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

International Journal of Advanced Robotic Systems

JiayuHe¹ 

关键词: Autonomous underwater vehicle;    status estimation;    fault diagnosis;    grey prediction;    particle filter;   

DOI  :  10.1177/1729881418814683

学科分类：自动化工程

来源: InTech

【 摘 要 】

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.

【 授权许可】

CC BY