CN115494147A - Bridge cable damage detection method of micro-magnetic detector based on wavelet transform algorithm - Google Patents
Bridge cable damage detection method of micro-magnetic detector based on wavelet transform algorithm Download PDFInfo
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Abstract
The invention discloses a method for detecting damage of a bridge cable of a micro-magnetic detector based on a wavelet transform algorithm, which comprises the following steps of: s10, placing the magnetic sensor device on a bridge cable, and enabling a detection end to surround the periphery of the bridge cable; s20, sending an instruction through a detection terminal to control the magnetic sensing device to operate on the bridge cable; s30, collecting magnetic signals near the cable through a magnetic sensing device; and S40, analyzing the collected cable magnetic leakage signals through a wavelet transform algorithm to obtain the positions of the defects of the steel wires in the cable. The invention can detect the defect position of the high-strength steel wire or steel strand in the cable through wavelet transformation.
Description
Technical Field
The invention belongs to the technical field of bridge cable detection, and particularly relates to a bridge cable damage detection method of a micro-magnetic detector based on a wavelet transform algorithm.
Background
Currently, the detection and protection of bridge cables includes the detection of an inner steel wire structure and the detection of an outer protective layer. The detection of the internal steel wire structure mainly aims at the fatigue damage caused by the stress deformation (including bridge load, wind power and other vibration), the change of the metal cross-sectional area, water mist erosion and the like of the internal steel wire of the cable. The detection method includes a cable force detection method, an eddy current detection method, a magnetic flux leakage detection method, an optical fiber detection method and the like.
Although a multifunctional cable climbing robot is developed at present, the corrosion damage degree in a cable member is difficult to detect in the prior art, and the detection technology of the cable member needs to be further improved, so that the cable climbing robot becomes a great problem which troubles bridge workers worldwide; the defect identification and positioning can not be effectively realized, the positioning accuracy is poor, and the identification accuracy is poor.
Disclosure of Invention
In order to solve the problems, the invention provides a method for detecting the damage of a bridge cable of a micro-magnetic detector based on a wavelet transform algorithm.
In order to achieve the purpose, the invention adopts the technical scheme that: a method for detecting damage of a bridge cable of a micro-magnetic detector based on a wavelet transform algorithm comprises the following steps:
s10, placing the magnetic sensor device on a bridge cable, and enabling a detection end to surround the periphery of the bridge cable;
s20, sending an instruction through a detection terminal to control the magnetic sensing device to operate on the bridge cable;
s30, collecting magnetic signals near the cable through a magnetic sensing device;
and S40, analyzing the collected cable magnetic leakage signals through a wavelet transform algorithm to obtain the positions of the defects of the steel wires in the cable.
Further, in the step S30, the collecting the magnetic signal near the cable by the magnetic sensor includes the steps of:
s301, a climbing mechanism of a magnetic sensing device is positioned at one point of a bridge cable, and magnetic induction data of the point is obtained through a magnetic detection mechanism of the magnetic sensing device;
s302, after the magnetic induction data of the point location is completely fed back, the climbing mechanism drives the magnetic detection mechanism to enter the point location of the next detection area, and the magnetic induction data of the next area is fed back;
and S303, repeating the steps S301 and S302 to complete the detection of the whole bridge cable and acquire the magnetic signals of the whole bridge cable.
Further, the magnetic induction data acquisition process comprises the following steps: and applying an excitation current through the magnetic induction sensor, and returning a signal through the change of the induced electromotive force of the induction coil to acquire magnetic induction data.
Further, in step S40, analyzing the collected cable leakage magnetic signals by a wavelet transform algorithm to obtain the positions of the defects of the steel wires in the cable, including the steps of:
s401, collecting magnetic signals by a magnetic sensor array of the magnetic sensor device, judging the condition of signal change, indicating that no defect exists under the condition of no signal change, and indicating that the defect exists under the condition of signal change;
s402, under the condition that the defects exist in the S401, the positions where the defects are located are distinguished through the size of signal transformation amplitude values by using a wavelet transformation method, wherein the highest position of the amplitude values is the outermost layer of the defects;
and S403, according to the change of other amplitudes, utilizing the monotonicity number and the wavelet energy to distinguish through the number and the energy, wherein the number is small, the energy is small, the number is an intermediate layer, and the number is large, and the energy is large, the intermediate layer is arranged.
Furthermore, after the magnetic sensor array of the magnetic sensor device collects magnetic signals, the signals of the multiple sensors are directly accumulated and averaged on the basis of removing direct current, so that signals after synthesis and enhancement can be obtained and then analyzed.
Further, a signal-to-noise ratio is used for detecting a defect signal, and when the signal-to-noise ratio exceeds a set threshold value, the defect signal can be judged to be defective;
the signal-to-noise ratio is calculated according to the following rule: the signal size is the maximum value of the frame data, the noise bottom is selected from the number of points which are equally spaced before and after the position of the maximum value of the signal, and l/2 points are respectively selected for summation and averaging.
Furthermore, a smooth Gaussian function with low-pass property is adopted, amplitude transformation analysis is carried out according to the first derivative of the smooth Gaussian function as a wavelet basis function, and a catastrophe point corresponding to the signal is found.
Furthermore, according to the first derivative of the smooth Gaussian function as a wavelet basis function, performing differential transformation on the waveform after the wavelet transformation, and solving a wavelet transformation energy value through the differential transformation;
obtaining energy peak values under each scale and summing the energy peak values to obtain total energy values of different defect types; and performing curve fitting on the total energy value to obtain standard values of different defect types.
The magnetic induction bridge cable detection device comprises a support main body, climbing mechanisms, magnetic detection mechanisms and video monitoring mechanisms, wherein the support main body is of a cylindrical structure, the climbing mechanisms are symmetrically arranged at two ends of the support main body, and the micro-magnetic detection mechanisms are arranged on the middle section of the support main body in a surrounding manner; the support main body adopts an openable and closable cylindrical structure, and a bridge cable to be detected penetrates through a central shaft of the support main body; the climbing mechanism comprises a plurality of climbing subunits which correspond to each other, surround the bridge cable therein and move along the bridge cable; the magnetic detection mechanism comprises a plurality of magnetic detection subunits which surround the bridge cable to be detected, and the detection end of the magnetic detection mechanism faces the bridge cable at the center; the detection data of the magnetic detection mechanism are collected and analyzed by the central controller and then transmitted to the detection terminal in a wireless manner; the video monitoring mechanism comprises four high-definition cameras, the four high-definition cameras are symmetrically arranged at four corners of a square at the upper end of the supporting main body, the bridge cable in the center is monitored for 360 degrees, and monitoring data are analyzed and processed through a PC end of the wireless transmission channel; meanwhile, the detection terminal wirelessly sends a control instruction to control the climbing mechanism to operate.
The beneficial effects of the technical scheme are as follows:
during detection, the steel wire is magnetized, and a magnetic field signal detected by the micro-magnetic detector is processed through wavelet analysis to judge the defect condition of the high-strength steel wire or the steel strand in the cable, so that defect identification and positioning are obtained through monotonicity and wavelet analysis through magnetic induction signals and a climbing position, the problem that the corrosion degree in the bridge cable cannot be accurately detected can be solved, the monitoring accuracy of the cable can be improved, and the corrosion position of the bridge cable can be accurately positioned.
The device climbs along a bridge cable by adjusting the climbing structure, and simultaneously detects magnetic induction signals by the magnetic detection structure; the device can accurately provide detection conditions for the magnetic induction device by adjusting the climbing structure, can provide accurate detection height, position and detection, and utilizes the magnetic induction signal and the climbing position to analyze the type of the bridge cable defect through continuous wavelet transformation, thereby completing defect identification and positioning.
The intelligent detection method for the defects of the bridge cable is formed, the climbing mechanism runs on the bridge cable, the magnetic detection mechanism is used for obtaining the magnetic signals of the bridge cable, the detection of the whole bridge cable is completed, the magnetic induction data of the whole bridge cable is obtained, then the magnetic induction data of the whole bridge Liang Lansuo is analyzed, the state result of the bridge cable is obtained, the problem that the accurate positioning of the interior of the bridge cable cannot be accurately detected can be solved, the monitoring accuracy of the cable can be improved, and the internal corrosion position of the bridge cable can be accurately positioned.
Drawings
FIG. 1 is a schematic flow chart of a method for detecting damage to a bridge cable by a wavelet transform algorithm micro-magnetic detector according to the present invention;
FIG. 2 is a schematic diagram of a magnetic sensor device according to an embodiment of the present invention;
FIG. 3 is a schematic view of the location of a defect in a cable in an embodiment of the invention;
FIG. 4 is a schematic diagram of a defect location of a detection curve after wavelet transform in an embodiment of the present invention;
FIG. 5 is a graph comparing before and after signal enhancement processing according to an embodiment of the present invention;
FIG. 6 is a diagram illustrating an energy fit curve according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described with reference to the accompanying drawings.
In this embodiment, referring to fig. 1, the present invention provides a method for detecting a bridge cable damage by a micro-magnetic detector based on a wavelet transform algorithm, including the steps of:
s10, placing the magnetic sensor device on a bridge cable, and enabling a detection end to surround the periphery of the bridge cable;
s20, sending an instruction through a detection terminal to control the magnetic sensing device to operate on the bridge cable;
s30, collecting magnetic signals near the cable through a magnetic sensing device;
and S40, analyzing the collected cable magnetic leakage signals through a wavelet transform algorithm to obtain the positions of the defects of the steel wires in the cable.
As shown in fig. 2, the magnetic induction bridge cable detection device comprises a support main body 1, climbing mechanisms 2, magnetic detection mechanisms 3 and a video monitoring mechanism, wherein the support main body 1 is of a cylindrical structure, the climbing mechanisms 2 are symmetrically arranged at two ends of the support main body 1, and the magnetic detection mechanisms 3 are arranged on the middle section of the support main body 1 in a surrounding manner; the support main body 1 adopts an openable and closable cylindrical structure, and a bridge cable 5 to be detected penetrates through a central shaft of the support main body 1; the climbing mechanism 2 comprises a plurality of climbing subunits which correspond to each other and in which a bridge cable is encircled and moves along the bridge cable; the magnetic detection mechanism 3 comprises a plurality of magnetic detection subunits which surround the bridge cable 5 to be detected, and the detection end of the magnetic detection mechanism 3 faces the bridge cable at the center; the detection data of the magnetic detection mechanism 3 are collected and analyzed by the central controller and then are wirelessly transmitted to the detection terminal; meanwhile, the detection terminal wirelessly sends a control instruction to control the climbing mechanism 2 to operate. The video monitoring mechanism comprises four high-definition cameras which are arranged according to four corners of a square, the detection end monitors the cable for 360 degrees on the bridge cable which faces the center, and the obtained video signals are transmitted to the PC end through a wireless network for analysis.
As an optimized solution of the above embodiment, the step S30 of collecting the magnetic signal near the cable by the magnetic sensor includes the steps of:
s301, a climbing mechanism of a magnetic sensing device is positioned at one point of a bridge cable, and magnetic induction data of the point is obtained through a magnetic detection mechanism of the magnetic sensing device;
s302, after the magnetic induction data of the point location is completely fed back, the climbing mechanism drives the magnetic detection mechanism to enter the point location of the next detection area, and the magnetic induction data of the next area is fed back;
and S303, repeating the steps S301 and S302 to complete the detection of the whole bridge cable and acquire the magnetic signals of the whole bridge cable.
Wherein, the acquisition process of the magnetic induction data comprises the following steps: and applying an excitation current through the magnetic induction sensor, and returning a signal through the change of the induced electromotive force of the induction coil to acquire magnetic induction data.
As an optimization scheme of the above embodiment, in the step S40, analyzing the collected cable leakage magnetic signals through a wavelet transform algorithm to obtain the positions of the defects of the steel wires inside the cable, including the steps of:
s401, collecting magnetic signals by a magnetic sensor array of the magnetic sensor device, judging the condition of signal change, indicating that no defect exists under the condition of no signal change, and indicating that the defect exists under the condition of signal change;
s402, under the condition that the defects exist in the S401, the positions where the defects are located are distinguished through the size of signal transformation amplitude values by using a wavelet transformation method, wherein the highest position of the amplitude values is the outermost layer of the defects;
and S403, according to the change of other amplitudes, utilizing the monotonicity number and the wavelet energy to distinguish through the number and the energy, wherein the number is small, the energy is small, the number is an intermediate layer, the number is large, and the energy is large, as shown in the figures 3 and 4.
After the magnetic sensor array of the magnetic sensor device collects magnetic signals, the signals of the multiple sensors are directly accumulated and averaged on the basis of removing direct current, and then the signals after synthesis and enhancement can be obtained and then analyzed.
The direct current of the multi-channel sensor signals is removed, and the signals are directly accumulated and averaged, and the calculation formula is as follows:
the results before and after one frame of data accumulation smoothing are given below, and in the simulation, only two paths of sensors closest to the defect are selected for accumulation processing. It can be seen that after processing, as shown in fig. 5, (a) before processing and (b) after processing, it can be seen that the defect signals are more apparent after processing, and the noise is greatly attenuated.
Preferably, the signal-to-noise ratio is used for detecting a defect signal, and when the signal-to-noise ratio exceeds a set threshold value, the defect signal can be judged to be defective;
the signal-to-noise ratio is calculated according to the following rule: the signal size is the maximum value of the frame data, the noise bottom is selected from the number of points which are equally spaced before and after the position of the maximum value of the signal, and l/2 points are respectively selected for summation and averaging.
The mathematical formula is as follows
The value of N is selected to exceed the number of actual points of the effective signal, and the value of l is obtained empirically and can be set to 16. The ratio of the signal to the noise floor is used as a judgment basis, only if the ratio is larger than a certain threshold value, the target signal is considered to exist, and the size of the threshold value is obtained according to actual test data. Actual test data shows that the signal-to-noise ratio of the defect signal is generally over 10dB and can be easily detected from the data after continuous wavelet transform.
Preferably, a smooth gaussian function with low-pass property is adopted, and amplitude transformation analysis is carried out according to the first derivative of the smooth gaussian function as a wavelet basis function to find out the catastrophe point corresponding to the signal.
Taking the first derivative of the smooth Gaussian function as a wavelet basis function, performing differential transformation on the waveform after the wavelet transformation, and solving a wavelet transformation energy value through the differential transformation;
obtaining energy peak values under each scale and summing the energy peak values to obtain total energy values of different defect types; and performing curve fitting on the total energy value to obtain standard values of different defect types.
Obtaining the value of the wavelet transformation energy, as follows
E(a,b)＝∫|W(a,b)| 2 da；
W (a, b) is a wavelet transform coefficient, E (a, b) is a scale wavelet energy spectrum, and the change of signal energy along with scales is reflected. The wavelet energy value is solved, the selection of wavelet basis is important, the Daubechies wavelet with good time-frequency localization characteristics is selected, the support length of the wavelet function and the scale function is 2*N-1, the number of vanishing moments is N, and the method is very suitable for calculating the wavelet energy. Therefore, the energy peak value under each scale can be obtained by performing calculation according to the formula, the total energy values of different defect types are obtained after summation, and the energy values are subjected to curve fitting to obtain the standard values of the different defect types. According to the idea, 5mm defect data is fitted, and a fitting curve is shown in FIG. 6. The horizontal axis represents the distance from the outermost surface, and the vertical axis represents the sum of energy values after wavelet transformation at different scales.
In fig. 6, four black dots represent three types of defects, the uppermost is an outer layer defect, the middle is an inner layer defect, and the last two dots represent an intermediate layer defect. The defect energy value of the inner layer is larger than that of the middle layer, because the magnetic field distribution of the defect of the inner layer is more uniform, and the change curve of the defect of the inner layer can be more prominent after wavelet transformation. So that its energy value is larger.
The energy determination is only one aspect, and the 3mm outer layer crack value may not be energetically different from the 5mm inner layer energy value, for which further determination is required. After the waveforms of the defects at different positions of the radius are observed, the defects at the innermost layer are relatively smooth, the defects at the middle layer are somewhat jumped, and the defects at the outer layer are located in the middle layer.
The climbing rope defect signal is generally a higher-frequency signal and is often represented by an abrupt waveform, and the detection of the abrupt waveform by the wavelet transformation has unique advantages. Wavelet analysis is an important development of fourier analysis, and has the characteristics of fast attenuation, sufficient smoothness and energy mainly concentrated in a local area. And the multi-scale analysis of continuous wavelet transform has great advantages for detecting defect target signals. It can decompose the signal into a series of different frequency bands, and the energy of the wavelet transform represents different characteristics of the signal in the frequency band.
The foregoing shows and describes the general principles and broad features of the present invention and advantages thereof. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are given by way of illustration of the principles of the present invention, but that various changes and modifications may be made without departing from the spirit and scope of the invention, and such changes and modifications are within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (9)
1. A method for detecting damage of a bridge cable by a wavelet transform algorithm micro-magnetic detector is characterized by comprising the following steps:
s10, placing the magnetic sensor device on a bridge cable, and enabling a detection end to surround the periphery of the bridge cable;
s20, sending an instruction through a detection terminal to control the magnetic sensing device to operate on the bridge cable;
s30, collecting magnetic signals near the cable through a magnetic sensing device;
and S40, analyzing the collected cable magnetic leakage signals through a wavelet transform algorithm to obtain the positions of the defects of the steel wires in the cable.
2. The method for detecting damage to a bridge cable by using a wavelet transform algorithm micro-magnetic detector as claimed in claim 1, wherein in the step S30, the magnetic signals near the cable are collected by using a magnetic sensor, comprising the steps of:
s301, a climbing mechanism of a magnetic sensing device is positioned at one point of a bridge cable, and magnetic induction data of the point is obtained through a magnetic detection mechanism of the magnetic sensing device;
s302, after the magnetic induction data of the point location is completely fed back, the climbing mechanism drives the magnetic detection mechanism to enter the point location of the next detection area, and the magnetic induction data of the next area is fed back;
and S303, repeating the steps S301 and S302 to complete the detection of the whole bridge cable and acquire the magnetic signals of the whole bridge cable.
3. The method for detecting the damage of the bridge cable of the micro-magnetic detector based on the wavelet transform algorithm as claimed in claim 2, wherein the magnetic induction data acquisition process comprises the steps of: and applying an excitation current through the magnetic induction sensor, and returning a signal through the change of the induced electromotive force of the induction coil to acquire magnetic induction data.
4. The method for detecting the damage of the bridge cable by the wavelet transform algorithm micro-magnetic detector according to claim 1, wherein in the step S40, the positions of the defects of the steel wire in the cable are obtained by analyzing the collected cable leakage magnetic signals by the wavelet transform algorithm, and the method comprises the following steps:
s401, collecting magnetic signals by a magnetic sensor array of the magnetic sensor device, judging the condition of signal change, indicating that no defect exists under the condition of no signal change, and indicating that the defect exists under the condition of signal change;
s402, under the condition that the defects exist in the S401, the positions where the defects are located are distinguished through the size of signal transformation amplitude values by using a wavelet transformation method, wherein the highest position of the amplitude values is the outermost layer of the defects;
and S403, according to the change of other amplitudes, utilizing the monotonicity number and the wavelet energy to distinguish through the number and the energy, wherein the number is small, the energy is small, the number is an intermediate layer, and the number is large, and the energy is large, the intermediate layer is arranged.
5. The method for detecting bridge cable damage of the micro-magnetic detector adopting the wavelet transform algorithm as claimed in claim 4, wherein after the magnetic sensor array of the magnetic sensor device collects magnetic signals, the signals of multiple paths of sensors are directly accumulated and averaged on the basis of removing direct current to obtain a synthesized and enhanced signal, and then the signal is analyzed.
6. The method for detecting the damage of the bridge cable of the micro-magnetic detector based on the wavelet transform algorithm as claimed in claim 4, wherein a signal-to-noise ratio is used for detecting a defect signal, and when the signal-to-noise ratio exceeds a set threshold value, the defect signal can be judged to be defective;
the signal-to-noise ratio is calculated according to the following rule: the signal size is the maximum value of the frame data, the noise bottom is selected from the number of points which are equally spaced before and after the position of the maximum value of the signal, and l/2 points are respectively selected for summation and averaging.
7. The wavelet transform algorithm micro-magnetic detector bridge cable damage detection method according to claim 4, characterized in that a smooth Gaussian function with low-pass property is adopted, and amplitude transform analysis is performed according to a first derivative of the smooth Gaussian function as a wavelet basis function to find a catastrophe point corresponding to a signal.
8. The method for detecting the damage of the bridge cable of the micro-magnetic detector based on the wavelet transform algorithm as claimed in claim 7, wherein a first derivative of a smooth Gaussian function is used as a wavelet basis function, a waveform after the wavelet transform is subjected to differential transform, and a wavelet transform energy conversion value is obtained through the differential transform;
obtaining energy peak values under each scale and summing the energy peak values to obtain total energy values of different defect types; and performing curve fitting on the total energy value to obtain standard values of different defect types.
9. The method for detecting the damage of the bridge cable by the micro-magnetic detector based on the wavelet transform algorithm is characterized in that the magnetic sensing device comprises a supporting main body (1), a climbing mechanism (2) and a magnetic detection mechanism (3), wherein the video monitoring mechanism (4) is arranged on the magnetic sensing device, the supporting main body (1) is of a cylindrical structure, the climbing mechanism (2) is arranged at two ends of the supporting main body (1) in a surrounding manner, the magnetic detection mechanism (3) is arranged on the middle section of the supporting main body (1) in a surrounding manner, and the video monitoring mechanism (4) is arranged on the upper end of the supporting main body; the support main body (1) adopts an openable and closable cylindrical structure, and a bridge cable (5) to be detected penetrates through a central shaft of the support main body (1); the climbing mechanism (2) comprises a plurality of climbing subunits which correspond to each other, are locked in the climbing subunits and move along the bridge cable; the magnetic detection mechanism (3) comprises a plurality of magnetic detection sub-units which surround the bridge cable (4) to be detected, and the detection end of the magnetic detection mechanism (3) faces the bridge cable at the center; the detection data of the magnetic detection mechanism (3) are collected and analyzed by the central controller and then are wirelessly transmitted to the detection terminal; meanwhile, the detection terminal wirelessly sends a control instruction to control the climbing mechanism (2) to operate; the video monitoring mechanism (4) comprises four high-definition cameras which are arranged at four corners of the upper end of the supporting main body in a square symmetrical arrangement mode, and the cable is subjected to 360-degree visual monitoring.
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| CN117849516A (en) * | 2024-03-07 | 2024-04-09 | 陕西明珠电力产业服务有限公司 | Transformer fault monitoring device and monitoring method thereof |
| CN117849516B (en) * | 2024-03-07 | 2024-05-31 | 陕西明珠电力产业服务有限公司 | Transformer fault monitoring device and monitoring method thereof |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117849516A (en) * | 2024-03-07 | 2024-04-09 | 陕西明珠电力产业服务有限公司 | Transformer fault monitoring device and monitoring method thereof |
| CN117849516B (en) * | 2024-03-07 | 2024-05-31 | 陕西明珠电力产业服务有限公司 | Transformer fault monitoring device and monitoring method thereof |
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