guided active learning strategy minimizes operator cognitive load, requesting sparse interventions only when encountering novel fault distributions. These results validate the deployment of state-of-the-art anomaly detection on resource-constrained robotics through offline-to-online distillation.
