With the widespread application and fast development of gas and oil pipeline network in China, the pipeline inspection technology has been used more extensively. The magnetic flux leakage （MFL） method has establishe...With the widespread application and fast development of gas and oil pipeline network in China, the pipeline inspection technology has been used more extensively. 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 the seamless pipe noise （SPN）. SPN can in some cases completely mask MFL signals from certain type of defects, and therefore considerably reduces the detectability of the defect signals. In this paper, a new de-noising algorithm called wavelet domain adaptive filtering is proposed for removing the SPN contained in the MFL data. The new algorithm results 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 proposed algorithm has good performance and considerably improves the detectability of the defect signals in the MFL data.展开更多
This paper considers the problem of noise cancellation for the magnetic flux leakage (MFL) data obtained from the inspection of oil pipelines. MFL data is contaminated by various sources of noise, and the noise can co...This paper considers the problem of noise cancellation for the magnetic flux leakage (MFL) data obtained from the inspection of oil pipelines. MFL data is contaminated by various sources of noise, and the noise can considerably reduce the detectability of flaw signals in MFL data. This paper presents a new de-noising approach for removing the system noise contained in the MFL data by using the coefficients de-noising with wavelet transform. Experimental results are presented to demonstrate the advantages of this de-noising approach over the conventional wavelet de-noising method.展开更多
文摘With the widespread application and fast development of gas and oil pipeline network in China, the pipeline inspection technology has been used more extensively. 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 the seamless pipe noise （SPN）. SPN can in some cases completely mask MFL signals from certain type of defects, and therefore considerably reduces the detectability of the defect signals. In this paper, a new de-noising algorithm called wavelet domain adaptive filtering is proposed for removing the SPN contained in the MFL data. The new algorithm results 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 proposed algorithm has good performance and considerably improves the detectability of the defect signals in the MFL data.
文摘This paper considers the problem of noise cancellation for the magnetic flux leakage (MFL) data obtained from the inspection of oil pipelines. MFL data is contaminated by various sources of noise, and the noise can considerably reduce the detectability of flaw signals in MFL data. This paper presents a new de-noising approach for removing the system noise contained in the MFL data by using the coefficients de-noising with wavelet transform. Experimental results are presented to demonstrate the advantages of this de-noising approach over the conventional wavelet de-noising method.
