Multi-fault diagnosis for autonomous underwater vehicle based on fuzzy weighted support vector domain description

An autonomous underwater vehicle (AUV) should have the ability of adapting the complexity and unpredictability of the marine environment, which means that the technology of AUV’s fault diagnosis is very significant, especially the part of thrusters. In order to make it possible, one fault diagnosis strategy of AUV’s thrusters is proposed, which is based on the support vector machine (SVM). SVM has many unique advantages in solving small-sample, nonlinear and high dimensional problems. In this paper, different character signal is inputted SVM to train and test it. The simulation results show that the fault diagnosis of AUV’s thrusters based on offline SVM can classify the fault styles successfully, which proves its feasibility and effectiveness. This method offers a new way to solve the fault diagnosis of AUVs.

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Thruster fault diagnosis in autonomous underwater vehicle based on grey qualitative simulation

Ocean Engineering ◽

10.1016/j.oceaneng.2015.06.037 ◽

2015 ◽

Vol 105 ◽

pp. 247-255 ◽

Cited By ~ 11

Author(s):

Ming-jun Zhang ◽

Yu-jia Wang ◽

Jian-an Xu ◽

Zheng-chen Liu

Keyword(s):

Fault Diagnosis ◽

Autonomous Underwater Vehicle ◽

Underwater Vehicle ◽

Qualitative Simulation

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Actuator Weak Fault Diagnosis in Autonomous Underwater Vehicle Based on Tri-Stable Stochastic Resonance

Applied Sciences ◽

10.3390/app10062048 ◽

2020 ◽

Vol 10 (6) ◽

pp. 2048 ◽

Cited By ~ 1

Author(s):

Yang Jiang ◽

Bo He ◽

Jia Guo ◽

Pengfei Lv ◽

Xiaokai Mu ◽

...

Keyword(s):

Fault Diagnosis ◽

Stochastic Resonance ◽

Autonomous Underwater Vehicle ◽

Underwater Vehicle ◽

Ant Colony ◽

Resonance System ◽

Resonance Model ◽

Weak Fault

The autonomous underwater vehicle (AUV) is mainly used in the development and exploration of the ocean. As an important module of the AUV, the actuator plays an important role in the normal execution of the AUV. Therefore, the fault diagnosis of the actuator is particularly important. At present, the research on the strong faults, such as the winding of the actuator, has achieved good results, but the research on the weak fault diagnosis is relatively rare. In this paper, the tri-stable stochastic resonance model is analyzed, and the ant colony tri-stable stochastic resonance model is used to diagnose the weak fault. The system accurately diagnoses the fault of the actuator collision and verifies the adaptive tri-stable stochastic resonance system. This model has better diagnostic results than the bi-stable stochastic resonance system.

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A Research on the Simultaneous Localization Method in the Process of Autonomous Underwater Vehicle Homing with Unknown Varying Measurement Error

Applied Sciences ◽

10.3390/app9214614 ◽

2019 ◽

Vol 9 (21) ◽

pp. 4614

Author(s):

Lingyan Dong ◽

Hongli Xu ◽

Xisheng Feng ◽

Xiaojun Han ◽

Chuang Yu

Keyword(s):

Measurement Error ◽

Autonomous Underwater Vehicle ◽

Underwater Vehicle ◽

Gaussian Mixture ◽

Statistical Characteristics ◽

Observation System ◽

Support Vector ◽

Position Information ◽

Localization Method ◽

Working Distance

We propose an acoustic-based framework for automatically homing an Autonomous Underwater Vehicle (AUV) to the fixed docking station (F-DS) and mobile docking station (M-DS). The proposed framework contains a simultaneous localization method of AUV and docking station (DS) and a guidance method based on the position information. The Simultaneous localization and mapping (SLAM) algorithm is not available as the statistical characteristics of the measurement error of the observation system are unknown. To solve this problem, we propose a data pre-processing method. Firstly, the measurement error data of acoustic sensor are collected. Then, We propose a Variational Auto-Encoder (VAE) based Gaussian mixture model (GMM) for estimating the statistical characteristics of measurement error. Finally, we propose a support vector regression (SVR) algorithm to fit the non-linear relationship between the statistical characteristics of measurement error and its corresponding working distance. We adopt a guidance method based on line-of-sight (LOS) and path tracking method for homing an AUV to the fixed docking station (F-DS) and mobile docking station (M-DS). The lake experimental data are used to verify the performance of the localization with the estimated statistical characteristics of measurement error.

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