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Inline inspection tools that are used to scan the interior defects of gas and oil pipelines tend to suffer from measuring error due to their sizing accuracy. This error often causes an over- or under-estimation of the operating conditions of the pipeline, which might lead to a system failure. While parametric calibration models provide a simple method to reduce the measuring error, it is limited to datasets that follow the normal distribution only. Thus, in this paper, a non-parametric calibration model based on k-nearest neighbor interpolation was proposed to improve the measurements recorded by the scanning tools. Corrosion data collected using an ultrasonic scan device and the magnetic flux leakage intelligent pig are considered in the research. The k-nearest neighbor interpolation is studied based on the effect of using six kernel functions with two different positioning approaches on the interpolation behavior. The results have shown enhancement in the accuracy of the readings obtained from the intelligent pig from ±20% of the pipeline wall thickness to only ±8%. This enhancement in the sizing accuracy is meant to prevent a possible system failure for using the corroded part of the studied pipeline for an extra 4.6 years instead of replacing it.

Error-reduction approach for corrosion measurements of pipeline inline inspection tools