公开(公告)号：US06519535B1

公开(公告)日：2003-02-11

申请号：US09587008

申请日：2000-06-05

申请人： Mark C. Petri ,  David S. Kupperman ,  James A. Morman ,  Jaques Reifman ,  Thomas Y. C. Wei

发明人： Mark C. Petri ,  David S. Kupperman ,  James A. Morman ,  Jaques Reifman ,  Thomas Y. C. Wei

IPC分类号： G01L100

CPC分类号： B21C51/00 ,  G01N27/9046

摘要： A signal processing technique which correlates eddy current inspection data from a tube having a critical tubing defect with a range of predicted burst pressures for the tube is provided. The method can directly correlate the raw eddy current inspection data representing the critical tubing defect with the range of burst pressures using a regression technique, preferably an artificial neural network. Alternatively, the technique deconvolves the raw eddy current inspection data into a set of undistorted signals, each of which represents a separate defect of the tube. The undistorted defect signal which represents the critical tubing defect is related to a range of burst pressures utilizing a regression technique.

摘要翻译： 提供了一种信号处理技术，其将具有关键管道缺陷的管的涡流检查数据与管的预测爆破压力的范围相关联。 该方法可以使用回归技术，优选人造神经网络，将表示关键管道缺陷的原始涡流检查数据与爆破压力的范围直接相关。 或者，该技术将原始涡流检查数据解卷积成一组未失真的信号，每个信号代表管的单独缺陷。 代表关键管道缺陷的未失真缺陷信号与采用回归技术的爆破压力范围有关。