发表机构
Center for AI & Robotics; New York University Abu Dhabi（人工智能与机器人中心; 纽约大学阿布扎比分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出结合移动时域估计与高斯过程的水下航行器执行器故障诊断框架，通过残差广义似然比检验实现鲁棒检测与量化，并在水箱实验中验证。
AI 中文摘要
本文提出了一种针对水下航行器执行器故障的基于模型的故障检测与诊断框架，该框架显式考虑了未建模动态的影响。为此，开发了移动时域估计器（MHE）来估计集总扰动，该扰动同时包含未建模效应和故障效应。采用高斯过程（GPs）来近似未建模动态，提供在不同运行条件下相应的均值和不确定性预测。在线运行期间，利用广义似然比检验评估MHE集总扰动估计与GP预测之间的残差。通过将基于GP的预测纳入诊断框架，实现了对未建模动态的鲁棒性，从而能够有效进行故障检测与隔离，并准确量化估计故障幅度。所提方法在实验室水箱中进行了实验验证，展示了在开环和闭环控制下可靠的诊断性能。
英文摘要
This work proposes a model-based fault detection and diagnosis framework for underwater vehicles subject to actuator faults that explicitly accounts for the presence of unmodeled dynamics. To this end, a Moving Horizon Estimator (MHE) is developed to estimate the lumped disturbance, capturing both unmodeled and fault effects. Gaussian Processes (GPs) are employed to approximate the unmodeled dynamics, providing predictions of the corresponding mean and uncertainty across diverse operating conditions. During online operation, the residual between the MHE lumped disturbance estimate and the GP prediction is evaluated using a Generalized Likelihood Ratio Test. By incorporating GP-based predictions within the diagnostic framework, robustness to unmodeled dynamics is achieved, enabling effective fault detection and isolation as well as accurate quantitative estimation of fault magnitude. The proposed methodology is experimentally validated in a laboratory water tank, demonstrating reliable diagnostic performance under both open-loop and closed-loop control.
CommentsAccepted for publication at the 2026 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2026)
