Automatic fault diagnosis for autonomous underwater vehicles using online topic models

As the capabilities of autonomous underwater vehicles (AUVs) improve, the missions become longer, riskier, and more complex. For AUVs to succeed in complex missions, they must be reliable in the face of subsystem failure and environmental challenges. In practice, fault detection activities carried out by most AUVs employ a rule-based emergency abort system that is triggered by specific events. AUVs equipped with the ability to diagnose faults and reason about mitigation actions in real time could improve their survivability and increase the value of individual deployments by replanning their mission in response to failures. In this paper, we focus on AUV autonomy as it pertains to self-perception and health monitoring and argue that automatic classification of state-sensor data represents an important enabling capability. We apply an online Bayesian nonparametric topic modeling technique to state-sensor data in order to automatically characterize the performance patterns of an AUV, then demonstrate how in combination with operator-supplied semantic labels these patterns can be used for fault detection and diagnosis by means of nearest-neighbor classifier. The method is applied in post-processing to diagnose faults that led to the temporary loss of the Monterey Bay Aquarium Research Institute's Tethys long-range AUV in two separate deployments. Our results show that the method is able to accurately identify and characterize patterns that correspond to various states of the AUV, and classify faults with high probability of detection and no false detects.

随着自主水下航行器（AUV）能力的提高，其执行的任务变得更长、风险更高且更复杂。为了使AUV在复杂任务中取得成功，它们在面对子系统故障和环境挑战时必须可靠。在实际应用中，大多数AUV所进行的故障检测活动采用的是基于规则的紧急中断系统，该系统由特定事件触发。具备实时诊断故障并对缓解措施进行推理能力的AUV可以通过根据故障重新规划任务来提高其生存能力，并增加单次部署的价值。在本文中，我们关注与自我感知和健康监测相关的AUV自主性，并认为状态 - 传感器数据的自动分类是一项重要的使能能力。我们将一种在线贝叶斯非参数主题建模技术应用于状态 - 传感器数据，以便自动描述AUV的性能模式，然后展示如何结合操作人员提供的语义标签，通过最近邻分类器将这些模式用于故障检测和诊断。该方法在事后处理中被用于诊断导致蒙特利湾水族馆研究所的忒提斯远程AUV在两次不同部署中暂时失联的故障。我们的结果表明，该方法能够准确识别和描述与AUV各种状态相对应的模式，并以高检测概率且无错误检测地对故障进行分类。