STATISTICAL CALIBRATION OF PIPELINE IN-LINE INSPECTION DATA
This paper describes a statistical methodology for the calibration of in-line inspection (ILI) tools which is based on the comparison of the ILI readings with field-measurements results. The systematic and random errors that affect the ILI and the field tools are estimated and from this information, an unbiased estimation of the true depth of the defects detected by the ILI tool is produced. The influence of the number of field verifications on the reliability of the calibration process is addressed. The methodology is tested through Monte Carlo simulations and illustrated using a real-life ILI dataset produced by an UT tool. Introduction: Today in-line inspection (ILI) tools used to detect, locate and size pipeline anomalies such as dents, cracks and corrosion metal loss are based on the magnetic flux leakage (MFL) and ultrasonic (UT) principles [1]. The information provided by ILI tools consists of geometrical data describing each detected anomaly, e.g. its length, depth, width and orientation. This information is affected by built-in measurement errors, both systematic and random, that have to be considered when the ILI data is used to conduct integrity studies and fitness-forpurpose investigations. Pipeline integrity analysts and ILI vendors are now aware of the fact that in assessing the severity of a defect, the key issue is how accurately its geometry has been measured. In this sense, the two important parameters that ILI vendors provide are the probability of detection (POD) and the sizing accuracy of the tool. The sizing accuracy associated with an ILI tool is quoted as an accuracy level and a percent confidence. The depth sizing accuracy associated with corrosion metal loss of today high resolution (HR) MFL tools is typically claimed to be ±10% of the pipe wall thickness (wt) with a confidence level of 80%. In the case of extra high resolution (XHR) MFL tools, the sizing accuracy is ±5% wt at 80% confidence. On the other hand, today HR and XHR UT tools are claimed to have a sizing accuracy of ±0.6 mm and ±0.3 mm at a confidence level of 80%, respectively [1]. Pipeline operators assess the accuracy of a pipeline inspection through the statistical comparison of the metal loss sizes predicted by the ILI tool with the sizes obtained through field inspections. (a) 0 10 20 30 40 50 60 70 80 90 100 0 10 20 30 40 50 60 70 80 90 100
Approaches to Dealing With Missing Data in Railway Asset Management
The collection of reliable and high-quality data is seen as a prerequisite for effective and efficient rail infrastructure and rolling stock asset management to meet the requirements of asset owners and service providers. In this paper, the importance of recovering missing information in railway asset management is highlighted, and the advanced models and algorithms that have been applied to recovering the missed data are analyzed and discussed. Through making comparisons among these models and algorithms, a procedure is proposed to guide selecting the appropriate models based on different data missing scenarios. Using the newly developed framework with one dataset from each scenario, new models with different structures are trained and finally, the most suitable model is selected and utilized to recover the missing data and the selected model’s performance is evaluated using the data with known or clearly identified missing data mechanisms. Challenges via application of advanced algorithms for recovering missing data are discussed.
Early Identification of Children at Risk
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