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基于多传感器融合与长短期记忆网络的里程表打滑期间在线检测管道中心线测量补偿方法

Compensation Method for Pipeline Centerline Measurement of in-Line Inspection during Odometer Slips Based on Multi-Sensor Fusion and LSTM Network.

作者信息

Liu Shucong, Zheng Dezhi, Li Rui

机构信息

School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China.

Institute of Disaster Prevention, Sanhe 065201, China.

出版信息

Sensors (Basel). 2019 Aug 29;19(17):3740. doi: 10.3390/s19173740.

Abstract

The accurate measurement of pipeline centerline coordinates is of great significance to the management of oil and gas pipelines and energy transportation security. The main method for pipeline centerline measurement is in-line inspection technology based on multi-sensor data fusion, which combines the inertial measurement unit (IMU), above-ground marker, and odometer. However, the observation of velocity is not accurate because the odometer often slips in the actual inspection, which greatly affects the accuracy of centerline measurement. In this paper, we propose a new compensation method for oil and gas pipeline centerline measurement based on a long short-term memory (LSTM) network during the occurrence of odometer slip. The field test results indicated that the mean of absolute position errors reduced from 8.75 to 2.02 m. The proposed method could effectively reduce the errors and improve the accuracy of pipeline centerline measurement during odometer slips.

摘要

管道中心线坐标的精确测量对于油气管道的管理和能源运输安全具有重要意义。管道中心线测量的主要方法是基于多传感器数据融合的在线检测技术,该技术结合了惯性测量单元(IMU)、地面标记和里程计。然而,由于里程计在实际检测中经常打滑,导致速度观测不准确,这极大地影响了中心线测量的精度。在本文中,我们提出了一种基于长短期记忆(LSTM)网络的油气管道中心线测量新补偿方法,用于解决里程计打滑时的问题。现场测试结果表明,绝对位置误差的平均值从8.75米降至2.02米。所提出的方法能够有效减少误差,提高里程计打滑时管道中心线测量的精度。

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