The integrity assessment of dents in pipelines is primarily driven by the dent depths as per the stipulations in current codes and standards. There is a provision for strain-based analysis to quantify the severity of dents based on their shapes in the ASME B31.8 non-mandatory Appendix R. In recent years, the pipeline industry has also started leveraging more advanced techniques such as Finite Element Analysis (FEA) for dent assessment. These assessments require the detailed deformation profile of dents, which are available from In-line Inspection (ILI) tools.

The ILI tools use caliper arms that roll along the inside of the pipeline and scan the inner profile. The measurements recorded by each caliper arm are susceptible to noise due to the vibration of the ILI tool, and as a result, the dent shapes obtained from ILI are not smooth. Strain assessments of dents typically require the calculation of radius of curvature in the longitudinal and circumferential directions. This becomes a complex problem while the ILI data contains noise, particularly for relatively shallow dents, when the dent depth approaches the magnitude of the noise in the data. In these cases, the radius of curvature estimation can become highly inaccurate. Furthermore, the amount of noise in the data can vary between dents, and so the accuracy of the estimation varies as well.

This paper presents several methods to resolve the above-mentioned issues. To address the issue of data noise itself, a combination of Fast Fourier Transform (FFT) and Gaussian filtering is used to produce a smooth profile that can be used to calculate the maximum radius of curvature of the dent. The smoothed profile also results in a better estimation of dent depth. To estimate the amount of uncertainty in the data, we apply many independent iterations of random noise to the smoothed curve. Characteristics required for further reliability analysis, such as dent depth or radius of curvature, are calculated for each iteration. This forms a distribution for each characteristic, and the properties of each distribution are used to quantify the uncertainty in the ILI data.

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