A Fault Location Algorithm Based on Convolutional Neural Network for Sensor System of Seafloor Observatory Network
文献类型：会议论文
| 作者 | Sun K(孙凯)
|
| 出版日期 | 2019 |
| 会议日期 | June 17-20, 2019 |
| 会议地点 | Marseille, France |
| 关键词 | seafloor observatory network data transmission system fault location convolutional neural network |
| 页码 | 1-5 |
| 英文摘要 | The seafloor observatory network (SFON) covers an extensive area and consists of many network devices functioning in the abyssal environment, which make patrolling inapplicable to fault location in the marine setting. Moreover, finding faults like degradation of precision or zero drift would be rather difficult if such faults are only located by the warning message from a single sensor. To solve this problem and as per the features of SFON, we propose a fault location algorithm based on the convolutional neural network (CNN) for the data transmission system. This algorithm which takes a holistic perspective and considers the features of network device can monitor all the sensors in a unified and centralized way. The algorithm sets the CNN parameters according to the features of the research object, and normalizes the data of sensors to images. It first qualitatively judges a fault, and then recognizes its source and type. The new algorithm has higher precision on fault recognition than the support vector machine. |
| 产权排序 | 1 |
| 会议录 | OCEANS 2019 Marseille
 |
| 会议录出版者 | IEEE |
| 会议录出版地 | New York |
| 语种 | 英语 |
| ISBN号 | 978-1-7281-1450-7 |
| 源URL | [http://ir.sia.cn/handle/173321/26046]  |
| 专题 | 沈阳自动化研究所_海洋信息技术装备中心 |
| 通讯作者 | Sun K(孙凯) |
| 作者单位 | Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China |
| 推荐引用方式 GB/T 7714 | Sun K. A Fault Location Algorithm Based on Convolutional Neural Network for Sensor System of Seafloor Observatory Network[C]. 见:. Marseille, France. June 17-20, 2019. |
入库方式： OAI收割
来源：沈阳自动化研究所
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