Underwater gliders have been widely used in oceanography for a range of applications. However, unpredictable events like shark strike or remora attachment can lead to abnormal glider behavior or even loss of the glider. This paper employs an anomaly detection algorithm to assess operational conditions of underwater gliders in the ocean environment. Prompt alerts are provided to glider pilots upon detecting any anomaly, so that they can take control of the glider to prevent further harm. The detection algorithm is applied to abundant datasets collected in real glider deployments led by the Skidaway Institute of Oceanography (SkIO) in the University of Georgia and the University of South Florida (USF). In order to demonstrate generality, the experimental evaluation is applied to four glider deployment datasets. Specifically, we utilize post-recovery DBD datasets carrying high-resolution information to perform detailed analysis of the anomaly and compare it with pilot logs. Additionally, we implement the online detection based on the real-time subsets of data transmitted from the glider at the surfacing events. While the real-time glider data may not contain as much rich information as the post-recovery one, the online detection is of great importance as it allows glider pilots to monitor potential abnormal conditions in real time.

翻译：水下滑翔机已在海洋学领域广泛应用于多种场景。然而，鲨鱼撞击或吸盘鱼附着等不可预测事件可能导致滑翔机行为异常甚至丢失。本文采用异常检测算法评估海洋环境中水下滑翔机的运行状态。一旦检测到异常，即时向滑翔机操作员发出警报，使其能够接管控制以避免进一步损害。该检测算法应用于佐治亚大学斯基达韦海洋研究所（SkIO）和南佛罗里达大学（USF）主导的实际滑翔机部署中收集的大规模数据集。为验证通用性，实验评估覆盖四个滑翔机部署数据集。具体而言，我们利用回收后携带高分辨率信息的DBD数据集进行异常详细分析，并与操作员日志进行对比。此外，我们基于滑翔机上浮事件时实时传输的数据子集实现在线检测。尽管实时滑翔机数据可能不如回收后数据信息丰富，但在线检测具有重大意义，它使操作员能够实时监控潜在异常状态。