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Quantitative Technology and Application Research on Magnetic Flux Leakage Inspection of Pipeline Defects

Author: JiangQi
Tutor: WangTaiYong
School: Tianjin University
Course: Mechanical Manufacturing and Automation
Keywords: Pipeline Leakage magnetic field Feature Extraction Pattern Recognition Neural Networks Intelligent detection
CLC: U178
Type: PhD thesis
Year: 2003
Downloads: 943
Quote: 28
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Thesis pipes (steel pipe) magnetic flux leakage detection, quantification, intelligent problem in close connection with the detection of the actual needs, through theoretical analysis and experimental summed up the pipes (steel pipe) magnetic flux leakage intelligent detection technology, systems analysis, and defects relationship between the shape parameters of the distribution of leakage magnetic field and the magnetic flux leakage signal with defects, defect leakage magnetic signal analysis, the influencing factors of the magnetic flux leakage signal compensation, defect leakage magnetic field waveform feature extraction and quantitative identification of defects Dimensions aspects of the in-depth study, the main results and innovation as follows: Robustness of the magnetic dipole the model approximation analyze common the defect leakage magnetic field, for the lack of magnetic dipole model, the finite element method is applied to the the defect leakage magnetic field analysis to achieve a common pipeline samples defect leakage magnetic field simulation. Using a Hall element as a magnetic flux leakage detecting sensors, guide to the pipe axis sectional leakage magnetic field is the tangential component of the magnetic flux leakage signal; defect Dimensions defects inclination and shape, and the pipe material and the magnetization, the movement speed of the detector, pipes background magnetic field, the pressure in the tube and remanent magnetic flux leakage signal. Gain amplification of each channel, the brightest magnetic flux leakage signal from the adjusted waveform differential, digital filtering and smooth magnetic flux leakage signal preprocessing methods; the analyzed spatial sampling of magnetic flux leakage signal conversion sampling signal the need to wait for time to study the the magnetic flux leakage signal wavelet denoising. Research using non-linear interpolation techniques to compensate for the impact of the pipeline thickness and material, the Fourier transform and the optimal filtering method to eliminate the movement speed of the detector of magnetic flux leakage signal, and experimental verification. The introduction of the magnetic flux leakage signal pattern recognition method, the amount of defects leakage characteristics of the magnetic field and defect dimensions; principal component analysis, multivariate nonlinear regression and statistical identification analysis defect magnetic flux leakage signal waveform characteristics extraction and quantitative identification accuracy in error permitted range. Neural network pattern recognition method applied to the detection of magnetic flux leakage, BP network and wavelet neural network quantitative identification of defects, high precision, good effect. Establishment of pipes (steel pipe) intelligent detection and data analysis system defect magnetic flux leakage, the design of the system engineering database, the defect leakage magnetic field data interpolation and image processing, complete defect leakage magnetic field characteristics automatically extracted and smart recognition of defects Dimensions given the steps and process pipeline magnetic flux leakage detection. All to solve the problem of domestic pipeline magnetic flux leakage detection, quantification, intelligent, and to supplement the lack of foreign pipeline magnetic flux leakage detection.

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CLC: > Transportation > Integrated transport > Pipeline transportation > Pipeline maintenance and repair
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