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|NDT.net Issue - 2016-03 - NEWS ||NDT.net Issue: 2016-03|
Publication: e-Journal of Nondestructive Testing (NDT) ISSN 1435-4934 (NDT.net Journal)
In-line service for internal inspection of unpiggable buried oil pipelines using long-range ultrasonic guided wavesTWI Ltd (The Welding Institute)193, Great Abington, Cambridge, United Kingdom
TWI is part of a consortium of organisations behind an EU-funded project that has created a new inspection system for buried oil pipelines. The PIGWaves project has developed an inspection tool for in-service non-destructive testing (NDT) of unpiggable pipelines, which also provides an alternative to existing methods of inspection for piggable pipes. The new system delivers drastically reduced data storage time, greater (robot) inspection speed and far quicker availability of inspection results after robot recovery.
Figure 1 PIGWaves collar prototype
Innovations and developments
The PIGWaves system performs total volume inspection far more rapidly and accurately than current methods of ultrasonic NDT inspection. Long-range ultrasonic testing (LRUT) is ideal for pipeline inspection as it only requires probe adjustments every 50 metres – the typical attainable propagation range of LRUT in pipelines.
Key features of the system:
neutrally buoyant robot performs a total volume inspection far more rapidly and cheaply enables inspection of pipelines with reduced diameters caused by obstacles or sharp bends probes deployed approximately every 40–60m, depending on the pipe configuration, reducing measurement times by several orders of magnitude much reduced data collection requirements for LRUT, compared with conventional UT, means that data storage from long pipes and data analysis is faster indication of different types of damage due to changes in received signal amplitude of the A‑scans compared to the time-baseline detects corrosion defects with thinning greater than 10% of wall thickness wireless in-pipe communication: robot communicates with base station at entry point to send NDT data and location.
Testing the system
To validate the PIGWaves system, a section of pipe was deliberately damaged. Initially, a crack was simulated with a slot increased in depth in seven equal increments until the entire wall thickness was penetrated (Figure 2, measurements 1–7). A secondary defect simulating wall thickness loss was introduced on the outside wall of the pipe in six steps (Figure 2, measurements 8–13).
The results presented on an A-scan (Figure 2a) an end‑of-pipe reflection, which reduced in amplitude in the presence of cracking and wall thinning, and as the width of the simulated corrosion was increased. In other regions of the filtered A‑scan, where anomalies are present (Figure 2a), there are several significant increases in signal amplitude due to the loss of wall thickness. The A-scan amplitude changes, following an increasing trend deviation from the defect-free signal.
Figure 2 LRUT results
Looking at the frequency domain of the signals with and without loss of wall thickness, there is a greater loss of energy over a wider range of frequencies around 30kHz compared to the results from the inserted cuts and thinning.
Changes on the A-scan’s amplitude reveal the presence of wall thickness loss. These different areas are evaluated by analysing the similarity of the signals at a given location.
Similarity is equal to one minus the error function of the rate of change of the A-scan’s amplitude:
Figure 2b shows the correlation between each of the signals acquired in comparison with the flawless pipe reference signal. The similarity is measured from one to zero, with one being equivalent to 100% equal signals. The error is calculated in one dimension; thus the similarity between two points on the real line is the absolute value of their numerical rate of change difference.
The Euclidean distances were analysed for one dimension, comparing the obtained signals on each measurement against the defect-free pipe signature. The distance between two points in one dimension is simply the absolute value of the difference between their coordinates. Mathematically, this is shown as follows:
Where p1 is the first coordinate of the first signal and p2 is the first coordinate of the second signal.
In Figure 2c, results show an increment on the distance between each signal measured and the reference signal when there is defect growth. The most important aspect considered is that the distance on amplitude change follows a trend with increasing deviation from the defect-free signal.
Figure 3 Internal collar for pigging inspection using LRUT
Guided waves allow rapid screening of long lengths of pipe to detect external or internal corrosion. Large cracks and corrosion are both detectable with guided wave technology. Depending on the position of the crack, when using only one guided wave mode, the feature can go unnoticed. Corrosion can be detectable from 10% of cross section loss only under certain conditions. The accuracy of detection is decreased by many factors such as distance, attenuation, scattering, absorption or leakage.
Figure 4 PIGWaves PIG
Research leading to these results received funding from the FP7/Capacities: Research for SMEs programme. It is part of the two-year project PIGWaves. For further information, please visit the project website (www.pigwaves.eu) or please contact us.
Ángela Angulo is a senior project leader at TWI specialising in condition monitoring. Ángela has an MEng degree in industrial engineering from the University of Navarra in Spain and an MSc in structural integrity from Brunel University.
Ángela joined TWI in January 2013, in the Integrity Management Group, which provides cost savings and reliability assurance through the development and application of advanced innovative inspection, assessment and risk management solutions for industry sectors including oil and gas. Within the condition monitoring section, Ángela works on the following areas of expertise: strategic health monitoring using vibration analysis, acoustic emission, modal analysis and guided ultrasonic waves.
Ángela has experience in forecasting for economic and societal impacts of project results, exploitation strategy and plan, and market route and opportunity identification.