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.

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.

scholarly journals Clustering Cloud-Like Model-Based Targets Underwater Tracking for AUVs

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 370 ◽  
Author(s):  
Mingwei Sheng ◽  
Songqi Tang ◽  
Hongde Qin ◽  
Lei Wan
Keyword(s):  
Nearest Neighbor ◽  
Autonomous Underwater Vehicles ◽  
Data Association ◽  
Underwater Vehicles ◽  
Near Neighbor ◽  
Cluster Center ◽  
Model Data ◽  
Sea Trial ◽  
Underwater Target ◽  
Imaging Sonar

Autonomous underwater vehicles (AUVs) rely on a mechanically scanned imaging sonar that is fixedly mounted on AUVs for underwater target barrier-avoiding and tracking. When underwater targets cross or approach each other, AUVs sometimes fail to track, or follow the wrong target because of the incorrect association of the multi-target. Therefore, a tracking method adopting the cloud-like model data association algorithm is presented in order to track underwater multiple targets. The clustering cloud-like model (CCM) not only combines the fuzziness and randomness of the qualitative concept, but also achieves the conversion of the quantitative values. Additionally, the nearest neighbor algorithm is also involved in finding the cluster center paired to each target trajectory, and the hardware architecture of AUVs is proposed. A sea trial adopting a mechanically scanned imaging sonar fixedly mounted on an AUV is carried out in order to verify the effectiveness of the proposed algorithm. Experiment results demonstrate that compared with the joint probabilistic data association (JPDA) and near neighbor data association (NNDA) algorithms, the new algorithm has the characteristic of more accurate clustering.

scholarly journals PROPELLER FAULT DIAGNOSIS BASED ON A RANK PARTICLE FILTER FOR AUTONOMOUS UNDERWATER VEHICLES

Brodogradnja ◽  
2018 ◽  
Vol 69 (2) ◽  
pp. 147-164 ◽  
Author(s):  
Jiayu He ◽  
◽  
Ye Li ◽  
Yanqing Jiang ◽  
Yueming Li ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Particle Filter ◽  
Autonomous Underwater Vehicles ◽  
Underwater Vehicles

scholarly journals Substation DC system grounding fault prediction method

2021 ◽  
Vol 252 ◽  
pp. 01036
Author(s):  
Guozhong Wang
Keyword(s):  
Leakage Current ◽  
Prediction Error ◽  
Prediction Method ◽  
Fault Prediction ◽  
Prediction Algorithm ◽  
Grey Prediction ◽  
Dc System ◽  
Ground Fault ◽  
Fault Parameters ◽  
The Future

There may be disturbance and uncertainty in the collection of leakage current in DC system of substation, which leads to the decrease of accuracy and increase of prediction error. Based on this, an improved grey prediction method is proposed to predict DC system branch grounding fault. Firstly, the characteristics of DC system ground fault parameters are collected. Secondly, the improved grey prediction algorithm is used to predict and estimate whether the detection reaches the fault threshold in the future. Finally, the validity of the proposed method is verified by MATLAB modeling.

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 Fault diagnosis in autonomous underwater vehicle propeller in the transition stage based on GP-RPF

2018 ◽  
Vol 15 (6) ◽  
pp. 172988141881468
Author(s):  
Jiayu He ◽  
Ye Li ◽  
Yueming Li ◽  
Yanqing Jiang ◽  
Li An
Keyword(s):  
Fault Diagnosis ◽  
Particle Filter ◽  
Estimation Error ◽  
Autonomous Underwater Vehicles ◽  
Loss Estimation ◽  
Control Signal ◽  
Grey Prediction ◽  
Transition Stage ◽  
The Status ◽  
Diagnosis Method

Propellers are one of the key parts on the autonomous underwater vehicles. When adopting the conventional particle filter to estimate the degree of fault, based on the status given by the sensors, the diagnosis value is not always satisfactory in the transition stage (as it accelerates substantially). The diagnosis value is relatively larger than it is in the cruising stage, and this might weaken the ability to classify using the fault diagnosis method. This article proposes a new fault diagnosis method combining the grey prediction and rank particle filter method. The main improvements include two aspects: status input prediction and thrust loss trend analysis. The status input into the rank particle filter is predicted by the grey prediction method, to meet the condition that the thrust loss estimation does not change quickly when the control signal changes drastically. Subsequently, the control signal change rate is combined to analyse the thrust loss change trend. This improvement reduces the diagnosis value under normal conditions and enlarges the ratio between faulty and normal conditions. Simulation experiments are carried out to verify the performance of the proposed algorithm. The results show that the proposed method could reduce the thrust loss estimation error and enlarge the ratio of diagnosis value between faulty and normal conditions, providing basis for the following operation.

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