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Alternative TitleThruster Fault Signal Diagnosis of Underwater Vehicle
徐高朋1,2; 李硕1; 曾俊宝1,2; 李一平1
Source Publication计算机仿真
Volume36Issue:7Pages:296-301, 327
Contribution Rank1
Funding Organization国家重点研发计划项目( 2016YFC0300604,2017YFC0305901) ; 中国科学院战略性先导科技专项(XDA13030205,XDB06050200)
Keyword水下机器人 推进器故障诊断 小波包变换 遗传算法 神经网络
Other AbstractDuring underwater vehicle operation,thruster fault may appear because of winding and blade damage. Traditional thruster fault diagnosis method mainly diagnoses fault by comparing the difference between the measured motion status and the estimated motion status of the underwater vehicle. Performance of traditional methods is greatly affected by the accuracy of the mathematical model of underwater vehicle and those methods are unable to achieve early diagnosis of thruster faults. In order to realize the early diagnosis of thruster faults for underwater vehicle,we proposed a fault diagnosis method for underwater vehicle thrusters based on wavelet packet transform and genetic algorithm optimization BP neural network was. Firstly,the wavelet packet transform was utilized to decompose the current signal of underwater vehicle thruster. Energy spectrum of the decomposed current signal was calculated. And then,energy spectrum components with obvious differences under different fault conditions were chosen to compose eigenvectors characterizing thruster faults. Finally,a fault classifier based on genetic algorithm optimized BP neural network was trained to identify different fault conditions of underwater vehicle thruster. The experimental results show that the proposed algorithm can effectively use transient features of thruster faults and achieve preferable performance for the fault diagnosis of underwater vehicle thruster.
Document Type期刊论文
Corresponding Author李硕
Recommended Citation
GB/T 7714
徐高朋,李硕,曾俊宝,等. 水下机器人推进器故障信号诊断[J]. 计算机仿真,2019,36(7):296-301, 327.
APA 徐高朋,李硕,曾俊宝,&李一平.(2019).水下机器人推进器故障信号诊断.计算机仿真,36(7),296-301, 327.
MLA 徐高朋,et al."水下机器人推进器故障信号诊断".计算机仿真 36.7(2019):296-301, 327.
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