计算机科学
人工智能
卷积(计算机科学)
管道运输
联营
编码器
编码(社会科学)
深度学习
管道(软件)
模式识别(心理学)
泄漏(经济)
鉴定(生物学)
特征提取
特征(语言学)
算法
工程类
人工神经网络
数学
语言学
统计
植物
哲学
环境工程
经济
程序设计语言
操作系统
生物
宏观经济学
作者
Xianming Lang,Dan Sui,Zefeng Cai,Zaihong Cai,Yue Lv
标识
DOI:10.1109/iai59504.2023.10327596
摘要
To solve the problem of difficult identification of pipeline signals under different working conditions, a one-dimensional deep convolution self-coding method for pipeline leakage identification was proposed. One-dimensional deep convolution and adaptive encoder are used to conduct unsupervised learning training for signal features, extract data features, and learn data feature information through the convolution layer and pooling layer. Finally, the results of pipeline working condition discrimination are output. The experimental results show that the method can accurately judge the working conditions of different types of pipelines with an accuracy of 97.37%. The superiority of this method in leakage diagnosis is verified by comparison with other methods.
科研通智能强力驱动
Strongly Powered by AbleSci AI