Title: Modeling and Soft-fault Diagnosis of Underwater Thrusters with Recurrent Neural Networks,
Abstract: Noncritical soft-faults and model deviations are a challenge for Fault
Detection and Diagnosis (FDD) of resident Autonomous Underwater Vehicles
(AUVs). Such systems may have a faster performance degradation due to the
permanent exposure to the marine environment, and constant monitoring of
component conditions is required to ensure their reliability. This works
presents an evaluation of Recurrent Neural Networks (RNNs) for a data-driven
fault detection and diagnosis scheme for underwater thrusters with empirical
data. The nominal behavior of the thruster was modeled using the measured
control input, voltage, rotational speed and current signals. We evaluated the
performance of fault classification using all the measured signals compared to
using the computed residuals from the nominal model as features.