A method and device for detecting steel cable defects based on a frequency-sweeping method

Abstract

A method and device for detecting steel cable defects based on a frequency sweep method. The method includes preliminary detection and secondary detection. During the preliminary detection process, an alternating magnetic field excitation and a direct current magnetic field excitation at a fixed frequency are applied to the steel cable to be measured, and the relative position and size information of the defects are output by detecting the magnetic leakage signal. Then, under the excitation of a changing alternating magnetic field, secondary detection is performed on the defective part. Through the Kalman filtering algorithm, the data under different frequency excitations are fused, and further calculation is carried out to obtain more accurate defect position and size information. On this basis, a detection device is also proposed, including a detection probe, a guide wheel, a signal processing module, a data acquisition module, and a data processing module. This method is based on AC-DC composite excitation and frequency scanning, can accurately distinguish internal and external defects of the steel cable, and greatly improves the positioning and quantitative accuracy of the defects.

Description

Technical Field

[0001] The present invention belongs to the technical field of steel cable detection, and particularly relates to a method and device for detecting steel cable defects based on a frequency-sweeping method. Background Technique

[0002] Steel cables are widely used in projects such as elevators, cableways, and bridges due to their light self-weight, good flexibility, and high strength, and play a particularly important role in them. However, during the use of steel cables, defects such as wear, corrosion, and broken wires will be formed under the influence of external forces and the external environment. When the defect size reaches a certain level, the steel cable will break, leading to accidents. Therefore, detecting steel cable defects is an essential task to ensure safety.

[0003] Currently, in many application scenarios, steel cables are inspected for damage by manual observation. This method is not only time-consuming and laborious, but also highly likely to result in missed detection of defects due to the influence of sundries such as oil stains on the surface of the steel cable, bringing adverse consequences such as low detection efficiency and low accuracy. By regularly and compulsorily replacing steel cables, the safety of the entire working scenario can be ensured, but it will cause a large amount of resource waste. Therefore, various non-destructive testing technologies, such as ray method, optical method, electromagnetic method, etc., have replaced manual work as a necessary means in the field of steel cable detection. Among them, the magnetic flux leakage detection technology in the electromagnetic method has become one of the most commonly used methods in the current field of non-destructive testing of steel cables due to its advantages such as low cost, easy implementation, and relatively high detection accuracy.

[0004] Magnetic flux leakage detection realizes the identification of the size and position of defects by detecting the magnetic flux leakage signal generated at the defect under strong magnetic excitation. However, currently, the steel cable defect detection devices based on this method generally have low accuracy, and mostly only use software algorithms to process signals to improve detection accuracy. This method has a large amount of calculation, slow calculation speed, occupies a large amount of processor resources, and is easily interfered by changes in the external environment, with limited application scope. Summary of the Invention

[0005] Aiming at the deficiencies of the prior art, the present invention proposes a method and device for detecting steel cable defects based on a frequency-sweeping method. By processing the collected magnetic flux leakage signals, the defect positions and defect depths on the surface of the steel cable are solved, realizing high-precision and high-efficiency detection of steel cable defects.

[0006] A method for detecting steel cable defects based on a frequency-sweeping method specifically includes the following steps:

[0007] Step 1: Apply a DC excitation magnetic field to the steel cable to be measured to make it in a magnetically saturated state, and then apply an AC excitation magnetic field at a certain frequency to modulate the magnetic field signal inside the steel cable to be measured; collect the magnetic signals around the steel cable to be measured. When there are defects in the steel cable to be detected, the output value of the sensor will change suddenly, and the magnetic flux leakage signal can be captured at the corresponding position.

[0008] Step 2: Perform AC and DC separation operations on the magnetic flux leakage signal captured in Step 1 to obtain the DC component and AC component of the magnetic flux leakage. After passing the AC component of the magnetic flux leakage through a band-pass filter as the signal to be measured, perform phase-sensitive demodulation together with a reference signal with the same frequency as the AC component of the magnetic flux leakage signal. After low-pass filtering the demodulation result, retain the DC quantity.

[0009] Step 3: Use equal-time pulses to perform preliminary sampling on the data processed in Step 2 to obtain sampling data, and then use equal-distance pulses to divide the sampling data. Determine the distance interval where the defect is located according to the waveform change of the sampling data, perform homogenization processing on the sampling data in this interval, calculate the number of equal-distance pulses before this distance interval and the sampling points corresponding to the amplitude mutation points and phase zero-crossing points of the magnetic flux leakage signal in this interval, solve to obtain the relative position of the defect, and solve to obtain the size information of the defect according to the change amount of the mutation signal amplitude and its corresponding sampling point range.

[0010] Step 4: Establish a steel cable model, obtain the relationship between the skin depth and the change of the AC excitation magnetic field frequency through simulation experiments, and the relationship between the defect depth and the change of the sensor output under the AC excitation magnetic field at different frequencies. Then layer the inside of the steel cable model, determine the depth of each layer and the corresponding change range of the sensor output, and establish a theoretical solution model for the defect depth to solve the theoretical depth of the defect corresponding to the change of the output under the AC excitation magnetic field at different frequencies.

[0011] Step 5: According to the solution result calculated in Step 3, locate the defect position of the steel cable to be measured, and then apply AC excitation magnetic fields at different frequencies, repeat Steps 1 to 3, obtain the measured defect depth under the AC excitation magnetic fields at different frequencies, and pass it together with the theoretical defect depth solved in Step 4 through the Kalman filtering algorithm to output the calculated defect depth values at different frequencies. Then, according to the relationship between the skin depth and the change of the AC excitation magnetic field frequency, assign weights to the calculated defect depth values at different frequencies to output the actual defect depth.

[0012] Step 6: According to the DC component of the magnetic flux leakage obtained in Step 2 and the DC quantities retained after phase-sensitive demodulation and low-pass filtering of the AC components of the magnetic flux leakage at low and high frequencies in Step 5, distinguish whether the defect is on the surface or inside the steel cable to be measured, and output the actual defect position.

[0013] Preferably, in step 5, an alternating excitation magnetic field with a frequency change range of [100, 1000] Hz is applied.

[0014] A steel cable defect detection device based on a frequency sweep method includes a detection probe, a guide wheel, a signal processing module, a data acquisition module, and a data processing module.

[0015] The detection probe includes a DC magnetic field generating device, an AC magnetic field generating device, and a magnetic field sensing device. The DC magnetic field generating device and the AC magnetic field generating device are used to apply an excitation magnetic field to the steel cable to be measured, and the magnetic field sensing device is used to collect the magnetic signals around the steel cable to be measured and transmit them to the signal processing module.

[0016] Preferably, the magnetic field sensing device includes a Hall sensor array, a tunneling magnetoresistance sensor array, a giant magnetoresistance sensor array, or an anisotropic magnetoresistance sensor array.

[0017] The guide wheel is used to move the steel cable to be measured, so that the detection probe acts on different positions of the steel cable to be measured, and drives the encoder to rotate and output equidistant pulses.

[0018] The signal processing module includes a lock-in amplifier circuit, a power amplifier circuit, and a power supply circuit. The lock-in amplifier circuit includes a sine wave generating circuit, an AC-DC separation circuit, a phase shift circuit, a pre-amplifier circuit, a band-pass filter circuit, a square wave conversion circuit, a phase-sensitive detector circuit, and a low-pass filter circuit, which are used to improve the signal-to-noise ratio of the input signal and then transmit it to the data acquisition module for sampling.

[0019] Preferably, the lock-in amplifier circuit is of the orthogonal vector type, the band-pass filter circuit is a variable frequency band-pass filter, and the phase-sensitive detector circuit is of the electronic switch type.

[0020] The data acquisition module includes an analog-to-digital conversion circuit and an encoder circuit, which perform equal-time sampling and equal-distance sampling on the data processed by the signal processing module, and then transmit it to the data processing module to judge the defect position and size on the steel cable to be measured and output the judgment result.

[0021] Preferably, the data processing module displays the defect information in the form of a three-dimensional image.

[0022] The present invention has the following beneficial effects:

[0023] By adopting the method of AC-DC composite excitation, compared with pure DC excitation, the detection accuracy can be improved, and compared with pure AC excitation, the power consumption can be greatly reduced; by using a lock-in amplifier circuit to implement signal processing, the signal-to-noise ratio can be greatly improved, with higher accuracy, faster response speed compared with software algorithm processing, and less susceptible to interference from changes in the external environment; the accuracy of the results obtained from the preliminary detection has been improved, and more accurate defect information can be obtained by performing secondary defect detection through the frequency sweeping method; a theoretical solution model for defect depth is established by combining simulation and experiment to implement the data fusion algorithm, making the finally obtained defect size closer to the actual value. Description of the Drawings

[0024] Figure 1 It is a schematic diagram of the deployment of the detection device in the embodiment;

[0025] Figure 2 It is a flowchart of the detection method in the embodiment;

[0026] Figure 3 It is a block diagram of the signal processing module in the embodiment;

[0027] Figure 4 It is a block diagram of the data fusion algorithm in the embodiment. Detailed Implementation Manner

[0028] In order to make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. It should be understood that the described embodiments are some, but not all, of the embodiments of the present invention, and the detailed description of the embodiments is not intended to limit the scope claimed by the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the protection scope of the present invention.

[0029] This embodiment performs defect detection on the steel cable of the high-altitude cableway. As Figure 1 shown, where 1 is the iron core, 2 is the permanent magnet, 3 is the coil, 4 is the steel cable to be measured, 5 is the magnetic field sensing array, 6 is the defect on the steel cable to be measured, 7 is the guide wheel, 8 is the encoder circuit, and 9 is the signal processing module. Two permanent magnets are placed on the iron core and the coil is wound to serve as a DC and AC magnetic field generating device. The DC and AC magnetic field generating device and the magnetic field sensing array are fixed together around the steel cable to be measured. The guide wheel is fixed below the steel cable to be measured, and the encoder circuit is used to record the rotation distance of the guide wheel. The signals collected by the magnetic field sensing array are input into the signal processing module for processing.

[0030] As Figure 2As shown, the defect detection process includes preliminary detection, which outputs the relative position and size information of the defect. Then, under the excitation of a changing alternating magnetic field, secondary detection is performed on the defective part, specifically including the following steps:

[0031] s1.1. Deploy the detection device as shown in Figure 1 . After fixing the detection probe, pass the steel cable to be measured through the center of the probe.

[0032] s1.2. First, perform preliminary detection by applying a composite AC and DC excitation at a fixed frequency to the steel cable.

[0033] s1.3. Start the guide wheel so that the detection probe can sequentially detect each position on the steel cable to be measured, and dynamically display the defect position and size information on the screen through a three-dimensional image.

[0034] s1.4. When a defect is detected on the steel cable to be measured, move the defect position located in s1.3 under the detection probe for secondary detection.

[0035] s1.5. Change the frequency of the AC excitation for sweep frequency detection, with the frequency change range being 100 Hz to 1000 Hz and the step size being 50 Hz.

[0036] s1.6. After the secondary detection is completed, update the defect position and size information in the three-dimensional image displayed on the screen.

[0037] s1.7. Record the defect position and size, scrap the steel cable that reaches the scrap standard, and in actual applications, through multiple detections, the change of the defect size over time can be summarized to make corresponding early warning indications and improvement measures.

[0038] As Figure 3 shown, the lock-in amplifier circuit in the signal processing module includes a sine wave generation circuit, a phase shift circuit, an AC-DC separation circuit, a preamplifier circuit, a band-pass filter circuit, a square wave conversion circuit, a phase-sensitive detector circuit, and a low-pass filter circuit. During the preliminary detection process, the sine wave generation circuit generates a sine wave of 100 Hz. During the secondary detection process, the sine wave generation circuit generates sine waves of different frequencies, thereby changing the frequency of the AC excitation magnetic field. A part of the sine wave passes through the power amplifier circuit and then acts on the coil to generate an AC excitation magnetic field of the corresponding frequency. Another part passes through the square wave conversion circuit and is input into the phase-sensitive detector circuit as a reference signal. The output signal of the magnetic field sensing array passes through the AC-DC separation circuit, and the AC component is sequentially preamplified and band-pass filtered, and then multiplied by the reference signal with a phase difference of 90 degrees through the phase-sensitive detector circuit. The output of the phase-sensitive detector circuit and the DC component output by the AC-DC separation circuit are jointly low-pass filtered and then enter the data acquisition module for sampling processing.

[0039] The data acquisition module performs equal-time sampling and equal-distance sampling on the data processed by the signal processing module, and then transmits it to the data processing module. Data fusion is performed in the data processing module to finally obtain the more accurate actual depth and actual position of the defect. The specific process is as follows:

[0040] S2.1 Simulate and experiment on the distribution of skin effect inside the steel cable under different-frequency AC excitations, obtain the variation relationship between the skin depth δ and the excitation frequency f, and establish the model δ(f);

[0041] S2.2 Simulate and experiment on the relationship between the defect depth H and the change in the sensor output Δv under different-frequency AC excitations, obtain the variation relationship between H and Δv, and establish the model H(Δv);

[0042] S2.3 Use the model δ(f) established in S2.1 to layer the inside of the steel cable and calculate the layer depth H corresponding to different frequencies n ;

[0043] S2.4 Combine simulation and experiment to obtain, respectively, at different frequencies, when the defect depth is its corresponding maximum depth H nmax , the maximum value of the theoretical output change Δv of the sensor nmax ;

[0044] S2.5 Integrate the above model δ(f) and model H(Δv) to obtain the theoretical depth solution model of the defect at different frequencies where Δv n is the change in the sensor output at frequency n. The theoretical depth of the defect is solved for this theoretical model at different frequencies, and its theoretical noise can be regarded as Gaussian white noise.

[0045] S2.6 For the measured depth of the defect depth at different frequencies obtained during the secondary detection process, its measured noise can also be regarded as Gaussian white noise; as Figure 4 shown, the Kalman gain is calculated through the covariance of the theoretical noise and the measured noise; thus, more accurate distribution depths at different frequencies are obtained through multi-channel Kalman filtering, and then weight values are assigned to them respectively according to the preference degrees of internal and external defects reflected by the skin effect for different-frequency excitation conditions, and finally the accurate actual depth of the defect is obtained.

Claims

1. A method for detecting steel cable defects based on a frequency sweep method, characterized in that: Specifically, it includes the following steps: Step 1: Apply a DC excitation magnetic field to the steel cable to be measured to make it in a magnetically saturated state, and then apply an AC excitation magnetic field at a certain frequency to modulate the magnetic field signal inside the steel cable to be measured; collect the magnetic flux leakage signal around the steel cable to be measured; Step 2: Perform AC and DC separation operations on the magnetic flux leakage signal captured in Step 1 to obtain the DC component and AC component of the magnetic flux leakage; use the AC component of the magnetic flux leakage after band-pass filtering as the signal to be measured, and perform phase-sensitive detection together with a reference signal with the same frequency as the AC component of the magnetic flux leakage signal. The detection result is low-pass filtered to retain the DC quantity; Step 3: Use equal-time pulses to perform preliminary sampling on the data processed in Step 2 to obtain sampling data, and then use equal-distance pulses to divide the sampling data. Determine the distance interval where the defect is located according to the waveform change of the sampling data, perform homogenization processing on the sampling data in this interval, calculate the number of equal-distance pulses before this distance interval and the sampling points corresponding to the amplitude mutation points and phase zero-crossing points of the signal in this interval, solve to obtain the relative position of the defect, and solve to obtain the size information of the defect according to the change amount of the mutation signal amplitude and its corresponding sampling point range; Step 4: Establish a steel cable model, obtain the relationship between the skin depth and the change of the AC excitation magnetic field frequency through simulation experiments, and the relationship between the defect depth and the change amount of the sensor output under the AC excitation magnetic field of different frequencies; then layer the inside of the steel cable model, determine the depth of each layer and the corresponding change range of the sensor output, and establish a theoretical defect depth solving model for solving the theoretical defect depth corresponding to the change amount of the output under the AC excitation magnetic field of different frequencies; the specific process is as follows: S4.1: Simulate and experiment on the distribution of the skin effect inside the steel cable under different frequencies of AC excitation to obtain the change relationship between the skin depth δ and the excitation frequency f, and establish the model δ(f); S4.2: Simulate and experiment on the relationship between the defect depth H and the change amount of the sensor output Δv under different frequencies of AC excitation to obtain the change relationship between H and Δv, and establish the model H(Δv); S4.

3. Use the model δ(f) established in S4.1 to perform stratification on the inside of the steel cable and calculate the layer depth H corresponding to different frequencies. n ; S4.

4. Combine simulation and experiment to obtain, respectively, the maximum theoretical output change Δv of the sensor at different frequencies when the defect depth is its corresponding maximum depth H nmax ; nmax ; S4.

5. Combining the above models δ(f) and H(Δv), a theoretical depth solution model for defects at different frequencies is obtained. where Δv n is the change in the sensor output at a frequency of n; the theoretical depth of the defect is solved for this theoretical model at different frequencies. Step 5: According to the solution result calculated in Step 3, locate the defect position of the steel cable to be measured, and then apply AC excitation magnetic fields at different frequencies, repeat Steps 1 to 3 to obtain the measured defect depth under the AC excitation magnetic fields of different frequencies, and pass it together with the theoretical defect depth solved through Step 4 through the Kalman filtering algorithm to output the calculated value of the defect depth at different frequencies; then assign weights to the calculated values of the defect depth at different frequencies according to the relationship between the skin depth and the change of the AC excitation magnetic field frequency, and output the actual defect depth; Step 6: Distinguish whether the defect is on the surface or inside the steel cable to be measured according to the DC component of the magnetic flux leakage obtained in Step 2 and the DC quantity retained after phase-sensitive detection and low-pass filtering of the AC components of the magnetic flux leakage at low and high frequencies in Step 5, and output the actual position of the defect.

2. The method for detecting steel cable defects based on the frequency sweeping method according to claim 1, characterized in that: In Step 5, an AC excitation magnetic field with a frequency change range of [100, 1000] Hz is applied.

3. A steel cable defect detection device based on a frequency sweep method, characterized in that: For performing the detection method as described in any one of Claims 1 or 2; The detection device includes a detection probe, a guide wheel, a signal processing module, a data acquisition module, and a data processing module; The detection probe includes a DC magnetic field generating device, an AC magnetic field generating device, and a magnetic field sensing device. The DC magnetic field generating device and the AC magnetic field generating device are used to apply an excitation magnetic field to the steel cable to be measured, and the magnetic field sensing device is used to collect the magnetic signals around the steel cable to be measured and transmit them to the signal processing module; The guide wheel is used to move the steel cable to be measured so that the detection probe acts on different positions of the steel cable to be measured; The signal processing module includes a lock-in amplifier circuit, a power amplifier circuit, and a power supply circuit. The lock-in amplifier circuit includes a sine wave generating circuit, an AC / DC separation circuit, a phase shift circuit, a pre-amplifier circuit, a band-pass filter circuit, a square wave conversion circuit, a phase-sensitive detector circuit, and a low-pass filter circuit, which are used to perform the data processing process in step 2, improve the signal-to-noise ratio of the input signal, and then transmit it to the data acquisition module for sampling; The data acquisition module includes an analog-to-digital conversion circuit and an encoder circuit, which perform equal-time sampling and equal-distance sampling on the data processed by the signal processing module, and then transmit it to the data processing module to judge the defect position and size on the steel cable to be measured and output the judgment result.

4. The steel cable defect detection device based on a frequency sweeping method according to claim 3, wherein: The magnetic field sensing device includes a Hall sensor array, a tunneling magnetoresistance sensor array, a giant magnetoresistance sensor array, or an anisotropic magnetoresistance sensor.

5. The cable defect detection device based on the frequency sweeping method according to claim 3, characterized in that: The lock-in amplifier circuit is of the orthogonal vector type, the band-pass filter circuit is a variable-frequency band-pass filter, and the phase-sensitive detector circuit is of the electronic switch type.

6. The steel cable defect detection device based on the frequency sweeping method according to claim 3, characterized in that: The data processing module displays the defect information in the form of a three-dimensional image.

7. The steel cable defect detection device based on a frequency sweeping method according to claim 3, wherein: When performing step 2, the sine wave generating circuit generates a sine wave of 100 Hz. When performing step 5, the sine wave generating circuit generates sine waves of different frequencies, thereby changing the frequency of the AC excitation magnetic field.

8. The cable defect detection device based on the frequency sweeping method according to claim 3 or 7, characterized in that: A part of the sine wave acts on the AC magnetic field generating device after passing through the power amplifier circuit to generate an AC excitation magnetic field of the corresponding frequency, and the other part passes through the square wave conversion circuit and is input into the phase-sensitive detector circuit as a reference signal.

9. The steel cable defect detection device based on the frequency sweeping method according to claim 3, characterized in that: After the output signal of the magnetic field sensing device passes through the AC / DC separation circuit, the AC component passes through pre-amplification and band-pass filtering in sequence, and then is multiplied by the reference signal with a phase difference of 90 degrees through the phase-sensitive detector circuit. The output of the phase-sensitive detector circuit and the DC component output by the AC / DC separation circuit pass through low-pass filtering together and then enter the data acquisition module for sampling processing.

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