Use of higher order statistics for enhancing magnetic flux leakage pipeline inspection data

Magnetic flux leakage (MFL) is one of the most commonly used techniques for the non-destructive evaluation of gas transmission pipelines. 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 the characteristic seamless pipe noise, lift-off variation of MFL sensor due to motion of the pipe and system noise due to on-board electronics, which can considerably reduce the detectability of defect signals. This paper presents a new technique to filter the correlated seamless pipe noise (SPN) and identify the defect regions in the MFL data, thereby reducing the data to be analyzed. The proposed filtering algorithm is based on higher order statistics (skewness and kurtosis), of the MFL data and is shown to be more robust than traditional filtering methods.