Advanced signal processing of magnetic flux leakage data obtained from seamless gas pipeline

Natural gas is normally transported through a vast network of pipelines. A major segment of this network employs seamless pipes. The manufacturing processes associated with the production of seamless pipes contribute to a helical variation in the grain properties of the pipe. This introduces an artifact, known as the seamless pipe noise (SPN), in the data obtained from magnetic flux leakage (MFL) inspection of these pipelines. SPN can overwhelm the signals generated by defects and other elements in pipelines, and can therefore, mask their indications in the MFL data. This paper presents a new technique for detecting signals in MFL data obtained from seamless pipes. The overall approach employs an adaptive filter and a wavelet based de-noising technique. The algorithm is computationally efficient and data independent. Results from application of the approach to data from field tests are presented. (C) 2002 Elsevier Science Ltd. All rights reserved.