Adaptive noise cancellation schemes for magnetic flux leakage signals obtained from gas pipeline inspection

Nondestructive evaluation of the gas pipeline system is most commonly performed using magnetic flux leakage (MFL) techniques. A major segment of this network employs seamless pipes. The data obtained From MFL inspection of seamless pipes is contaminated by various sources of noise, including seamless pipe noise due to material properties of the pipe, lift-off variation of MFL sensor due to motion of the pipe and system noise due to on-board electronics. The noise can considerably reduce the detectability of defect signals in MFL data. This paper presents a new technique for improving the signal-to-noise-ratio in MFL data obtained from seamless pipes. The approach utilizes normalized least mean squares adaptive noise filtering coupled with wavelet shrinkage denoising to minimize the effects of various sources of noise. Results from application of the approach to data from field tests are presented. It is shown that the proposed algorithm is computationally efficient and data-independent