一种基于MEMS imu的小直径地下管道气动定位球

IF 4.3 2区 综合性期刊 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Xiaoji Niu;Jundong Hu;Qijin Chen;Dong Zhao
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引用次数: 0

摘要

小直径地下管线(一般为20 ~ 65mm)的准确定位对于避免或减少城市建设过程中的破坏,提高维护效率至关重要。然而,传统的管道检测仪表(pig)主要是为正常尺寸的管道设计的,其配备的高级惯性测量单元(imu)由于其整体尺寸大,并且依赖于大尺寸的高精度imu,因此无法检测小直径或紧密弯曲的管道。由于目前还没有有效的方法来精确定位这些管道,因此这一限制在准确定位这些管道方面留下了严重的空白。为了解决这一挑战,我们提出了一种新的方法,将空气推进式惯性定位球(IPB)与芯片级微机电系统(MEMS) IMU集成在一起,从而使设备紧凑轻便,足以穿越小直径管道。然而,它带来了一个严重的问题,即MEMS IMU芯片具有较大的传感器误差,导致快速的位置漂移,因此只能保持几秒钟的定位精度。传统的清管器通常是通过钢缆以低速(约1m /s)通过管道,与之不同的是,轻质IPB的设计是由气流推动,使其能够快速(约10m /s)通过管道。这大大缩短了MEMS IMU的集成时间,从而有效地减轻了其累积误差。在一根长48m的小管径管道中进行的现场试验证明了IPB的有效性,单趟定位横向误差为0.77 m,高度方向误差为1.13 m。通过对4次独立下入的结果进行平均,最大定位误差在横向方向上降至0.36 m(占管长的0.75%),在高度方向上降至0.36 m(占管长的0.75%)。该方法为小直径紧密弯曲地下管道的定位提供了实用、高效、准确的解决方案,解决了地下管道测量中的一个关键空白。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A MEMS IMU-Based Air-Propelled Positioning Ball for Small-Diameter Underground Pipeline Localization
Accurate locating of small-diameter underground pipelines (typically 20–65 mm) is crucial for avoiding or minimizing the damage during urban construction and improving maintenance efficiency. However, conventional pipeline inspection Gauges (PIGs) equipped with high-grade inertial measurement units (IMUs), which are primarily designed for normal size pipeline, cannot access small diameter or tightly curved pipelines due to their large overall size and the large-sized high-precision IMUs they rely on. This limitation leaves a critical gap in accurately locating such pipelines, as no effective method currently exists for their precise positioning. To address this challenge, we propose a novel method using an air-propelled inertial positioning ball (IPB) integrated with a chip-level micro-electromechanical systems (MEMS) IMU, so as to make the device compact and lightweight enough to traverse the small-diameter pipelines. However, it comes with a serious problem that the MEMS IMU chip has large sensor errors leading to fast position drift and therefore can only keep positioning accuracy for several seconds. Unlike the conventional PIGs, which are typically pulled through pipelines by steel cables at a low speed (around 1 m/s), the lightweight IPB is designed to be propelled by airflow, allowing it to fly rapidly (around 10 m/s) through the pipeline. This significantly reduces the integration time of the MEMS IMU, thereby mitigating its cumulative errors effectively. Field tests conducted in a 48-m-long small-diameter pipe demonstrate the effectiveness of the IPB, with single-run positioning errors of 0.77 m in the transverse direction and 1.13 m in the height direction. By averaging the results from four independent runs, the maximum positioning errors were reduced to 0.36 m (0.75% of pipe length) in the transverse direction and 0.36 m (0.75% of pipe length) in the height direction. The proposed new approach provides a practical, efficient, and accurate solution for locating small-diameter and tightly curved underground pipelines, addressing a critical gap in underground pipeline surveying.
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来源期刊
IEEE Sensors Journal
IEEE Sensors Journal 工程技术-工程:电子与电气
CiteScore
7.70
自引率
14.00%
发文量
2058
审稿时长
5.2 months
期刊介绍: The fields of interest of the IEEE Sensors Journal are the theory, design , fabrication, manufacturing and applications of devices for sensing and transducing physical, chemical and biological phenomena, with emphasis on the electronics and physics aspect of sensors and integrated sensors-actuators. IEEE Sensors Journal deals with the following: -Sensor Phenomenology, Modelling, and Evaluation -Sensor Materials, Processing, and Fabrication -Chemical and Gas Sensors -Microfluidics and Biosensors -Optical Sensors -Physical Sensors: Temperature, Mechanical, Magnetic, and others -Acoustic and Ultrasonic Sensors -Sensor Packaging -Sensor Networks -Sensor Applications -Sensor Systems: Signals, Processing, and Interfaces -Actuators and Sensor Power Systems -Sensor Signal Processing for high precision and stability (amplification, filtering, linearization, modulation/demodulation) and under harsh conditions (EMC, radiation, humidity, temperature); energy consumption/harvesting -Sensor Data Processing (soft computing with sensor data, e.g., pattern recognition, machine learning, evolutionary computation; sensor data fusion, processing of wave e.g., electromagnetic and acoustic; and non-wave, e.g., chemical, gravity, particle, thermal, radiative and non-radiative sensor data, detection, estimation and classification based on sensor data) -Sensors in Industrial Practice
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