Weak thruster fault prediction method for autonomous underwater vehicles based on grey model

2018 ◽  
Vol 233 (3) ◽  
pp. 348-356
Author(s):  
Weixin Liu ◽  
Mingjun Zhang ◽  
Yujia Wang
Keyword(s):  
Differential Equation ◽  
Autonomous Underwater Vehicle ◽  
Autonomous Underwater Vehicles ◽  
Prediction Method ◽  
Underwater Vehicles ◽  
Fault Prediction ◽  
Grey Model ◽  
Original Sequence ◽  
Background Value ◽  
Operation Sequence

When adopting the conventional grey model (GM(1,1)) to predict weak thruster fault for autonomous underwater vehicles, the prediction error is not always satisfactory. In order to solve the problem, this article develops a new weak thruster fault prediction method based on an improved GM(1,1). In the developed GM(1,1) based fault prediction method, this article mainly makes improvement in the following aspects: construction of grey background value, solution of whiting differential equation and construction of predicted sequence. Specifically, the integral operation is used in range of the two adjacent steps to obtain the grey background value at first. Second, in the solving of whiting differential equation, the point corresponding to the least difference between the accumulated generation sequence and its predicted sequence is determined, and then this special point’s value in the original sequence is considered as the initial condition of the whiting differential equation. Third, in the construction of predicted sequence, another predicted value is obtained based on the error sequence between the accumulated generating operation sequence and its predicted sequence, and then the new predicted result is used to re-adjust the accumulated generating operation sequence, so as to guarantee the re-adjustability of the fault prediction result. Finally, experiments are performed on Beaver 2 autonomous underwater vehicle to evaluate the prediction performance of the developed method.

An improved particle filter propeller fault prediction method based on grey prediction for underwater vehicles

2020 ◽  
Vol 42 (11) ◽  
pp. 1946-1959
Author(s):  
Jiayu He ◽  
Ye Li ◽  
Jian Cao ◽  
Yueming Li ◽  
Yanqing Jiang ◽  
...  
Keyword(s):  
Particle Filter ◽  
Autonomous Underwater Vehicles ◽  
Prediction Method ◽  
Underwater Vehicles ◽  
Fault Prediction ◽  
Gaussian Kernel ◽  
Filter Method ◽  
Grey Prediction ◽  
Sea Trial ◽  
False Diagnosis

The overall architectural complexity of autonomous underwater vehicles continuous to increase, enlarging the probability of fault occurrence in subsystems. Estimating the thrust loss by particle filter provided a useful method to detect the fault in propeller subsystem. In order to detect the fault in propellers as early as possible, the particle filter direct prediction method could amplify the fault trend and detect the fault earlier, but at the same time increase the possibility of false diagnosis. Therefore, a more accurate fault diagnosis method was required to discover the fault early and decrease the occurrence of false diagnosis. In this paper, an improved particle filter prediction method was proposed, combining the advantage of grey prediction to forecast the motion state, reducing the uncertainty in particle filter direct prediction process. Besides, the Gaussian kernel function was applied to judge the credibility of the prediction result, decreasing the possibility of the false diagnosis. In the experiments with simulated working conditions data and a section of actual sea trial data with propeller fault, the proposed method detected the fault earlier compared with the original particle filter method, and reduced the false diagnosis rate compared with the particle filter direct prediction method. The results show that the proposed method is effective in detecting the fault early with low false diagnosis.

Study of Microminiature Autonomous Underwater Vehicle Hull Form Based on CFD Technique

2012 ◽  
Vol 512-515 ◽  
pp. 2682-2685 ◽  
Author(s):  
Hai Long Shen ◽  
Mo Du ◽  
Yu Min Su
Keyword(s):  
Pressure Distribution ◽  
Autonomous Underwater Vehicle ◽  
Autonomous Underwater Vehicles ◽  
Prediction Method ◽  
Turbulence Models ◽  
Underwater Vehicles ◽  
Resistance Coefficient ◽  
Hydrodynamic Performance ◽  
Calculation Results ◽  
Partition Method

Based on CFD technique，this paper discusses the applicability of different turbulence models and mesh partition method which are used to predict the hydrodynamic performance of AUV. Firstly, the hydrodynamic performance prediction method was gotten and was validated, and three different shapes autonomous underwater vehicles were designed. The hydrodynamic performances of the three autonomous underwater vehicles were predicted. Then, the advantage and disadvantage of the four autonomous underwater vehicles were obtained by comparing the resistance and pressure distribution. Based on these, two other AUV hulls were designed which combined the advantages of them, and the hydrodynamic performance was predicted. The calculation results showed that the resistance and hull pressure distribution were improved remarkably comparing with the parent model. The resistance coefficient of optimized hull is reduced by 20% compared to the parent hull.

scholarly journals Optimal design of nose and tail of an autonomous underwater vehicle hull to reduce drag force using numerical simulation

2019 ◽  
Vol 234 (1) ◽  
pp. 76-88 ◽  
Author(s):  
Mohammad Saghafi ◽  
Roham Lavimi
Keyword(s):  
Energy Consumption ◽  
Drag Force ◽  
Autonomous Underwater Vehicle ◽  
Turbulence Modeling ◽  
Stokes Equations ◽  
Autonomous Underwater Vehicles ◽  
Three Dimensional ◽  
Underwater Vehicle ◽  
Underwater Vehicles ◽  
Navier Stokes

In this research, the flow around the autonomous underwater vehicles with symmetrical bodies is numerically investigated. Increasing the drag force in autonomous underwater vehicles increases the energy consumption and decreases the duration of underwater exploration and operations. Therefore, the main objective of this research is to decrease drag force with the change in geometry to reduce energy consumption. In this study, the decreasing or increasing trends of the drag force of axisymmetric bare hulls have been studied by making alterations in the curve equations and creating the optimal geometric shapes in terms of hydrodynamics for the noses and tails of autonomous underwater vehicles. The incompressible, three-dimensional, and steady Navier–Stokes equations have been used to simulate the flow. Also, k-ε Realizable with enhanced wall treatment was used for turbulence modeling. Validation results were acceptable with respect to the 3.6% and 1.4% difference with numerical and experimental results. The results showed that all the autonomous underwater vehicle hulls designed in this study, at an attack angle of 0°, had a lower drag force than the autonomous underwater vehicle hull used for validation except geometry no. 1. In addition, nose no. 3 has been selected as the best nose according to the lowest value of stagnation pressure, and also tail no. 3 has been chosen as the best tail due to the production of the lowest vortex. Therefore, geometry no. 5 has been designed using nose and tail no. 3. The comparison made here showed that the maximum drag reduction in geometry no. 5 was equal to 26%, and therefore, it has been selected as the best bare hull in terms of hydrodynamics.

scholarly journals Report on Research with Biomimetic Autonomous Underwater Vehicle — Low Level Control

2018 ◽  
Vol 212 (1) ◽  
pp. 105-123
Author(s):  
Tomasz Praczyk ◽  
Piotr Szymak ◽  
Krzysztof Naus ◽  
Leszek Pietrukaniec ◽  
Stanisław Hożyń
Keyword(s):  
Research And Development ◽  
Autonomous Underwater Vehicle ◽  
Autonomous Underwater Vehicles ◽  
Underwater Vehicle ◽  
Underwater Vehicles ◽  
Swimming Pool ◽  
Final Report ◽  
Level Control ◽  
Low Level

Abstract The paper presents the first part of the final report on all the experiments with biomimetic autono-mous underwater vehicle (BAUV) performed within the confines of the project entitled ‘Autonomous underwater vehicles with silent undulating propulsion for underwater ISR’, financed by Polish National Center of Research and Development. The report includes experiments in the swimming pool as well as in real conditions, that is, both in a lake and in the sea. The tests presented in this part of the final report were focused on low-level control.

scholarly journals Report on Research with Biomimetic Autonomous Underwater Vehicle — Navigation and Autonomous Operation

2018 ◽  
Vol 213 (2) ◽  
pp. 53-67 ◽  
Author(s):  
Tomasz Praczyk ◽  
Piotr Szymak ◽  
Krzysztof Naus ◽  
Leszek Pietrukaniec ◽  
Stanisław Hożyń
Keyword(s):  
Research And Development ◽  
Autonomous Underwater Vehicle ◽  
Autonomous Underwater Vehicles ◽  
Underwater Vehicle ◽  
Underwater Vehicles ◽  
Swimming Pool ◽  
Vehicle Navigation ◽  
Final Report ◽  
Autonomous Operation

Abstract The paper presents the second part of the final report on all the experiments with biomimetic autonomous underwater vehicle (BAUV) performed within the confines of the project entitled ‘Autonomous underwater vehicles with silent undulating propulsion for underwater ISR’, financed by Polish National Center of Research and Development. The report includes experiments on the swimming pool as well as in real conditions, that is, both in a lake and in the sea. The tests presented in this part of the final report were focused on navigation and autonomous operation.

scholarly journals Autonomous Underwater Vehicle in Internet of Underwater Things: A Survey

2021 ◽  
Vol 2129 (1) ◽  
pp. 012080
Author(s):  
Chinonso Okereke ◽  
Nur Haliza Abdul Wahab ◽  
Mohd Murtadha Mohamad ◽  
S H Zaleha
Keyword(s):  
Energy Storage ◽  
Crucial Role ◽  
Autonomous Underwater Vehicle ◽  
Autonomous Underwater Vehicles ◽  
Underwater Vehicle ◽  
Underwater Vehicles ◽  
The Internet ◽  
The Earth ◽  
Future Work

Abstract Water, mostly oceans, covers over two-third of the earth. About 95% of these oceans are yet to be explored which includes 99% of the sea-beds. The introduction of the Internet of Underwater Things (IoUT) underwater has become a powerful technology necessary to the quest to develop a SMART Ocean. Autonomous Underwater Vehicles (AUVs) play a crucial role in this technology because of their mobility and longer energy storage. In order for AUV technologies to be effective, the challenges of AUVs must be adequately solved. This paper provides an overview of the challenges of IoUT, the contributions of AUVs in IoUT as well as the current challenges and opening in AUV. A summary and suggestion for future work was discussed.

Safe Navigation Algorithm for Autonomous Underwater Vehicles

2021 ◽  
Vol 29 (1) ◽  
pp. 97-110
Author(s):  
V.S. Bykova ◽  
◽  
A.I. Mashoshin ◽  
I.V. Pashkevich ◽  
◽  
...  
Keyword(s):  
Control System ◽  
Autonomous Underwater Vehicle ◽  
Autonomous Underwater Vehicles ◽  
Underwater Vehicle ◽  
Underwater Vehicles ◽  
Limited Size ◽  
Safe Navigation ◽  
Navigation Algorithm ◽  
Navigation Algorithms ◽  
Ice Edge

Two safe navigation algorithms for autonomous underwater vehicles are described: algorithm for avoidance of point obstacles including all the moving underwater and surface objects, and limited size bottom objects, and algorithm for bypassing extended obstacles such as bottom elevations, rough lower ice edge, garbage patches. These algorithms are developed for a control system of a heavyweight autonomous underwater vehicle.

Multi-autonomous underwater vehicles collaboratively search for intelligent targets in an unknown environment in the presence of interception

2020 ◽  
pp. 095440622094362
Author(s):  
Xi-wen Ma ◽  
Yan-li Chen ◽  
Gui-qiang Bai ◽  
Yong-bai Sha ◽  
Jun Liu
Keyword(s):  
Autonomous Underwater Vehicle ◽  
Autonomous Underwater Vehicles ◽  
Full Range ◽  
Underwater Vehicle ◽  
Underwater Vehicles ◽  
Layer By Layer ◽  
Self Organizing Map ◽  
Unknown Environment ◽  
Underwater Environment ◽  
Wave Diffusion

We present a bionic neural wave network that uses multiple autonomous underwater vehicles to search and acquire intelligent targets in an unknown underwater environment. The neuron pheromone content is arranged according to neural wave diffusion and layer-by-layer energy attenuation, when underwater mesh space based on neural wave diffusion theory was established that the neuron nodes in the neural network structure correspond to obstacles, autonomous underwater vehicles, and targets in the environment. In order to solve the problems of over-allocation and under-allocation of the multi-autonomous underwater vehicles system during the cooperative capture of targets, a redistribution mechanism based on the improved self-organizing map algorithm is implemented and directed to rationalize task distribution. Two different taboo search methods are employed to update the autonomous underwater vehicle path in real time, and the polynomial coefficient solution method is used to fit partial path data. So that the autonomous underwater vehicle trajectory can be obtained and an interceptor position coordinate can be predicted. An auxiliary autonomous underwater vehicle is aimed to replace the intercepted autonomous underwater vehicle and the matching capture points are tracked to ensure the completion of the task so that the full range of hunting targets is identified. In order to simulate an unknown complex underwater environment, obstacles are randomly arranged around the target, the location information of the obstacle, and the target is unknown and unpredictable. Four simulation experiments were performed to verify the accuracy and efficiency of the algorithm under unknown environment. The results show that this algorithm can improve the path update average efficiency by 66% compared with other algorithms. Obviously, this algorithm is reasonable and effective.

scholarly journals Updating Autonomous Underwater Vehicle Risk Based on the Effectiveness of Failure Prevention and Correction

2018 ◽  
Vol 35 (4) ◽  
pp. 797-808 ◽  
Author(s):  
Mario P. Brito ◽  
Gwyn Griffiths
Keyword(s):  
Autonomous Underwater Vehicle ◽  
Risk Mitigation ◽  
Autonomous Underwater Vehicles ◽  
Underwater Vehicle ◽  
Underwater Vehicles ◽  
Probability Of Failure ◽  
Risk Profile ◽  
Failure Prevention ◽  
Significant Difference

AbstractAutonomous underwater vehicles (AUVs) have proven to be feasible platforms for marine observations. Risk and reliability studies on the performance of these vehicles by different groups show a significant difference in reliability, with the observation that the outcomes depend on whether the vehicles are operated by developers or nondevelopers. This paper shows that this difference in reliability is due to the failure prevention and correction procedures—risk mitigation—put in place by developers. However, no formalization has been developed for updating the risk profile based on the expected effectiveness of the failure prevention and correction process. A generic Bayesian approach for updating the risk profile is presented, based on the probability of failure prevention and correction and the number of subsequent deployments on which the failure does not occur. The approach, which applies whether the risk profile is captured in a parametric or nonparametric survival model, is applied to a real case study of the International Submarine Engineering Ltd. (ISE) Explorer AUV.

scholarly journals An evaluation of path-planning methods for autonomous underwater vehicle based on terrain-aided navigation

2019 ◽  
Vol 16 (3) ◽  
pp. 172988141985318
Author(s):  
Zheng Cong ◽  
Ye Li ◽  
Yanqing Jiang ◽  
Teng Ma ◽  
Yusen Gong ◽  
...  
Keyword(s):  
Path Planning ◽  
Ant Colony Algorithm ◽  
Autonomous Underwater Vehicle ◽  
Autonomous Underwater Vehicles ◽  
Underwater Vehicle ◽  
Underwater Vehicles ◽  
Positioning Accuracy ◽  
Planning Methods ◽  
Planning Algorithms ◽  
Motion Characteristics

This article presents a comparison of different path-planning algorithms for autonomous underwater vehicles using terrain-aided navigation. Four different path-planning methods are discussed: the genetic algorithm, the A* algorithm, the rapidly exploring random tree* algorithm, and the ant colony algorithm. The goal of this article is to compare the four methods to determine how to obtain better positioning accuracy when using terrain-aided navigation as a means of navigation. Each algorithm combines terrain complexity to comprehensively consider the motion characteristics of the autonomous underwater vehicles, giving reachable path between the start and end points. Terrain-aided navigation overcomes the challenges of underwater domain, such as visual distortion and radio frequency signal attenuation, which make landmark-based localization infeasible. The path-planning algorithms improve the terrain-aided navigation positioning accuracy by considering terrain complexity. To evaluate the four algorithms, we designed simulation experiments that use real-word seabed bathymetry data. The results of autonomous underwater vehicle navigation by terrain-aided navigation in these four cases are obtained and analyzed.

Sign in / Sign up

Export Citation Format

Share Document