Wavelet Transform Domain Adaptive De-noising Algorithm for Removing the Seamless Pipe Noise in the MFL Data

摘要

In the pipe inspection technologies, 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 various sources of noise, which considerably reduces the detectability of defect signals in the MFL data. In this paper, a new de-noising algorithm called wavelet transform domain adaptive filtering is proposed for removing the seamless pipe noise (SPN) contained in the MFL data. The new algorithm is resulted from combining the wavelet transform with the adaptive filtering technique. Results from application of the proposed algorithm to the MFL data from field tests show that the algorithm has good performance and considerably improves the detectability of the defect signals in the MFL data.