Research On CBM Pipeline Defect Recognition System Based On Weakening Magnetic

As a ferromagnetic device for transporting gases,liquids or fluids with solid particles,pipes are widely used in industrial piping systems dominated by chemical transportation systems.Among them,due to the laying of coalbed methane pipelines in a humid soil environment and long-term use,problems such as corrosion and perforation of pipelines are inevitable,and the degree of aging of the pipelines of different pipelines is not the same.Coal seams may occur in places where pipelines are seriously damaged.Air leakage,loss of gas volume,and increased output not only cause waste of resources,but also serious accidents in the transportation system,which may endanger the lives and property of people near the pipeline transportation.Therefore,it is extremely urgent to carry out in-depth research work on the identification and judgment of pipeline defects.At present,methods for detecting pipelines include transmission detection method,stress strain measurement method,acoustic wave/ultrasonic reflection method,optical fiber detection method,etc.,but these detection methods have the disadvantages of limited detection distance and high cost for buried pipelines.With the development of robot technology,the detection method of metal natural magnetic memory phenomenon will greatly reduce the cost and improve the detection accuracy.However,a set of mature theories and methods have not yet been formed to guide the corresponding practical work.Therefore,based on the theoretical knowledge of weak magnetic detection and general working conditions,this paper has carried out a series of research work.In order to realize the pipeline defect detection under complex structure and the effective transmission,processing and identification classification of detection information under strong interference environment,this paper uses multi-physics simulation software to simulate the existence of pipeline under geomagnetic field based on the existing metal magnetic memory theory.The physical environment of the defect,and the multi-faceted feature analysis of the magnetic signal at the defect,verifying the theoretical basic knowledge of the system design;simultaneously analyzing the advantages and disadvantages of the current magnetic field signal detection method,determining the system's acquisition mechanism for the magnetic signal,including The magnetic signal processing methods including wavelet transform and wavelet denoising are studied.Combined with various defect recognition classification methods,an algorithm based on KNN recognition is designed.Finally,on the basis of theoretical research,the overall design of the system,including hardware circuit design,PCB production,lower computer program writing,PC software design,etc.,combines hardware and software to carry the experimental platform,and transform the algorithm Experiment verification for the program combined with the hardware and software platform.A series of experiments were carried out on the sample through the experimental platform.The results show that the TMR detection sensor designed in this paper can well collect the defect characteristic signals of the pipeline under the geomagnetic field environment.The combination of hardware noise reduction and software noise reduction can be very good.To remove noise interference,the KNN algorithm-based recognition classification method can well characterize the defect location.Therefore,the system has strong recognition ability for pipeline defect identification under complex structure,and can realize signal processing and recognition judgment under strong interference environment,which has certain guiding significance for the development of pipeline defect detection technology.