Extreme value analysis (EVA) is occasionally used to predict corrosion progress. This paper adopts EVA to predict the depth of extreme pits to prioritize inspection and maintenance. It considers the peaks over threshold (POT) method to illustrate the predictive capacity of this method in assessing degradation progress based on consecutive inspection reports. The proposed approach uses distribution parameters to establish stochastic corrosion models. Four consecutive inline inspections of a pipeline are used to validate the model. As the block maxima (BM) method is often used in extreme value analysis of corrosion damage depths, the POT approach is compared to the BM's predictive results. The POT approach is considerably more capable (33%) of assessing failures in individual sections than the same workflow implemented with BM. With the downside of increased falsely categorized failures (10.6%). The method's performance in assessing failures makes it most useful for data-driven maintenance of process systems.

极值分析 (EVA) 偶尔用于预测腐蚀进程。本文采用EVA预测极坑深度，优先检查和维护。它考虑了阈值峰值 (POT) 方法来说明该方法在基于连续检查报告评估退化进程方面的预测能力。所提出的方法使用分布参数来建立随机腐蚀模型。管道的四次连续在线检查用于验证模型。由于块最大值 (BM) 方法经常用于腐蚀损伤深度的极值分析，因此将 POT 方法与 BM 的预测结果进行比较。与使用 BM 实施的相同工作流程相比，POT 方法在评估各个部分的故障方面的能力要强得多 (33%)。随着错误分类失败的增加 (10.6%) 的不利影响。该方法在评估故障方面的性能使其对于过程系统的数据驱动维护最有用。