An Underwater Thruster Fault Diagnosis Simulator and Thrust Calculation Method Based on Fault Clustering

Abstract

In order to study the fault diagnosis method of small underwater thruster, an experimental device for fault diagnosis of underwater thruster is designed, and a controller hardware and monitoring software of upper computer and lower computer are developed to realize the acquisition and storage of parameters for underwater propeller. The experimental device can simulate four kinds of thruster faults, collect the hydrophone data, classify the fault types by fault clustering analysis, analyze the spectrum of four types of faults, and calculate the thrust under different fault conditions based on the results of spectrum analysis. The experimental results show that the experimental system effectively simulates different faults of the thruster, and the analysis method realizes the classification of different faults. The thrust loss of different faults is also calculated based on the analysis method.

Unmanned Surface Vehicle Thruster Fault Diagnosis via Vibration Signal Wavelet Transform and Vision Transformer under Varying Rotational Speed Conditions

“…A test bed and data collection system for thruster experimentation were used and research was conducted on fault diagnosis using underwater acoustic data. Clustering and frequency analysis-based methods were used to identify fault features [ 34 ].…”

“…To address the inherent challenges in fault diagnosis via hull vibrations and to diagnose faults at varying rotational speeds of USVs’ propulsion systems, the Continuous Wavelet Transform (CWT) was applied to convert time-series vibration data into scalograms [ 30 , 31 ]. Although a number of methodologies have been applied for fault diagnosis at a constant rotation speed of USV thrusters [ 32 , 33 , 34 ], analyzing varying rotational speeds demands a methodology that simultaneously accounts for attributes in both the temporal and frequency domains. CWT, renowned for its ability to encapsulate both the physical characteristics and time-frequency domain nuances, has been extensively researched and validated within the realm of Physics-Informed Neural Networks (PINNs) [ 35 ].…”

“…Although a number of methodologies have been applied for fault diagnosis at a constant rotation speed of USV thrusters [32][33][34], analyzing varying rotational speeds demands a methodology that simultaneously accounts for attributes in both the temporal and frequency domains. CWT, renowned for its ability to encapsulate both the physical characteristics and time-frequency domain nuances, has been extensively researched and validated within the realm of Physics-Informed Neural Networks (PINNs) [35].…”

Thruster Fault Diagnostics and Fault Tolerant Control for Autonomous Underwater Vehicle with Ocean Currents

“…An autonomous underwater vehicle (AUV) is one of the most important exploration tools in the ocean underwater environment. As an important part of AUV, the thruster directly determines the efficiency and safety with strong working intensity for AUV, However, the thruster fault usually happens in engineering practice [1,2]. Therefore, how to make thruster fault diagnosis and fault tolerant control for AUV is the premise for completing underwater missions [3,4].…”