The noise as an undesired phenomenon often appears in the pulsed eddy current testing(PECT)signal, and it is difficult to recognize the character of the testing signal. One of the most common noises presented in the P...The noise as an undesired phenomenon often appears in the pulsed eddy current testing(PECT)signal, and it is difficult to recognize the character of the testing signal. One of the most common noises presented in the PECT signal is the Gaussian noise, since it is caused by the testing environment. A new denoising approach based on singular value decomposition(SVD) is proposed in this paper to reduce the Gaussian noise of PECT signal. The approach first discusses the relationship between signal to noise ratio(SNR) and negentropy of PECT signal. Then the Hankel matrix of PECT signal is constructed for noise reduction, and the matrix is divided into noise subspace and signal subspace by a singular valve threshold. Based on the theory of negentropy, the optimal matrix dimension and threshold are chosen to improve the performance of denoising. The denoised signal Hankel matrix is reconstructed by the singular values of signal subspace, and the denoised signal is finally extracted from this matrix. Experiment is performed to verify the feasibility of the proposed approach, and the results indicate that the proposed approach can reduce the Gaussian noise of PECT signal more effectively compared with other existing approaches.展开更多
In oil and gas extraction,ferromagnetic metal casings serve as critical infrastructure to ensure the safety of hydrocarbon transport.However,under high-temperature and high-pressure conditions,casings buried deep unde...In oil and gas extraction,ferromagnetic metal casings serve as critical infrastructure to ensure the safety of hydrocarbon transport.However,under high-temperature and high-pressure conditions,casings buried deep underground are prone to deformation,twisting,and even rupture due to erosion and corrosion,potentially leading to significant economic losses and safety hazards.Therefore,regular inspection and maintenance of in-service well casings are essential.Pulsed eddy current testing(PECT)has been widely used for casing defect detection owing to its efficiency,non-contact nature,and rich information content.However,the presence of substantial noise during detection degrades the quality of defect detection images.To address this issue,we investigated image processing techniques for casing defect detection images and proposed an image processing algorithm(BIC)based on bidimensional empirical mode decomposition(BEMD),improved wavelet threshold denoising(IWTD),and contrast limited adaptive histogram equalization(CLAHE).The proposed method first applied BEMD-IWTD for noise suppression in defect detection images,followed by CLAHE for image enhancement.To validate the effectiveness of the method,defect detection experiments were conducted on casings with ring-shaped and local defects,and the acquired images were processed.After being processed with the BIC algorithm,ring-shaped defects of different depths could be effectively distinguished,especially the 1 mm and 2 mm deep defects that were previously affected by noise.In the local defect images,small-sized defects difficult to be identified due to noise interference were successfully recognized,and the defect contrast C_(d)was significantly improved.The results demonstrate that the proposed BIC algorithm effectively suppresses the noise in defect detection images,enhances the contrast between defects and the background,and improves defect recognition and detection accuracy,providing reliable image processing support for subsequent defect analysis.展开更多
文摘The noise as an undesired phenomenon often appears in the pulsed eddy current testing(PECT)signal, and it is difficult to recognize the character of the testing signal. One of the most common noises presented in the PECT signal is the Gaussian noise, since it is caused by the testing environment. A new denoising approach based on singular value decomposition(SVD) is proposed in this paper to reduce the Gaussian noise of PECT signal. The approach first discusses the relationship between signal to noise ratio(SNR) and negentropy of PECT signal. Then the Hankel matrix of PECT signal is constructed for noise reduction, and the matrix is divided into noise subspace and signal subspace by a singular valve threshold. Based on the theory of negentropy, the optimal matrix dimension and threshold are chosen to improve the performance of denoising. The denoised signal Hankel matrix is reconstructed by the singular values of signal subspace, and the denoised signal is finally extracted from this matrix. Experiment is performed to verify the feasibility of the proposed approach, and the results indicate that the proposed approach can reduce the Gaussian noise of PECT signal more effectively compared with other existing approaches.
基金supported by National Natural Science Foundation of China(No.62303385)。
文摘In oil and gas extraction,ferromagnetic metal casings serve as critical infrastructure to ensure the safety of hydrocarbon transport.However,under high-temperature and high-pressure conditions,casings buried deep underground are prone to deformation,twisting,and even rupture due to erosion and corrosion,potentially leading to significant economic losses and safety hazards.Therefore,regular inspection and maintenance of in-service well casings are essential.Pulsed eddy current testing(PECT)has been widely used for casing defect detection owing to its efficiency,non-contact nature,and rich information content.However,the presence of substantial noise during detection degrades the quality of defect detection images.To address this issue,we investigated image processing techniques for casing defect detection images and proposed an image processing algorithm(BIC)based on bidimensional empirical mode decomposition(BEMD),improved wavelet threshold denoising(IWTD),and contrast limited adaptive histogram equalization(CLAHE).The proposed method first applied BEMD-IWTD for noise suppression in defect detection images,followed by CLAHE for image enhancement.To validate the effectiveness of the method,defect detection experiments were conducted on casings with ring-shaped and local defects,and the acquired images were processed.After being processed with the BIC algorithm,ring-shaped defects of different depths could be effectively distinguished,especially the 1 mm and 2 mm deep defects that were previously affected by noise.In the local defect images,small-sized defects difficult to be identified due to noise interference were successfully recognized,and the defect contrast C_(d)was significantly improved.The results demonstrate that the proposed BIC algorithm effectively suppresses the noise in defect detection images,enhances the contrast between defects and the background,and improves defect recognition and detection accuracy,providing reliable image processing support for subsequent defect analysis.
