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

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.

Sensor Fault Diagnosis for Autonomous Underwater Vehicles

2011 ◽  
Vol 9 (5) ◽  
pp. 2062-2066
Author(s):  
Xiao Liang ◽  
Jundong Zhang ◽  
Wei Li ◽  
Jianguo Lin
Keyword(s):  
Fault Diagnosis ◽  
Autonomous Underwater Vehicles ◽  
Underwater Vehicles ◽  
Sensor Fault

Automatic fault diagnosis for autonomous underwater vehicles using online topic models

2016 ◽  
Author(s):  
Ben-Yair Raanan ◽  
James G. Bellingham ◽  
Yanwu Zhang ◽  
Mathieu Kemp ◽  
Brian Kieft ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Autonomous Underwater Vehicles ◽  
Topic Models ◽  
Underwater Vehicles

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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