去除SPN的小波域自适应FIR滤波修正算法

Application of modified WTD adaptive FIR filtering algorithm to cancelling SPN

漏磁检测(MFL)是油气管道在线检测中应用非常成熟的一种无损检测技术.漏磁检测数据通常被无缝管道噪声所污染,而无缝管道噪声在某些情况下可能完全掩没来自某一类型缺陷的漏磁信号,因而极大地降低了管道缺陷的可检测性.本文对小波变换域自适应有限冲激响应(FIR)滤波算法进行修正,提出一种可有效去除漏磁数据中无缝管道噪声(SPN)的修正算法.该算法利用无缝管道噪声和漏磁信号的不同相关特性,可有效去除漏磁数据中相关度高的漏磁信号,从而提高漏磁数据中相关度较小的缺陷信号的可检测性.该修正算法用于实测漏磁数据,所得结果说明该算法具有良好的去噪效果,从而提高了漏磁数据中缺陷信号的可检测性.

The magnetic flux leakage （MFL） method has been widely used in-line inspection technique for the evaluation of gas and oil pipelines. The MFL data obtained from seamless pipeline inspection is usually contaminated by the seamless pipe noise （SPN）. SPN can in some cases completely mask MFL signals from certain type of defects, and therefore considerably reduces the detectability of the defect signals. This paper presents a modified wavelet transform domain adaptive FIR filtering algorithm for removing the SPN in the MFL data. The proposed algorithm can effectively cancel the SPN in the MFL data with high correlation and therefore improves the detecability of the defect signals with relatively low correlation by employing the different correlation properties of SPN and defect signals. Results from application of the modified algorithm to the MFL data from field tests show that the modified algorithm has good performance and improves the detectability of the defect signals in the MFL data.