An intelligent data analysis system for fault diagnosis of marine monitoring sensors

Shiyao Zhao, Miaomiao Song, Yunlu Liu, Shuting Liu, Jianjia Zheng, Sijia Wang, Peng Tai, Ziliang Jiang
{"title":"An intelligent data analysis system for fault diagnosis of marine monitoring sensors","authors":"Shiyao Zhao, Miaomiao Song, Yunlu Liu, Shuting Liu, Jianjia Zheng, Sijia Wang, Peng Tai, Ziliang Jiang","doi":"10.1109/cniot55862.2022.00044","DOIUrl":null,"url":null,"abstract":"In order to improve the accuracy of the monitoring data of marine monitoring sensors, diagnose various potential faults of the sensors and repair them in time, an intelligent data analysis system for diagnosing the faults of the marine monitoring sensor is proposed and developed. Three kinds of data analysis methods based on principle of statistics, including the Grubbs Criterion, the PauTa Criterion and the Dixon Criterion, are used to realize the automatic detection of abnormal data and the corresponding algorithm workflow is designed and implemented with Python intelligent computing modules. Taking the wave sensor data as an example, a set of experiments are conducted to verify the effectiveness of the intelligent system. The results indicate that the system ensures the effectiveness and accuracy of monitoring data properly and it can be used to monitor and analyze the abnormal data of wave sensors for fault diagnosis.","PeriodicalId":251734,"journal":{"name":"2022 3rd International Conference on Computing, Networks and Internet of Things (CNIOT)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 3rd International Conference on Computing, Networks and Internet of Things (CNIOT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/cniot55862.2022.00044","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

In order to improve the accuracy of the monitoring data of marine monitoring sensors, diagnose various potential faults of the sensors and repair them in time, an intelligent data analysis system for diagnosing the faults of the marine monitoring sensor is proposed and developed. Three kinds of data analysis methods based on principle of statistics, including the Grubbs Criterion, the PauTa Criterion and the Dixon Criterion, are used to realize the automatic detection of abnormal data and the corresponding algorithm workflow is designed and implemented with Python intelligent computing modules. Taking the wave sensor data as an example, a set of experiments are conducted to verify the effectiveness of the intelligent system. The results indicate that the system ensures the effectiveness and accuracy of monitoring data properly and it can be used to monitor and analyze the abnormal data of wave sensors for fault diagnosis.
用于船舶监测传感器故障诊断的智能数据分析系统
为了提高海洋监测传感器监测数据的准确性,及时诊断传感器的各种潜在故障并进行修复,提出并开发了一种海洋监测传感器故障诊断智能数据分析系统。采用基于统计学原理的Grubbs准则、PauTa准则和Dixon准则三种数据分析方法,实现了异常数据的自动检测,并利用Python智能计算模块设计实现了相应的算法工作流。以波浪传感器数据为例,进行了一组实验,验证了智能系统的有效性。结果表明,该系统能较好地保证监测数据的有效性和准确性,可用于对波浪传感器异常数据的监测和分析,用于故障诊断。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信