Analysis Of Gas Pipeline Defect Identification And Diagnosis Technology Based On Magnetic Memory Testing

In recent years,large-caliber,high-pressure gas pipelines have been constructed in my country.If gas pipelines have defects in construction and operation that cannot be identified and dealt with in time,the safety of these pipelines will face very severe challenges.It even caused catastrophic accidents such as leakage and explosion.If the defects of the gas pipeline can be identified in time and accurately,relevant measures can be taken in time to solve the pipeline defects in the bud and ensure that no serious safety accidents occur in the gas pipeline.Based on the metal magnetic memory detection technology,this paper conducted an experimental study on the defect identification of an in-service gas pipeline in the west.The specific research content is as follows: First,the reasons for the frequent occurrence of gas pipeline accidents are summarized and analyzed.Secondly,the magnetic memory detection mechanism is analyzed and researched.Based on the principle analysis and mathematical deduction,the magnetic memory detection technology in the gas pipeline defect identification is proposed.Judgment criteria in application,and verified by experiments.Secondly,according to the characteristics of the magnetic memory detection signal and the related characteristics of the noise signal,the characteristic signal obtained by the experiment is reduced by Fourier transform and traditional wavelet analysis,and an improved method is proposed for its shortcomings and defects.Introduce the wavelet threshold analysis method of weight index and apply it to the magnetic memory detection technology.Finally,the classification of pipeline defects based on BP neural network and magnetic memory detection technology is carried out.This method is used to identify and detect pipeline defects,and the identification results are verified.The full text study shows:The reasons for the frequent occurrence of gas pipeline accidents are the difficulty in accurately detecting defects in buried pipelines,the lack of spare pipelines in pipeline operation,the difficulty of field maintenance,and the complex terrain that makes pipeline installation and construction difficult.Most of the existing magnetic memory detection technology applied to pipeline accident diagnosis can only be used for qualitative analysis,but cannot quantitatively determine the judgment criteria.From the perspective of energy analysis,this article starts with the geomagnetic field and external loads,combined with the existing research and derivation,and based on the molecular field theory,makes a quantitative study on the judgment criteria of magnetic memory testing in pipeline defect recognition and accident diagnosis.At the stress concentration of the pipeline,the positive and negative values of the normal component of the leakage magnetic field will jump and the zero-crossing quantification criterion is proposed.The experimental verification results show that the criterion can be used to judge pipeline defects,but noise reduction is required.Research has found that Fourier transform processing,soft and hard threshold function methods in wavelet analysis,and other signal noise reduction methods themselves have certain defects.The experimental results of these methods show that their processing effects cannot meet the requirements of noise reduction.Therefore,the weight index is introduced,and a new improved wavelet threshold denoising processing method for magnetic memory detection signals is proposed,and its processing effect is verified through experiments and theory.It is found that the root mean square error of the improved denoising method is only0.317,The signal-to-noise ratio reaches 27.312,and its processing effect is better than the traditional wavelet threshold noise reduction method.Finally,according to the nucleation and propagation mechanism of pipeline defects and cracks,the pipeline safety management state is divided into three states: defect-free state,stress concentration state,and crack state.Designed and trained a BP neural network suitable for the application of magnetic memory detection technology in gas pipeline defect recognition and accident diagnosis.The trained BP neural network was used to identify and diagnose pipeline defects and accidents,and the correct rate of pipeline defect recognition was found.Up to 86.7 %,which can effectively support the accident diagnosis of gas pipelines.