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发布时间：2019-03-09

论文类型：期刊论文

发表时间：2014-09-01

发表刊物：OPTIK

收录刊物：EI、SCIE

卷号：125

期号：18

页面范围：5426-5431

ISSN号：0030-4026

关键字：Scour; Monitoring; Active thermometry; BP neural network; Subsea pipelines

摘要：Scour monitoring is an important concern for subsea pipeline systems. The active-thermometry-based scour monitoring is based on the difference of heat transfer properties between sediment and sand, recognizes the surrounding media though temperature changing patterns during heating and cooling processes, and hence detects the free spans. Based on the scour monitoring system, a two-layer BP neural network was employed to process the monitoring data and achieved media recognition. The network's inputs were normalized temperature time histories. The network's outputs denoted different media: sediment and water. To validate the method, three experiments were conducted; one was used for training the network and the other two for testing. Also, the effect of noise on the network's performance was studied through simulation. The results demonstrated the feasibility and rdbustness of the neural network. (C) 2014 Elsevier GmbH. All rights reserved.