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A modified wavelet transform domain adaptive FIR filtering algorithm for removing the SPN contained in the MFL data

The magnetic flux leakage (MFL) method has established itself as the most 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 detectability 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 considerably improves the detectability of the defect signals in the MFL data