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In equipment maintenance and management, corrosion detection is a crucial step. Although traditional detection techniques are effective, with technological advancements, pulse eddy current testing has...
Factors influencing magnetic flux leakage testing accuracy: magnetizing strength, defect flux leakage field, detection sensors, data acquisition, and data analysis technology.
The magnetizing system applies an excitation magnetic field to the pipe wall, where anomalies generate a response, i.e., a coupled leakage field. The excitation strength of the magnetizing system must be strong enough to produce a measurable leakage field at defect locations. Magnetic flux leakage testing's most significant influence factor is magnetization strength. Higher detection and quantization accuracy require high and relatively uniform magnetization strength.
Applying a higher-than-saturation magnetizing strength will generate a stronger leakage signal, increasing the detection rate of metal loss.
The defect leakage field does not correspond one-to-one with defect shapes; the same leakage field may have different shapes. It is mainly influenced by the geometric dimensions and positions of defects, such as on the inner or outer wall. For example, with circular defects, the leakage field spreads in the circumferential direction, making it elliptical instead of circular. The tendency of flux passing through the pipeline leads to circumferential propagation, known as the butterfly effect, where the magnetic lines' path widens at the defect, reaching several times the normal wall thickness.
Main parameters affecting the defect leakage field: depth, length, width, taper, planar shape, stress, and deformation.
Magnetic flux leakage testing is based on sensor systems detecting leakage signals, with measurement systems converting these signals into electrical signals. The sensor system filters and smooths the real magnetic field, containing noise; thus, the detected field is not entirely consistent with the real field.
Magnetic flux leakage testing devices generally use two types of sensors: Hall elements and coils. Coils measure the rate of change of the magnetic field, while Hall elements measure the actual magnetic field intensity. Previously, coils were used in MFL systems that did not require an electrical source. Currently, Hall-effect sensors are widely used. Coils respond to electromagnetic field changes, with the output dependent on velocity, losing the constant part, i.e., background magnetic field cannot be detected. Hall-effect sensors convert the magnetic field level directly into output voltage, requiring an operating power source.
Magnetic flux leakage testing equipment records leakage signals at fixed intervals both axially and circumferentially; the circumferential interval is determined by the number of sensors. 12-inch three-axis high-definition sensors have over300 sensors, with a circumferential spacing of less than4mm.
The axial acquisition interval is generally within2mm. In a single magnetic flux leakage testing project, the data volume is: number of sensors times inspection distance divided by the sampling interval. For a12-inch detector, if it is12-bit AD collection, inspecting100 kilometers requires more than25G of storage space.
Magnetic flux leakage testing data must be displayed in a certain way for quick retrieval and identification of defects and pipeline features (welds, branches, valves, etc.).
Data analysis refers to the process of estimating the geometric shape and depth of defects based on the MFL signal. Magnetic flux leakage testing data analysis technology and accuracy rely on the detector's capabilities and limitations, as well as defect quantification methods.
The relationship between defect leakage signals and defect geometric dimensions is complex. Various defect size quantification methods exist, including template matching, mathematical statistics, and neural networks. The most commonly used tangent quantification method is the mathematical statistics method, which develops defect geometric size calculation models by combining defect signal characteristics, defect width and length, background magnetic field levels, detector speed, and magnetization levels.
