Thruster fault identification using improved peak region energy and multiple model least square support vector data description for autonomous underwater vehicle

Abstract

This article investigates a novel fault identification approach to determine the percentage of the thrust loss for autonomous underwater vehicle thrusters. The novel approach is developed from a combination of the peak region energy (PRE) and support vector data description (SVDD) by considering that PRE is able to acquire a primary feature in low dimensions from signals without any secondary process and that SVDD can establish a hypersphere boundary for a class of fault samples even in the case of a small number of training samples. Three improvements, namely removing the fusion, an energy leakage and a homomorphic transform are applied to the PRE. It forms an improved PRE to increase the area under the curve. Furthermore, another three new contents, namely the least square, a multiple model fusion and a dead zone are added to the SVDD. It constructs a multiple model least square SVDD to increase the overall identification accuracy. Experiments are performed on an experimental prototype autonomous underwater vehicle in a pool. The experimental results indicate the effectiveness of the proposed method.

Correlation analysis and fault detection of current and speed signals for underwater thruster entanglement

“…How to extract fault features from this complex signal has always been the hot point problem of underwater thruster fault diagnosis. 10 Common fault diagnosis methods include the state observer, 11,12 hidden Markov model, 13,14 wavelet time-frequency analysis, 15 D-S evidence theory, 16 support vector machine, 17 and neural network. 18,19 The application of deep learning technology, such as neural network, to intelligent fault diagnosis of underwater thrusters has attracted the attention of researchers.…”