A Novel Pipeline Defect Recognition Model Based On Magnetic Eddy Current Inspection Using For Environmental Protection Storage And Transportation Demands

Safety and environmental protection are the necessary conditions in the process of oil and gas pipeline gathering and transportation, which is of great significance to effectively improve the efficiency and reduce environmental pollution. Magnetic eddy current testing technology is often used to detect defects in metal materials. Unfavorable factors such as high lift-off degree and shielding of ferromagnetic materials will cause interference to the magnetic eddy current detection signal, causing misjudgment and omission of some defects. In order to improve the accuracy of magnetic eddy current signal defect diagnosis under complex noise interference, this paper proposes an improved wavelet packet threshold noise reduction (WD) and empirical mode decomposition (EMD) pipeline defect identification algorithm. First, the particle swarm optimization algorithm (PSO) is used to optimize the calculation method of the threshold in wavelet packet decomposition, and then the optimal wavelet base and the number of decomposition layers for wavelet packet threshold noise reduction are determined according to the characteristics of the magnetic eddy current detection signal. WPD noise reduction method; finally, a pipeline defect recognition model based on improved wavelet packet threshold noise reduction algorithm and EMD is formed, and the effectiveness of the method is proved by indoor pipeline test signals and field pipeline test signals. This study is of great significance to improve the efficiency of pipeline transportation and reduce the probability of environmental pollution.