{"title":"基于分布式光纤传感器和卷积神经网络(cnn)的管道退化评估","authors":"Zekun Wu, Qirui Wang, A. Gribok, Kevin P. Chen","doi":"10.1364/ofs.2022.w4.41","DOIUrl":null,"url":null,"abstract":"We present a machine learning method to analyze data harnessed by distributed fiber sensors for pipeline monitoring. Convolutional neural networks are used to identify and classify pipeline internal defects with 99% and 94% accuracy, respectively.","PeriodicalId":265406,"journal":{"name":"27th International Conference on Optical Fiber Sensors","volume":"23 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Pipeline Degradation Evaluation Based on Distributed Fiber Sensors and Convolutional Neural Networks (CNNs)\",\"authors\":\"Zekun Wu, Qirui Wang, A. Gribok, Kevin P. Chen\",\"doi\":\"10.1364/ofs.2022.w4.41\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We present a machine learning method to analyze data harnessed by distributed fiber sensors for pipeline monitoring. Convolutional neural networks are used to identify and classify pipeline internal defects with 99% and 94% accuracy, respectively.\",\"PeriodicalId\":265406,\"journal\":{\"name\":\"27th International Conference on Optical Fiber Sensors\",\"volume\":\"23 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"27th International Conference on Optical Fiber Sensors\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1364/ofs.2022.w4.41\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"27th International Conference on Optical Fiber Sensors","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1364/ofs.2022.w4.41","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Pipeline Degradation Evaluation Based on Distributed Fiber Sensors and Convolutional Neural Networks (CNNs)
We present a machine learning method to analyze data harnessed by distributed fiber sensors for pipeline monitoring. Convolutional neural networks are used to identify and classify pipeline internal defects with 99% and 94% accuracy, respectively.
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