Application of robotics in offshore oil and gas industry- A review Part II

Inspection of ship hulls and marine structures using autonomous underwater vehicles has emerged as a unique and challenging application of robotics. The problem poses rich questions in physical design and operation, perception and navigation, and planning, driven by difficulties arising from the acoustic environment, poor water quality and the highly complex structures to be inspected. In this paper, we develop and apply algorithms for the central navigation and planning problems on ship hulls. These divide into two classes, suitable for the open, forward parts of a typical monohull, and for the complex areas around the shafting, propellers and rudders. On the open hull, we have integrated acoustic and visual mapping processes to achieve closed-loop control relative to features such as weld-lines and biofouling. In the complex area, we implemented new large-scale planning routines so as to achieve full imaging coverage of all the structures, at a high resolution. We demonstrate our approaches in recent op...

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Advanced perception, navigation and planning for autonomous in-water ship hull inspection

This paper reports a real-time monocular visual simultaneous localization and mapping (SLAM) algorithm and results for its application in the area of autonomous underwater ship hull inspection. The proposed algorithm overcomes some of the specific challenges associated with underwater visual SLAM, namely, limited field of view imagery and feature-poor regions. It does so by exploiting our SLAM navigation prior within the image registration pipeline and by being selective about which imagery is considered informative in terms of our visual SLAM map. A novel online bag-of-words measure for intra and interimage saliency are introduced and are shown to be useful for image key-frame selection, information-gain-based link hypothesis, and novelty detection. Results from three real-world hull inspection experiments evaluate the overall approach, including one survey comprising a 3.4-h/2.7-km-long trajectory.

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Real-Time Visual SLAM for Autonomous Underwater Hull Inspection Using Visual Saliency

Artificial neural network models for predicting condition of offshore oil and gas pipelines

Condition assessment of the buried utility service infrastructure

This work evaluates the use of artificial neural networks (ANNs) for pattern recognition of magnetic flux leakage (MFL) signals in weld joints of pipelines obtained by intelligent pig. Initially the ANNs were used to distinguish the pattern signals with non-defect (ND) and signals with defects (D) along of the weld bead. In the next step the ANNs were applied to classify signal patterns with three types of defects in the weld joint: external corrosion (EC), internal corrosion (IC) and lack of penetration (LP). The defects were intentionally inserted in the weld bead of a pipeline of API 5L-X65 steel with an outer diameter of 304.8 mm. In this way, the MFL signal itself, digitized with 1025 points, was used as the ANN input. Initially the signals were used as inputs for the neural network without any type of pre-processing, later four types of pre-processing were applied to the signals: Fourier analysis, Moving-average filter, Wavelet analysis and Savitzky–Golay filter. Signal processing techniques were employed to improve the performance of the neural networks in distinguishing between the defect classes. The results showed that it is possible to classify signals of classes D and ND using ANN with very efficient results (94.2%), as well as for corrosion (CO) and LP signals (92.5%). Also it is possible to classify the defect pattern signals: EC, IC and LP using neural networks with an average rate of success of 71.7% for the validation set.

MFL signals and artificial neural networks applied to detection and classification of pipe weld defects

On the reliability of an automated ultrasonic system for hull inspection in ship-based oil production units

The objective of this work is to evaluate the reliability of the ultrasonic non-destructive testing (NDT) technique, for specific test conditions, using PoD (probability of detection) curves developed by experimental procedures. Two classes of defects, lack of penetration (LP) and lack of fusion (LF), were intentionally inserted in 24 girth weld beads of an API X70 steel pipeline with an outer diameter of 254 mm and wall thickness of 19.05 mm. These welds were inspected using manual and automatic ultrasonic techniques. The automatic ultrasonic inspection was carried out using pulse-echo and TOFD (Time-of-Flight Diffraction) techniques and manual tests were carried out by five qualified inspectors using pulse-echo technique. The bootstrap technique was used in order to obtain a confidence interval for the PoD curves. The results, besides producing real PoD curves, showed the superiority of the automatic techniques over the manual test in the probability of detection of these two classes of defects.

Reliability of the manual and automatic ultrasonic technique in the detection of pipe weld defects

Reliability of the ultrasonic inspection by numerical simulation technique

The main objective of this paper is to extend the authors' last work (1) , which described an automated ultrasonic system developed for the non-destructive underwater inspection of defects caused by corrosion on hulls of floating units employed in the offshore oil industry. The main advantages of this system are that it can be carried out without production stoppages and the oil storage tanks do not need to be emptied during the hull inspection process. The inspection system is made up of a mobile vehicle with magnetic wheels that secure it to the ship hull and an ultrasound pulse-echo apparatus, which provides a non-destructive technique in an immersed state. The vehicle is remotely controlled by means of an umbilical cable. The ultrasound inspection results are shown in A, B, C and D-scan images. In this paper the first results obtained by this system are described. The laboratory and field results showed that this system is effective for this purpose.

A. A. Carvalho

Papers

MFL signals and artificial neural networks applied to detection and classification of pipe weld defects

On the reliability of an automated ultrasonic system for hull inspection in ship-based oil production units

Reliability of the manual and automatic ultrasonic technique in the detection of pipe weld defects

Reliability of the ultrasonic inspection by numerical simulation technique

Inspection of ship hulls using automated ultrasonic inspection : NDT in oil & gas industries