Modeling and Soft-fault Diagnosis of Underwater Thrusters with Recurrent Neural Networks
This addresses reliability monitoring for AUVs in marine environments, but it is incremental as it applies existing RNN methods to a specific domain.
The paper tackled the problem of detecting and diagnosing soft-faults in underwater thrusters for autonomous underwater vehicles, using recurrent neural networks on empirical data, and found that using computed residuals as features improved fault classification performance.
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.