Sensor Fault Diagnosis of Autonomous Underwater Vehicle Based on Extreme Learning Machine

被引：0

作者：

Li, Xun ^{[1
]}

Song, Yan ^{[1
]}

Guo, Jia ^{[1
]}

Feng, Chen ^{[1
]}

Li, Guangliang ^{[1
]}

Yan, Tianhong ^{[2
]}

He, Bo ^{[1
]}

机构：

[1] Ocean Univ China, Coll Informat Sci & Engn, Songling Rd 238, Qingdao, Peoples R China

[2] China Jiliang Univ, Dept Mech & Elect Engn, Hangzhou, Zhejiang, Peoples R China

来源：

2017 IEEE UNDERWATER TECHNOLOGY (UT) | 2017年

关键词：

Autonomous underwater vehicle; Sensor fault diagnosis; Phase space reconstruction; Extreme learning machine;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Autonomous underwater vehicles (AUVs) work in complex marine environments, and sensors play an important role in AUV systems. Therefore, research on sensor failure diagnosis technology is important for improving the reliability of AUV systems. In this paper, a new method combining phase space reconstruction and extreme learning machine (ELM) is proposed. This method is applied to predict sensor output to achieve sensor fault diagnosis for AUVs. The results of the simulation experiments based on sea trial data shown that the proposed method can diagnose sensor faults and recover the signal after faults occur in a period of time.

引用

页数：5

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