Jihoon Lee, Seungmin Oh, Yeonggwang Kim, Dongsu Lee, Akm Ashiquzzaman, Jinsul Kim
{"title":"Pig Farm Environment Sensor Data Correlation and Heatmap Analysis for Predicting Sensor Remaining Useful Life✱","authors":"Jihoon Lee, Seungmin Oh, Yeonggwang Kim, Dongsu Lee, Akm Ashiquzzaman, Jinsul Kim","doi":"10.1145/3426020.3426136","DOIUrl":null,"url":null,"abstract":"Various smart farm technologies are currently being developed around the world to enhance agricultural competitiveness. Korea is also speeding up the development of Korean smart farm technology suitable for domestic environment, but it is difficult to develop high-reliability sensors and systems, and has problems such as preventing sensors from failing, so in this paper, environmental data values such as temperature, humidity, carbon dioxide, ammonia, etc. are sensed, refined, and pretreated to derive correlation and heat maps between sensors. This will not only predict the RUL (Remaining Useful Life) of the sensor using machine learning in the future, but also develop a reliable system by detecting failures and errors.","PeriodicalId":305132,"journal":{"name":"The 9th International Conference on Smart Media and Applications","volume":"28 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"The 9th International Conference on Smart Media and Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3426020.3426136","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
Various smart farm technologies are currently being developed around the world to enhance agricultural competitiveness. Korea is also speeding up the development of Korean smart farm technology suitable for domestic environment, but it is difficult to develop high-reliability sensors and systems, and has problems such as preventing sensors from failing, so in this paper, environmental data values such as temperature, humidity, carbon dioxide, ammonia, etc. are sensed, refined, and pretreated to derive correlation and heat maps between sensors. This will not only predict the RUL (Remaining Useful Life) of the sensor using machine learning in the future, but also develop a reliable system by detecting failures and errors.
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