用截断数据集估计测量性能

Jason B. Skow, J. Krynicki, A. Fraser, Gustavo Gonzalez
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摘要

2021年9月,API发布了1163标准第三版“在线检验系统确认”。与以前的版本相比,这个版本带来了许多改进,包括第8节“系统结果验证”中的更多细节,其中定义了用于验证ILI运行公差的方法。该标准描述了三个级别的验证,“第3级”要求操作人员使用在验证轴和挖掘现场测量的真实数据计算ILI工具的测量性能。在现实世界中,检测数据集具有一些特征,使它们难以用于准确估计测量性能,其中之一是“截断”,即具有下限或上限阈值的数据,超过该阈值没有数据报告。例如,大多数UTCD ILI工具具有较低的截断水平,例如裂缝高度为1毫米,这表示信号阈值,低于该阈值,测量结果要么不可靠,要么不报告。尽管管道中存在低于报告阈值的小特性,但是ILI工具通常不会报告它们。本文描述了当数据集具有较低截断阈值时,使用API 1163 Level 3方法估计ILI工具性能的模型。用仿真数据对该模型进行了测试,以显示它如何响应大范围的特征种群特征,然后将其应用于两个真实的现场数据集。将截断算法与该算法的标准非截断版本进行比较,以显示新算法在哪些方面性能最佳,以及在实现管道完整性缓解方面最有用。本研究中使用的模型与API 1163 -附录C中记录的示例一致,即贝叶斯推理方法。模型的结果产生测量性能规格,可作为管道风险或可靠性分析的输入。截断数据集的影响在检验和无损检测领域(包括厚度测量)很常见,因为它反映了存在低于报告阈值的特征的现实。本文描述了将结果格式化以供使用,并获得更准确的测量性能结果(例如,单位图)所需的步骤。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Estimating Measurement Performance With Truncated Data Sets
In September 2021, the API released the third edition of the 1163 Standard “In-line Inspection Systems Qualification”. This edition brought many improvements over previous versions, including more detail in Section 8 “System Results Validation”, which defines the methodologies used to validate ILI run tolerances. The standard describes three levels of validation, with ‘Level 3’ requiring the operator calculate ILI tool measurement performance with real-world data measured in validation spools and excavation sites. Real-world, inspection data sets have some characteristics that make them difficult to use to accurately estimate measurement performance, one of which is ‘truncation’, that is data with a lower- or upper-bound threshold above which no data is reported. For example, most UTCD ILI tools have a lower truncation level, such as 1 mm for crack height, which represents a signal threshold below which measurements are either not reliable, or not reported. Although small features below the reporting threshold exist on the pipeline, they are not normally reported by the ILI tool. This paper describes a model to estimate ILI tool performance using API 1163 Level 3 methods when the data set has a lower-truncation threshold. The model is tested with simulation data to show how it responds over a wide range of feature population characteristics, and then applied to two real field data sets. Comparisons are made between the truncation algorithm and the standard non-truncated version of the algorithm, to show where the new algorithm performs best and is most useful to implement pipeline integrity mitigations. The model used in this study is consistent with the example documented in API 1163 - Appendix C, the Bayesian inference method. The results of the model produce measurement performance specifications that can be used as inputs in a pipeline risk or reliability analysis. The influence of truncated data sets is common in the field of inspection and NDE (including thickness measurements), as it reflects the reality that there are features below reporting threshold. The steps required to format the results for use, and achieve more accurate measurement performance results (e.g., unity charts), are described in this paper.
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