Sensor Fault Diagnosis of Autonomous Underwater Vehicle Based on LSTM

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	Sensor Fault Diagnosis of Autonomous Underwater Vehicle Based on LSTM
	Lu, Jun 2; He X(何旭)1 ; Gao S(高升)1; Zhang W(张伟)1 ; Qin XC(秦晓成)2
	2018
会议日期	July 25-27, 2018
会议地点	Wuhan, China
关键词	Autonomous underwater vehicle Fault diagnosis Sensor fault LSTM
页码	6067-6072
英文摘要	Autonomous underwater vehicle (AUV) is a complex nonlinear system, and it is difficult to establish its accurate mathematical model and further deal with fault diagnosis problem. Therefore, a novel fault diagnosis method based on a predictive model with using the Long Short-Term Memory Network (LSTM) is proposed in this paper. First, the LSTM network is trained by AUV experimental data, such as acceleration navigation, changing the depth of navigation, changing the direction of navigation and changing the speed of navigation. Then, the trained network can be used to establish the motion model of AUV by fitting the sensor system. Furthermore, the residual generated by comparing the output of the predictive model of the sensors with the actual measured value of the sensors can be used to diagnose sensors fault of AUV, which contains a lot of fault information. Finally, sensor fault diagnosis simulation experiment of the AUV is carried out. The results of the simulation show that the method is effective. © 2018 Technical Committee on Control Theory, Chinese Association of Automation.
源文献作者	Academy of Mathematics and Systems Science (AMSS), CAS ; China Society for Industrial and Applied Mathematics ; et al. ; Huazhong University of Science and Technology ; Hubei Association of Automation ; Wuhan University of Science and Technology
产权排序	2
会议录	Proceedings of the 37th Chinese Control Conference
会议录出版者	IEEE Computer Society
会议录出版地	New York
语种	英语
ISSN号	2161-2927
ISBN号	978-988-15639-5-8
WOS记录号	WOS:000468622400139
内容类型	会议论文
源URL	[http://ir.sia.cn/handle/173321/24123]
专题	沈阳自动化研究所_空间自动化技术研究室
通讯作者	Qin XC(秦晓成)
作者单位	1.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China 2.Shenyang Ligong University, Shenyang 110159, China
推荐引用方式 GB/T 7714	Lu, Jun,He X,Gao S,et al. Sensor Fault Diagnosis of Autonomous Underwater Vehicle Based on LSTM[C]. 见:. Wuhan, China. July 25-27, 2018.