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In modern industrial manufacturing, eddy current testing technology has become an important tool for ensuring the surface quality of forged steel rolls. Eddy current roll surface detection is based on...
As key energy sources for societal production, the safety of transporting and storing oil and natural gas is crucial. Pipelines and storage tanks can suffer from corrosion and damage over long-term use, and if not promptly detected and repaired, may lead to serious accidents. Therefore, the application of magnetic flux leakage NDT technology in pipeline damage detection is becoming increasingly widespread.
Magnetic flux leakage NDT is a detection method based on the principle of magnetic field changes. Specifically, when an external magnetic field is applied to ferromagnetic materials, these materials become magnetized. If there are defects on the surface of the material, such as cracks, pores, or corrosion, the propagation path of the magnetic lines of force will change, and some magnetic lines will leak out from the material's surface. This is known as the “magnetic flux leakage phenomenon.” By using magnetic sensors to detect these leaked magnetic signals, the continuity information of the material can be obtained, thus identifying internal defects in the material.
Compared to other NDT methods, magnetic flux leakage NDT has the following advantages: First, it does not require complex preprocessing of the object being tested, making it simple to operate and highly automated; secondly, it can quickly obtain detection signals, making it suitable for long-distance and large-area detection tasks; furthermore, magnetic flux leakage NDT can effectively detect and provide quantitative analyses for different types of defects, such as pores, corrosion, and cracks. Therefore, it is widely used in damage detection of key equipment such as oil and gas pipelines and storage tanks.
In the pipeline inspection process, magnetic flux leakage NDT first magnetizes the pipe wall through a detector and then detects the propagation of magnetic lines within the pipe wall. When there are defects in the pipeline, magnetic flux leakage will occur, and the detector will store the collected leakage signal into a data acquisition system. Subsequently, the signal is processed for noise reduction and optimization through preprocessing technology, and finally, the detection results are presented in image form via visualization tools, allowing analysts to quickly identify the damage location and type in the pipeline.
In recent years, with advancements in data processing technology, magnetic flux leakage NDT has shown stronger abilities in handling magnetic field signals under complex conditions. For instance, through signal processing methods such as wavelet decomposition, it is possible to effectively extract small defect signals hidden in noise and accurately calculate defect size and depth through inversion techniques. These technological advancements have made magnetic flux leakage NDT more mature in pipeline damage detection applications and are widely applied in the regular inspection and maintenance of oil and gas pipelines, chemical storage tanks, and other fields.
Magnetic Flux Detection and Signal Acquisition
The internal detector is pulled into the pipeline to magnetize the pipe wall and collect the magnetic flux leakage signals. The collected signals are stored in the data acquisition room before extracting the detector from the pipeline.
Signal Preprocessing and Visualization
First, calibrate the detector parameters before testing. Then, extract defect signals using methods like wavelet decomposition, and finally, present the defect signals in image form through visualization programs.
Anomalous Signal Identification
Identify anomalous signals through signal characteristic analysis and classify these signals to distinguish between pipeline damage and non-damaged states.
Defect Quantitative Analysis
Invert defect location, size, and depth based on defect signals.
Equipment Safety Assessment and Prediction
Perform safety assessment and prediction of pipeline operation status based on defect inversion results and factors such as pipeline environment.
