Robotic pipeline wall thickness evaluation for dense nondestructive testing inspection. Issue 8 (13th November 2018)

• Record Type: Journal Article
• Authors: Valls Miro, Jaime; Ulapane, Nalika; Shi, Lei; Hunt, Dave; Behrens, Michael
• Other Names: Hutter Marco guestEditor.; Siegwart Roland guestEditor.
• Is Part Of: Journal of field robotics. Volume 35:Issue 8(2018)
• Page Start: 1293
• Page End: 1310
• Publication Date: 2018-11-13
• Subjects: gaussian process -- inspection harsh environments -- mapping -- NDT -- pipeline robot; Robots, Industrial -- Periodicals; Automatic control -- Periodicals; 629.892
• DOI: 10.1002/rob.21828
• Languages: English
• ISSNs: 1556-4959

Abstract

Abstract: This paper addresses automated mapping of the remaining wall thickness of metallic pipelines in the field by means of an inspection robot equipped with nondestructive testing (NDT) sensing. Set in the context of condition assessment of critical infrastructure, the integrity of arbitrary sections in the conduit is derived with a bespoke robot kinematic configuration that allows dense pipe wall thickness discrimination in circumferential and longitudinal direction via NDT sensing with guaranteed sensing lift‐off (offset of the sensor from pipe wall) to the pipe wall, an essential barrier to overcome in cement‐lined water pipelines. A tailored covariance function for pipeline cylindrical structures within the context of a Gaussian Processes has also been developed to regress missing sensor data incurred by a sampling strategy folllowed in the field to speed up the inspection times, given the slow response of the pulsed eddy current electromagnetic sensor proposed. The data gathered represent not only a visual understanding of the condition of the pipe for asset managers, but also constitute a quantative input to a remaining‐life calculation that defines the likelihood of the pipeline for future renewal or repair. Results are presented from deployment of the robotic device on a series of pipeline inspections which demonstrate the feasibility of the device and sensing configuration to provide meaningful 2.5D geometric maps.