The invention discloses a low-frequency electromagnetic detection method for large-scale damage of ferromagnetic materials based on broadband excitation. Detection direction of the magnetic field signal of low-frequency electromagnetic sensor is determined according to the size of ferromagnetic member detection defect; the reference signal and detection signal acquisition position are selected, fix the distance between sensor and tested part, excite a Chirp signal as a broadband excitation signal to perform broadband excitation low-frequency electromagnetic detection; the computer processes collected broadband detection signal; use the difference of Euclidean distance between reference signal and defect detection signal as a defect characterization parameter to obtain the Euclidean distance curve of different depth defects on the upper and lower surfaces of ferromagnetic components with the detection position. Through the analysis and processing of the low-frequency electromagnetic broadband detection signal, the Euclidean response signal and reference signal under broadband excitation are used to characterize the change of material damage degree, which can effectively reduce the influence of magnetic field skin effect, and is beneficial to the effective characterization of the upper and lower material surface defects of at different depths.

The invention discloses a low-frequency electromagnetic detection method for large-scale damage of ferromagnetic materials based on broadband excitation. Detection direction of the magnetic field signal of low-frequency electromagnetic sensor is determined according to the size of ferromagnetic member detection defect; the reference signal and detection signal acquisition position are selected, fix the distance between sensor and tested part, excite a Chirp signal as a broadband excitation signal to perform broadband excitation low-frequency electromagnetic detection; the computer processes collected broadband detection signal; use the difference of Euclidean distance between reference signal and defect detection signal as a defect characterization parameter to obtain the Euclidean distance curve of different depth defects on the upper and lower surfaces of ferromagnetic components with the detection position. Through the analysis and processing of the low-frequency electromagnetic broadband detection signal, the Euclidean response signal and reference signal under broadband excitation are used to characterize the change of material damage degree, which can effectively reduce the influence of magnetic field skin effect, and is beneficial to the effective characterization of the upper and lower material surface defects of at different depths.

A system includes a tool to dispose in a wellbore lined with pipe. The tool includes first and second receiver coils having a non-uniform winding along a longitudinal axis, a third receiver coil having a non-uniform winding coaxial with at least one of the first and second receiver coils, and a transmitter. The system includes a processor to execute instructions to perform operations including causing the transmitter to emit an induced magnetic field, measuring the induced magnetic field using the first receiver coil to create a first measurement and using the first and second receiver coils to create a second measurement. The operations include determining a static magnetic field, selecting the first or second measurement based on a magnitude of the static magnetic field to determine a selected measurement, and determining at least one property of the pipe using the selected measurement.

The present invention discloses a 3D defect detection method with magnetic flux leakage testing (MFLT). It has advantages of higher accuracy of 3D detection of defect and simpler testing device relative to the prior MFLT art. This method includes the following steps: S1: artificially magnetizing a to-be-tested structure, and measuring its MFLT signals {B}; S2: inverting magnetic charge distribution of the interior of the to-be-tested structure by using a magnetic charge distribution reconstruction algorithm to obtain the magnetic charge density of a non-defective region of the to-be-tested structure; and S3: using the magnetic charge density of the non-defective region of the to-be-tested structure as a known constant, and conducting inverse iteration to reconstruct defect depth of the defective region to obtain a 3D image of the defective region of the to-be-tested structure.

The present invention discloses a 3D defect detection method with magnetic flux leakage testing (MFLT). It has advantages of higher accuracy of 3D detection of defect and simpler testing device relative to the prior MFLT art. This method includes the following steps: S1: artificially magnetizing a to-be-tested structure, and measuring its MFLT signals {B}; S2: inverting magnetic charge distribution of the interior of the to-be-tested structure by using a magnetic charge distribution reconstruction algorithm to obtain the magnetic charge density of a non-defective region of the to-be-tested structure; and S3: using the magnetic charge density of the non-defective region of the to-be-tested structure as a known constant, and conducting inverse iteration to reconstruct defect depth of the defective region to obtain a 3D image of the defective region of the to-be-tested structure.

A magnetic body inspection device (100) is a magnetic body inspection device for inspecting states of a plurality of magnetic bodies (W) by a total magnetic flux method that measures a magnetic flux inside the magnetic body (W). The device includes a plurality of detection coils (10) each for detecting the magnetic field of each of the magnetic bodies (W), an excitation unit (11) provided for the plurality of magnetic bodies (W), and a detection signal output unit (12) for outputting a detection signal based on the magnetic field of each of the magnetic bodies (W).

Provided is an intelligent analysis system for inner detecting magnetic flux leakage (MFL) data in pipelines, including a complete data set building module, a discovery module, a quantization module and a solution module, wherein: a complete data set building method is adopted in the complete data set building module to obtain a complete magnetic flux leakage data set; a pipeline connecting component discovery method is adopted in the discovery module to obtain the precise position of a weld; an anomaly candidate region search and identification method is adopted in the discovery model to find out magnetic flux leakage signals with defects; a defect quantization method based on a random forest is adopted in the quantization module to obtain a defect size; and a pipeline solution based on an improved ASME B31G standard is adopted in the solution module to output an evaluation result.

Provided is an intelligent analysis system for inner detecting magnetic flux leakage (MFL) data in pipelines, including a complete data set building module, a discovery module, a quantization module and a solution module, wherein: a complete data set building method is adopted in the complete data set building module to obtain a complete magnetic flux leakage data set; a pipeline connecting component discovery method is adopted in the discovery module to obtain the precise position of a weld; an anomaly candidate region search and identification method is adopted in the discovery model to find out magnetic flux leakage signals with defects; a defect quantization method based on a random forest is adopted in the quantization module to obtain a defect size; and a pipeline solution based on an improved ASME B31G standard is adopted in the solution module to output an evaluation result.

Method for determining the geometry of one or more real, examined defects of a metallic, in particular magnetizable object, in particular a pipe or a tank, by means of at least two reference data sets of the object generated on the basis of different, non-destructive measurement methods,

wherein the object is at least partially represented on or by an at least two-dimensional, preferably three-dimensional, object grid, in an EDP unit,
wherein an output defect geometry, in particular on the object grid or an at least two-dimensional defect grid, is generated by inversion of at least parts of the reference data sets, in particular by at least one neural network (NN) trained for this object, a respective prediction data set for the non-destructive measurement methods used in the generation of the reference data sets is calculated on the basis of the output defect geometry by a simulation routine, a comparison of at least parts of the prediction data sets with at least parts of the reference data sets is carried out and, depending on at least one accuracy measure, the method for determining the geometry of the defect is terminated or an iterative adjustment of the output defect geometry to the geometry of the real defect(s) is carried out, as well as methods for determining a load limit (FIG. 1).

Method for determining the geometry of one or more real, examined defects of a metallic, in particular magnetizable object, in particular a pipe or a tank, by means of at least two reference data sets of the object generated on the basis of different, non-destructive measurement methods,

wherein the object is at least partially represented on or by an at least two-dimensional, preferably three-dimensional, object grid, in an EDP unit,
wherein an output defect geometry, in particular on the object grid or an at least two-dimensional defect grid, is generated by inversion of at least parts of the reference data sets, in particular by at least one neural network (NN) trained for this object, a respective prediction data set for the non-destructive measurement methods used in the generation of the reference data sets is calculated on the basis of the output defect geometry by a simulation routine, a comparison of at least parts of the prediction data sets with at least parts of the reference data sets is carried out and, depending on at least one accuracy measure, the method for determining the geometry of the defect is terminated or an iterative adjustment of the output defect geometry to the geometry of the real defect(s) is carried out, as well as methods for determining a load limit (FIG. 1).