Abstract
Automatic crack identification for pipeline analysis utilizes three-dimensional (3D) image technology to improve the accuracy and reliability of crack identification. A new technique that integrates a deep learning algorithm and 3D shadow modeling (3D-SM) is proposed for the automatic identification of corrosion cracks in pipelines. Since the depth of a corrosion crack is below the surrounding area of the crack, a shadow of the crack is projected when the crack is exposed under light sources. In this study, we analyze the shadow areas of cracks through 3D shadow modeling (3D-SM) and identify the evolving cracks through the shape analysis of the shadows. To denoise the 3D images, the connected domain analysis is implemented so that the shadow groups of the evolving cracks can be retained and the scattered shadow groups that occur due to insignificant defects can be eliminated. Moreover, a novel deep neural network is developed to process the 3D images. The proposed automatic crack identification method successfully processes the 3D images efficiently and accurately diagnoses the corrosion cracks. Experimental results show that the proposed method achieves satisfactory performance with 93.53% accuracy and a 92.04% regression rate.
Highlights
Steel pipelines play an important role in gas and liquid transportation over long distances
The proposed method integrates the 3. Three-Dimensional (3D) shadow modeling (3D-SM) with convolutional neural network (CNN) for automatic crack identification in pipelines
We proposed an automatic crack identification method to detect corrosion cracks of pipelines using 3D images
Summary
Steel pipelines play an important role in gas and liquid transportation over long distances. Corrosion crack identification highly relies on manual detection and subjective decision. Nondestructive evaluation (NDE) approaches have been proposed for corrosion crack identification in steel pipelines. NDE methods detect corrosion cracks of steel pipelines through different mediums, such as X-ray, gamma-ray radiography, ultrasonic, thermography, eddy current [2,3], fiber optic distributed [4,5] and electrical capacitance sensors [6,7,8,9,10,11,12]. Due to the different needs for heat or wave sources and complicated data analyzers of different NDE methods, such applications can encounter various difficulties in terms of operational and monitoring requirements. It may not be possible to achieve thorough monitoring of the wide fields [13]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
