The magnetic flux leakage (MFL) nondestructive evaluation technique is used extensively for in-service inspection of gas and oil pipelines. Unfortunately, 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 MFL data. In this paper, a new denoising algorithm is presented for removing seamless pipe noise (SPN) and system noise contained in MFL data. The algorithm first utilizes the new wavelet domain adaptive filtering method proposed by combining wavelet transform with the adaptive filtering technique to remove SPN contained in MFL data and then exploits the coefficient denoising approach with wavelet transform to cancel the system noise in the output of the wavelet domain adaptive SPN cancellation system. Theoretical analysis shows that the proposed denoising algorithm has a better overall performance than the existing denoising algorithm. Results of application of the proposed algorithm to MFL data from field tests are presented to demonstrate the performance of the proposed algorithm compared with the existing denoising algorithm.
A new denoising algorithm for MFL data obtained from seamless pipeline inspection
Ein neuer Rauschminderungsalgorithmus für magnetische Streuflussdaten bei der Prüfung nahtloser Pipelines
Russian Journal of Nondestructive Testing ; 44 , 3 ; 184-195
2008
12 Seiten, 8 Bilder, 9 Quellen
Article (Journal)
English
British Gas has seamless pipe inspection program
Tema Archive | 1985
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