Research Papers

Pipeline Inspection Gauge Position Estimation Using Inertial Measurement Unit, Odometer, and a Set of Reference Stations

[+] Author and Article Information
Md Sheruzzaman Chowdhury

Mechatronics Graduate Program,
American University of Sharjah,
P.O. Box 26666, Sharjah, United Arab Emirates
e-mail: sheruz@gmail.com

Mamoun F. Abdel-Hafez

American University of Sharjah,
P.O. Box 26666, Sharjah, United Arab Emirates
e-mail: mabdelhafez@aus.edu

Manuscript received January 2, 2015; final manuscript received June 1, 2015; published online January 4, 2016. Assoc. Editor: Athanasios Pantelous.

ASME J. Risk Uncertainty Part B 2(2), 021001 (Jan 04, 2016) (10 pages) Paper No: RISK-15-1002; doi: 10.1115/1.4030945 History: Received January 02, 2015; Accepted June 30, 2015

This paper presents a low-cost methodology to estimate the position of a pipeline inspection gauge (PIG). The environment in which the PIG navigates is inside the thick walls of a metallic pipeline, where it is not possible to receive a global positioning system (GPS) signal. As a consequence, it is necessary to use other means of navigation. A technique is presented in the paper that uses an inertial measurement unit (IMU), a speedometer, and a set of reference stations. A Kalman filter is used to fuse the measurements from the IMU, the speedometer, and the reference stations. The reference stations, with known GPS coordinates, are installed for every set interval to correct the PIG’s state estimate from the errors that accumulate due to the integration of the IMU measurements. The paper presents three scenarios. These scenarios differ in the way the update step of the Kalman filter is performed. Experimental results are presented along with a 100-run Monte Carlo test to verify the estimator’s consistency.

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Path traveled in experiment

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Fig. 3

Odometer simulation from experimental data

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Fig. 4

Speedometer simulation from experimental data

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Fig. 5

Estimation using speedometer only

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Fig. 6

Position and velocity standard deviation

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Fig. 7

100-run Monte Carlo position errors

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Fig. 8

Estimation using speedometer and reference stations that are 500 m spaced

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Fig. 9

Position and velocity standard deviation

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Fig. 10

100-run Monte Carlo position errors

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Fig. 11

Estimation using speedometer and reference stations every 100 m

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Fig. 12

Position and velocity standard deviations

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Fig. 13

100-run Monte Carlo simulation

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Fig. 14

Position estimate using speedometer and update points every 10 m

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Fig. 15

Position and velocity standard deviations

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Fig. 16

Monte Carlo simulation for 100 runs




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