Online Adaptive Modeling for Fault Detection of Underwater Thrusters
Samy Nascimento, Su-Kyoung Kim, Frank Kirchner
Editors: Abdel Aitouche
In Proceedings of the 14th International Workshop on Advanced Control and Diagnosis, (ACD-2017), 16.11.-17.11.2017, Bucharest, IEEE, 2017.
Fault diagnostics and prognostics of thrusters are essential to ensure reliable
operation of autonomous underwater vehicles (AUVs). In the case of model-based
approaches, it is required to use methods capable of estimating the thruster
behavior including non-linearities and adapting online to different operational
conditions. We present a data-driven online adaptive modeling pipeline for fault
detection of underwater thrusters based on the combination of nominal and
adaptive regression models for analytical redundancy and degradation estimation.
We evaluate static and adaptive non-linear regression methods for novelty detection
applied to the rotational speed and current signals of thrusters.