Fault Diagnosis for Underwater Vehicles using Moving Horizon Estimation and Gaussian Processes

Abstract: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.

Robotics (cs.RO)

arXiv:2609.14539 [cs.RO]

(or arXiv:2609.14539v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2609.14539 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.