Enhancing underwater thruster anomaly detection with support vector glow encoding description

Abstract

As the driving system of autonomous underwater vehicles (AUVs), the healthy operation of underwater thrusters is crucial to ensure the performance of AUVs. Due to the challenge of obtaining faulty samples during normal operation, anomaly detection of underwater thrusters is often carried out using only normal samples. To address this, Support Vector Glow Encoding Description (SVGED) is proposed for anomaly detection of underwater thrusters trained with only normal samples. Specifically, the deep features of the operational vibration signals collected from the underwater thruster are extracted by a deep convolutional autoencoder based on a glow model. The glow-encoded data are then used to identify the boundary of normal operation for the underwater thruster. When a new sample enters the trained SVGED model, it can be effectively identified as representing a normal or anomalous condition of the underwater thruster. The developed SVGED was evaluated using AUV experiments. When using the proposed SVGED method, the accuracy reaches 99.81%, Results show that the proposed method can effectively real time detect anomalies in the underwater thruster compared to other methods. It lays the foundation for ensuring the healthy operation of AUVs.