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水下机器人推进器故障信号诊断
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
Department水下机器人研究室
Source Publication计算机仿真
ISSN1006-9348
2019
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
Abstract水下机器人作业过程中,推进器会因为异物缠绕和桨叶受损而出现故障。传统推进器故障诊断方法,主要通过比较水下机器人运动状态的测量值和估计值之间的差别来诊断故障,诊断精度受水下机器人数学模型的影响较大,且无法在故障出现早期对运动状态影响较小时实现诊断。为实现推进器故障的早期诊断,提出了一种基于小波包变换和遗传算法优化BP神经网络的推进器故障诊断方法。首先,利用小波包变换对推进器电流信号进行分解,并计算分解后电流信号的能量谱;然后筛选在不同推进器故障状态下差别较明显的能量谱分量,组成表征推进器故障的特征向量。最后采用基于遗传算法优化的BP神经网络训练故障分类器,实现故障的识别。实验结果表明,上述方法能够有效利用推进器故障的瞬时特征,对水下机器人推进器故障诊断具有良好的效果。
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.
Language中文
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/25453
Collection水下机器人研究室
Corresponding Author李硕
Affiliation1.中国科学院沈阳自动化研究所
2.中国科学院大学
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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