Characterization of the Uncertainties in the Inspection Results of Ultrasonic Intelligent Pigs

Salama, Mamdouh M.; Nestleroth, Bruce; Maes, Marc A.; Dash, Chris

doi:10.1115/omae2013-11168

Cited by 6 publications

(4 citation statements)

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“…Typical in-pipe robots are divided into wheel-driven robots, tractor/track-driven robots and fluid-powered robots, according to different driving mechanisms in conventional locomotion [5][6][7]. For example, the pipeline inspection gauge (PIG) [8] is a type of passive robot equipped with magnetic flux leakage (MFL) [9], ultrasonic testing (UT) [10], eddy currents (ECs) [11], and electromagnetic acoustic transducers (EMATs). However, the significant drawbacks are the significantly higher operating cost and indirect observation of defects.…”

Section: Introductionmentioning

confidence: 99%

Gas-Driven Endoscopic Robot for Visual Inspection of Corrosion Defects Inside Gas Pipelines

Jin¹,

Xiang

Ma³

et al. 2023

Processes

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The internal inspection of corrosion in large natural gas pipelines is a fundamental task for the prevention of possible failures. Photos and videos provide direct proof of internal corrosion defects. However, the implementation of this technique is limited by fast robot motion and poor lighting conditions, with high-quality images being key to its success. In this work, we developed a natural gas-driven pipeline endoscopic robot (GDPER) for the visual inspection of the inner wall surfaces of pipelines. GDPER consists of driving, odometer, and vision modules connected by universal joints. It is designed to work in a 154 mm gas-pressurized pipeline up to a maximum of 6 MPa, allowing it to smoothly pass through bends and cross-ring welds at a maximum speed of 3 m/s using gas pressure driving. Test results have shown that HD MP4 video files can be obtained, and the location of defects on the pipelines can be detected by intelligent video image post-processing. The gas-driven function enables the survey of very long pipelines without impacting the transport of the pipage.

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Section: Introductionmentioning

confidence: 99%

Gas-Driven Endoscopic Robot for Visual Inspection of Corrosion Defects Inside Gas Pipelines

Jin¹,

Xiang

Ma³

et al. 2023

Processes

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“…The advanced versions of these autonomous systems, also called “smart PIGs”, can move inside pipelines and measure irregularities that may represent corrosion, cracks, joints, deformation (e.g., dents, pipe ovality), laminations or other defects (e.g., weld defects) in the pipeline. The most common ILI methods that have been installed on smart PIGs and confirmed to be successful for pipeline inspection are magnetic flux leakages (MFL) [20], ultrasonic transducers (UT) [21], electromagnetic acoustic transducers (EMAT) [22] and eddy currents (EC) [23]. However, certain constraints seriously limit the practicality of the aforementioned methods.…”

Section: Introductionmentioning

confidence: 99%

A Real-Time, Non-Contact Method for In-Line Inspection of Oil and Gas Pipelines Using Optical Sensor Array

Sampath

Bhattacharya

Aryan

et al. 2019

Sensors

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Corrosion is considered as one of the most predominant causes of pipeline failures in the oil and gas industry and normally cannot be easily detected at the inner surface of pipelines without service disruption. The real-time inspection of oil and gas pipelines is extremely vital to mitigate accidents and maintenance cost as well as to improve the oil and gas transport efficiency. In this paper, a new, non-contact optical sensor array method for real-time inspection and non-destructive evaluation (NDE) of pipelines is presented. The proposed optical method consists of light emitting diodes (LEDs) and light dependent resistors (LDRs) to send light and receive reflected light from the inner surface of pipelines. The uniqueness of the proposed method lies in its accurate detection as well as its localization of corrosion defects, based on the utilization of optical sensor array in the pipeline, and also the flexibility with which this system can be adopted for pipelines with different services, sizes, and materials, as well as the method’s economic viability. Experimental studies are conducted considering corrosion defects with different features and dimensions to confirm the robustness and accuracy of the method. The obtained data are processed with discrete wavelet transform (DWT) for noise cancelation and feature extraction. The estimated sizes of the corrosion defects for different physical parameters, such as inspection speed and lift-off distance, are investigated and, finally, some preliminary tests are conducted based on the implementation of the proposed method on an in-line developed smart pipeline inspection gauge (PIG) for in-line inspection (ILI) application, with resulting success.

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“…Caleyo et al 15 developed a statistical calibration method based on parametric regression estimators to calibrate the measurements of an MFL pig tools using the ultrasonic ILI-reported data and corresponding field measurements. The calibration process was based on comparing the growth of the pipeline defects using the readings of different tools as done by Salama et al 16 Others used the corrosion data from different scans at different times as done by Din et al 14 Abdolrazaghi et al 17 proposed the calibration of MFL-IP with the data of UT scan tool using the linear regression model. The calibration was done based on the readings of three runs of both MFL-IP and UT device.…”

Section: Introductionmentioning

confidence: 99%

Error-reduction approach for corrosion measurements of pipeline inline inspection tools

Hamed

Shafie

Mustaffa

et al. 2018

Measurement and Control

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Inline inspection tools that are used to scan the interior defects of gas and oil pipelines tend to suffer from measuring error due to their sizing accuracy. This error often causes an over-or underestimation of the operating conditions of the pipeline, which might lead to a system failure. While parametric calibration models provide a simple method to reduce the measuring error, it is limited to datasets that follow the normal distribution only. Thus, in this paper, a nonparametric calibration model based on k-nearest neighbor interpolation was proposed to improve the measurements recorded by the scanning tools. Corrosion data collected using an ultrasonic scan device and the magnetic flux leakage intelligent pig are considered in the research. The k-nearest neighbor interpolation is studied based on the effect of using six kernel functions with two different positioning approaches on the interpolation behavior. The results have shown enhancement in the accuracy of the readings obtained from the intelligent pig from 620% of the pipeline wall thickness to only 68%. This enhancement in the sizing accuracy is meant to prevent a possible system failure for using the corroded part of the studied pipeline for an extra 4.6 years instead of replacing it.

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Characterization of the Uncertainties in the Inspection Results of Ultrasonic Intelligent Pigs

Cited by 6 publications

References 0 publications

Gas-Driven Endoscopic Robot for Visual Inspection of Corrosion Defects Inside Gas Pipelines

Gas-Driven Endoscopic Robot for Visual Inspection of Corrosion Defects Inside Gas Pipelines

A Real-Time, Non-Contact Method for In-Line Inspection of Oil and Gas Pipelines Using Optical Sensor Array

Error-reduction approach for corrosion measurements of pipeline inline inspection tools

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