Research Progress on an Intelligent X-ray Backscattering Nondestructive Testing Robot for the Internal Inspection of Petroleum Pipelines

Research Progress on an Intelligent X-ray Backscattering Nondestructive Testing Robot for the Internal Inspection of Petroleum Pipelines

Backscatter

Corrosion

Detectors

Imaging

Inspection

Pipelines

X-ray imaging

The X-ray transmission imaging technology is often used for nondestructive testing outside the pipe, but for deep buried petroleum pipelines, its equipment cannot meet environmental layout requirements and one side accessible detection devices are needed. There are problems exists in traditional one side accessible internal pipeline inspection methods. The commonly used ultrasonic internal pipe inspection method can detect lamination corrosion and wall thickness very well, but it has poor detection effect on pitting corrosion, and requires liquid medium and cleaning device. The magnetic flux leakage detection method can detect pitting corrosion and deformation, but it is poor at resolving the sizing corrosion clusters. In view of the problems existing in the non-destructive inspection technologies of deep buried pipelines, this paper proposes the concept of an intelligent X-ray backscattering nondestructive in-line inspection robot by using the advantages of the high penetrating ability of X-rays, the single-sided arrangement of backscattering imaging technology and the modern intelligent robot technology. The Monte Carlo software (Geant4) has been used to model and design the core of the proposed X-ray backscatter imaging system, and further used to simulate the detection performance of defects. The simulation results show that the X-ray backscattering detection technology can detect the variation of the wall thickness defects of ≥4 mm and the cracks of ≥1 mm width in the pipe. The detection system could be further improved in resolution.

1-3

H. Li

Y. Wang

X. Zhang

2019 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC)

2019

conferencePaper

2577-0829