A bridge cable damage detection method based on wavelet transform algorithm and micromagnetic detector

Abstract

The present invention discloses a method for detecting bridge cable damage using a wavelet transform algorithm micromagnetic detector, comprising the steps of: S10, placing a magnetic sensor device on the bridge cable, with the detection end surrounding the outer periphery of the bridge cable; S20, sending instructions through a detection terminal to control the magnetic sensor device to run on the bridge cable; S30, collecting magnetic signals near the cable through the magnetic sensor device; S40, analyzing the collected cable leakage magnetic signals through a wavelet transform algorithm to obtain the position of the steel wire defect inside the cable. The present invention can detect the defect position of the high-strength steel wire or steel strand inside the cable through wavelet transform.

Description

Technical Field

[0001] The invention belongs to the technical field of bridge cable detection, and in particular relates to a bridge cable damage detection method using a wavelet transform algorithm micromagnetic detector. Background Art

[0002] At present, the inspection and protection of bridge cables include the inspection of internal steel wire structure and the inspection of external protective layer. The inspection of internal steel wire structure mainly targets the fatigue damage caused by the deformation of internal steel wire of the cable (including bridge load, wind and other vibrations), the change of metal cross-sectional area and water mist erosion. The inspection methods include cable force inspection method, eddy current inspection method, magnetic flux leakage inspection method, optical fiber inspection and so on.

[0003] Although a variety of cable-climbing robots have been developed, the existing technology makes it difficult to detect the degree of internal corrosion damage of cable components. The detection technology of cable components still needs to be further improved, which has become a huge problem that plagues bridge workers around the world. It is impossible to effectively identify and locate defects, the positioning accuracy is poor, and the identification accuracy is poor. Summary of the invention

[0004] In order to solve the above problems, the present invention proposes a bridge cable damage detection method using a wavelet transform algorithm and a micromagnetic detector. Based on the principle of magnetic leakage detection, the defect position of the high-strength steel wire or steel strand inside the cable can be detected through wavelet transform.

[0005] To achieve the above object, the technical solution adopted by the present invention is: a method for detecting bridge cable damage using a wavelet transform algorithm micromagnetic detector, comprising the steps of:

[0006] S10, placing the magnetic sensor device on the bridge cable, with the detection end surrounding the outer periphery of the bridge cable;

[0007] S20, sending instructions through the detection terminal to control the magnetic sensor device to operate on the bridge cable;

[0008] S30, collecting magnetic signals near the cable through a magnetic sensor device;

[0009] S40, analyzing the collected cable magnetic leakage signal by wavelet transform algorithm to obtain the position of the steel wire defect inside the cable.

[0010] Furthermore, in the step S30, collecting magnetic signals near the cable by means of a magnetic sensor comprises the following steps:

[0011] S301, the climbing mechanism of the magnetic sensor device is positioned at a point of the bridge cable, and the magnetic induction data of the point is obtained by the magnetic detection mechanism of the magnetic sensor device;

[0012] S302, after the magnetic induction data of the point is fed back, the climbing mechanism drives the magnetic detection mechanism to enter the next detection area point, and feeds back the magnetic induction data of the next area;

[0013] S303, by repeating steps S301 and S302, the detection of the entire bridge cable is completed and the magnetic signal of the entire bridge cable is obtained.

[0014] Furthermore, the process of acquiring the magnetic induction data includes the steps of applying an excitation current through a magnetic induction sensor, returning a signal through a change in the induced electromotive force of the induction coil, and acquiring the magnetic induction data.

[0015] Furthermore, in the step S40, the collected cable magnetic leakage signal is analyzed by wavelet transform algorithm to obtain the position of the steel wire defect inside the cable, including the steps of:

[0016] S401, the magnetic sensor array of the magnetic sensor device collects magnetic signals and determines the change of the signals. If there is no signal change, it indicates that there is no defect, and if there is a signal change, it indicates that there is a defect;

[0017] S402, in the case of defects as described in S401, using a wavelet transform method, distinguishing the location of the defects by the magnitude of the signal transformation amplitude, and the location with the highest amplitude is the outermost defect;

[0018] S403, according to the change of other amplitudes, using the number of monotonicity and wavelet energy, distinguishing by number and energy, a small number and small energy is the middle layer, and a large number and large energy is the inner layer.

[0019] Furthermore, after the magnetic sensor array of the magnetic sensor device collects the magnetic signals, the multi-channel sensor signals are directly accumulated and averaged on the basis of removing the DC, so as to obtain the synthetically enhanced signal, which is then analyzed.

[0020] Furthermore, the signal-to-noise ratio is used to detect defective signals, and when the signal-to-noise ratio exceeds a set threshold, it can be determined as defective;

[0021] The signal-to-noise ratio is calculated according to the following rules: the signal size takes the maximum value of the frame data, the noise floor is selected at equally spaced points before and after the signal maximum position, and then 1 / 2 points are taken for summing and averaging.

[0022] Furthermore, a smooth Gaussian function with a low-pass property is used, and its first-order derivative is used as a wavelet basis function to perform amplitude transformation analysis to find the mutation point corresponding to the signal.

[0023] Furthermore, according to the first-order derivative of the smoothed Gaussian function as the wavelet basis function, the waveform after wavelet transformation is subjected to differential transformation, and the wavelet transformation energy value is obtained by differential transformation;

[0024] The energy peak value at each scale is obtained and summed to obtain the total energy value of different defect types; the total energy value is then curve fitted to obtain the standard value of different defect types.

[0025] The magnetic induction bridge cable detection device comprises a supporting body, a climbing mechanism, a magnetic detection mechanism and a video monitoring mechanism, wherein the supporting body is a columnar structure, climbing mechanisms are symmetrically arranged at both ends of the supporting body, and a micro-magnetic detection mechanism is arranged around the middle section of the supporting body; the supporting body adopts an openable and closable columnar structure, and the bridge cable to be detected passes through the central axis of the supporting body; the climbing mechanism comprises a plurality of climbing sub-units corresponding to each other and the bridge cable is embraced therein and moves along the bridge cable; the magnetic detection mechanism comprises a plurality of magnetic detection sub-units surrounding 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 is summarized and analyzed by the central controller and then transmitted wirelessly to the detection terminal; the video monitoring mechanism comprises four high-definition cameras, which are arranged symmetrically at the four corners of the square at the upper end of the supporting body, and the central bridge cable is monitored 360°, and the monitoring data is analyzed and processed through wireless transmission to the PC end; at the same time, the detection terminal wirelessly sends control instructions to control the operation of the climbing mechanism.

[0026] The beneficial effects of adopting this technical solution are:

[0027] During detection, the steel wire is magnetized, and the magnetic field signal detected by the micromagnetic detector is processed by wavelet analysis to determine the defect condition of the high-strength steel wire or steel strand inside the cable. The defect identification and positioning are obtained through monotonicity and wavelet analysis of the magnetic induction signal and the climbing position. This can solve the problem that the degree of corrosion inside the bridge cable cannot be accurately detected, improve the accuracy of cable monitoring, and accurately locate the corrosion position of the bridge cable.

[0028] The present invention enables the device to climb along the bridge cable by adjusting the climbing structure, and detects the magnetic induction signal through the magnetic detection structure at the same time; by adjusting the climbing structure, the device can accurately provide detection conditions for the magnetic induction device, and can provide accurate detection height, position and detection, and utilizes the magnetic induction signal and the climbing position to analyze the type of bridge cable defects through continuous wavelet transform, thereby completing defect identification and positioning.

[0029] The present invention forms an intelligent detection method for bridge cable defects. The method runs on the bridge cable through a climbing mechanism, obtains the magnetic signal of the bridge cable through a magnetic detection mechanism, completes the detection of the entire bridge cable, obtains the magnetic induction data of the entire bridge cable, and then analyzes the magnetic induction data of the entire bridge cable to obtain the status result of the bridge cable. This can solve the problem that the internal positioning of the bridge cable cannot be accurately detected, can improve the accuracy of cable monitoring, and can accurately locate the internal corrosion position of the bridge cable. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 A schematic flow chart of a method for detecting bridge cable damage using a micromagnetic detector using a wavelet transform algorithm according to the present invention;

[0031] Figure 2 is a structural schematic diagram of a magnetic sensor device in an embodiment of the present invention;

[0032] Figure 3 Schematic diagram of the structure of the defect position in the cable in an embodiment of the present invention;

[0033] Figure 4 Schematic diagram of defect position of detection curve after wavelet transformation in an embodiment of the present invention;

[0034] Figure 5 A comparison diagram of signals before and after enhancement processing in an embodiment of the present invention;

[0035] Figure 6 Schematic diagram of the energy fitting curve in an embodiment of the present invention. DETAILED DESCRIPTION

[0036] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention is further described below with reference to the accompanying drawings.

[0037] In this embodiment, see Figure 1 As shown, the present invention proposes a bridge cable damage detection method using a wavelet transform algorithm micromagnetic detector, comprising the steps of:

[0038] S10, placing the magnetic sensor device on the bridge cable, with the detection end surrounding the outer periphery of the bridge cable;

[0039] S20, sending instructions through the detection terminal to control the magnetic sensor device to operate on the bridge cable;

[0040] S30, collecting magnetic signals near the cable through a magnetic sensor device;

[0041] S40, analyzing the collected cable magnetic leakage signal by wavelet transform algorithm to obtain the position of the steel wire defect inside the cable.

[0042] Among them, Figure 2As shown, the magnetic induction bridge cable detection device includes a support body 1, a climbing mechanism 2, a magnetic detection mechanism 3 and a video monitoring mechanism 4. The support body 1 is a columnar structure, and the climbing mechanisms 2 are symmetrically arranged at both ends of the support body 1, and the magnetic detection mechanism 3 is arranged around the middle section of the support body 1; the support body 1 adopts an openable columnar structure, and the bridge cable 5 to be detected passes through the central axis of the support body 1; the climbing mechanism 2 includes a plurality of climbing sub-units corresponding to each other and the bridge cable is embraced therein, and moves along the bridge cable; the magnetic detection mechanism 3 includes a plurality of magnetic detection sub-units surrounding 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 is wirelessly transmitted to the detection terminal after being summarized and analyzed by the central controller; and the detection terminal wirelessly sends control instructions to control the operation of the climbing mechanism 2. The video monitoring mechanism 4 includes four high-definition cameras, which are arranged according to the four corners of a square, and the detection end faces the bridge cable at the center to monitor the cable 360°, and the obtained video signal is transmitted to the PC end through a wireless network for analysis.

[0043] As an optimization solution of the above embodiment, in step S30, collecting magnetic signals near the cable by means of a magnetic sensor comprises the following steps:

[0044] S301, the climbing mechanism of the magnetic sensor device is positioned at a point of the bridge cable, and the magnetic induction data of the point is obtained by the magnetic detection mechanism of the magnetic sensor device;

[0045] S302, after the magnetic induction data of the point is fed back, the climbing mechanism drives the magnetic detection mechanism to enter the next detection area point, and feeds back the magnetic induction data of the next area;

[0046] S303, by repeating steps S301 and S302, the detection of the entire bridge cable is completed and the magnetic signal of the entire bridge cable is obtained.

[0047] The process of acquiring the magnetic induction data includes the steps of applying an excitation current through a magnetic induction sensor, returning a signal through a change in the induced electromotive force of an induction coil, and acquiring the magnetic induction data.

[0048] As an optimization scheme of the above embodiment, in the step S40, the collected cable magnetic leakage signal is analyzed by wavelet transform algorithm to obtain the position of the steel wire defect inside the cable, including the steps of:

[0049] S401, the magnetic sensor array of the magnetic sensor device collects magnetic signals and determines the change of the signals. If there is no signal change, it indicates that there is no defect, and if there is a signal change, it indicates that there is a defect;

[0050] S402, in the case of defects as described in S401, using a wavelet transform method, distinguishing the location of the defects by the magnitude of the signal transformation amplitude, and the location with the highest amplitude is the outermost defect;

[0051] S403, according to the change of other amplitudes, the number of monotonicity and wavelet energy are used to distinguish by number and energy. The number with small number and small energy is the middle layer, and the number with large number and large energy is the inner layer, such as Figure 3 and Figure 4 shown.

[0052] Among them, after the magnetic sensor array of the magnetic sensor device collects the magnetic signal, the multi-channel sensor signals are directly accumulated and averaged on the basis of DC removal, so as to obtain the synthetic enhanced signal, and then analyze it.

[0053] The multi-channel sensor signals are directly accumulated and averaged on the basis of DC removal. The calculation formula is:

[0054]

[0055] The following is the result of one frame of data before and after accumulation and smoothing. In the simulation, only the two sensors closest to the defect are selected for accumulation processing. It can be seen that after processing, Figure 5 (a) is before treatment, and (b) is after treatment. It can be seen that after treatment, the defect signal is more obvious and the noise is greatly attenuated.

[0056] Preferably, the defect signal is detected by using the signal-to-noise ratio, and when the signal-to-noise ratio exceeds a set threshold, it can be determined as a defect;

[0057] The signal-to-noise ratio is calculated according to the following rules: the signal size takes the maximum value of the frame data, the noise floor is selected at equally spaced points before and after the signal maximum position, and then 1 / 2 points are taken for summing and averaging.

[0058] The mathematical formula is as follows

[0059]

[0060] The N value is selected based on the actual number of valid signal points, and the l value is obtained based on experience and can be set to 16. The ratio of the signal to the noise floor is used as the basis for judgment. Only when it is greater than a certain threshold, it is considered that there is a target signal. The threshold value is obtained based on the actual test data. The actual test data shows that after continuous wavelet transform, the signal-to-noise ratio of the defect signal is generally above 10dB, which can be easily detected.

[0061] Preferably, a smooth Gaussian function with a low-pass property is used, and its first-order derivative is used as a wavelet basis function to perform amplitude transformation analysis to find the mutation point corresponding to the signal.

[0062] , according to the first-order derivative of the smoothed Gaussian function as the wavelet basis function, the waveform after wavelet transformation is differentially transformed, and the wavelet transformation energy value is obtained by differential transformation;

[0063] The energy peak value at each scale is obtained and summed to obtain the total energy value of different defect types; the total energy value is then curve fitted to obtain the standard value of different defect types.

[0064] Calculate the wavelet transform energy value as follows

[0065] E(a,b)=∫|W(a,b)| 2 da;

[0066] W(a,b) is the wavelet transform coefficient, and E(a,b) is the scale wavelet energy spectrum, which reflects how the signal energy changes with scale. To calculate the wavelet energy value, the choice of wavelet basis is very important. Here we choose the Daubechies wavelet with good time-frequency localization characteristics. The support length of its wavelet function and scale function is 2*N-1, and the number of vanishing moments is N, which is very suitable for the calculation of wavelet energy. Therefore, according to the above formula, the energy peak value at each scale can be obtained. After summing, the total energy value of different defect types can be obtained. The energy value is fitted with a curve to obtain the standard value of different defect types. According to this idea, the 5mm defect data is fitted, and the fitting curve is as follows Figure 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.

[0067] Figure 6 The four black dots in the middle represent three types of defects. The top one is the outer layer defect, the middle one is the inner layer defect, and the last two dots represent the middle layer defect. The energy value of the inner layer defect is larger than that of the middle layer defect. This is because the magnetic field distribution of the inner layer defect is more uniform, and the wavelet transform can highlight its change curve more. Therefore, its energy value is larger.

[0068] Judging from the energy is only one aspect. The energy value of the 3mm outer layer crack may not be much different from the 5mm inner layer energy value. Therefore, further judgment is needed. After observing the waveforms of the defects at different radius positions, it is found that the innermost defect is relatively smooth, the middle one has some jumps, and the outer layer defect is in the middle. Using these characteristics, the monotonicity method is used to further judge the defect type.

[0069] The signal of the climbing wire defect is generally a higher frequency signal, which often appears as a sudden change waveform, and the wavelet transform has a unique advantage in detecting sudden change waveforms. Wavelet analysis is an important development of Fourier analysis, with the characteristics of fast decay, full smoothness, and energy mainly concentrated in a local area. The multi-scale analysis of continuous wavelet transform has great advantages in detecting defect target signals. It can decompose the signal into a series of different frequency bands, and the energy of wavelet transform represents the different characteristics of the signal in this frequency band.

[0070] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A method for detecting bridge cable damage using a wavelet transform algorithm micromagnetic detector, characterized in that: Includes steps: S10, placing the magnetic sensor device on the bridge cable, with the detection end surrounding the outer periphery of the bridge cable; S20, sending instructions through the detection terminal to control the magnetic sensor device to operate on the bridge cable; S30, collecting magnetic signals near the cable through a magnetic sensor device; S40, analyzing the collected cable magnetic leakage signal by wavelet transform algorithm to obtain the position of the steel wire defect inside the cable, including the steps of: S401, the magnetic sensor array of the magnetic sensor device collects magnetic signals and determines the change of the signals. If there is no signal change, it indicates that there is no defect, and if there is a signal change, it indicates that there is a defect; S402, in the case of defects as described in S401, using a wavelet transform method, distinguishing the location of the defects by the magnitude of the signal transformation amplitude, and the location with the highest amplitude is the outermost defect; S403, according to the change of other amplitudes, using the number of monotonicity and wavelet energy, distinguishing by number and energy, a small number and small energy is the middle layer, and a large number and large energy is the inner layer; step S403 also includes taking the first-order derivative of the smoothed Gaussian function as the wavelet basis function, performing differential transformation on the waveform after wavelet transformation, and calculating the wavelet transformation energy value by differential transformation; The energy peak value at each scale is obtained and summed to obtain the total energy value of different defect types; Then the total energy value is curve fitted to obtain the standard value of different defect types.

2. According to claim 1, a wavelet transform algorithm micromagnetic detector bridge cable damage detection method is characterized in that: In the step S30, the magnetic signal near the cable is collected by a magnetic sensor, including the steps of: S301, the climbing mechanism of the magnetic sensor device is positioned at a point of the bridge cable, and the magnetic induction data of the point is obtained by the magnetic detection mechanism of the magnetic sensor device; S302, after the magnetic induction data of the point is fed back, the climbing mechanism drives the magnetic detection mechanism to enter the next detection area point, and feeds back the magnetic induction data of the next area; S303, by repeating steps S301 and S302, the detection of the entire bridge cable is completed and the magnetic signal of the entire bridge cable is obtained.

3. The method for detecting bridge cable damage using a wavelet transform algorithm micromagnetic detector according to claim 2 is characterized in that: The process of acquiring magnetic induction data includes the steps of applying an excitation current through a magnetic induction sensor, returning a signal through a change in the induced electromotive force of an induction coil, and acquiring magnetic induction data.

4. The method for detecting bridge cable damage using a wavelet transform algorithm micromagnetic detector according to claim 1 is characterized in that: After the magnetic sensor array of the magnetic sensor device collects the magnetic signals, the multi-channel sensor signals are directly accumulated and averaged on the basis of DC removal, so as to obtain the synthetic enhanced signal, which is then analyzed.

5. The method for detecting bridge cable damage using a wavelet transform algorithm micromagnetic detector according to claim 1 is characterized in that: The signal-to-noise ratio is used to detect defective signals. When the signal-to-noise ratio exceeds the set threshold, it can be judged as defective; The signal-to-noise ratio is calculated according to the following rules: the signal size takes the maximum value of the frame data, the noise floor is selected at equally spaced points before and after the signal maximum position, and then 1 / 2 points are taken for summing and averaging.

6. The method for detecting bridge cable damage using a wavelet transform algorithm micromagnetic detector according to claim 1 is characterized in that: A smooth Gaussian function with low-pass properties is used, and its first-order derivative is used as the wavelet basis function to perform amplitude transformation analysis and find the mutation point corresponding to the signal.

7. The method for detecting bridge cable damage using a wavelet transform algorithm micromagnetic detector according to claim 1 is characterized in that: The magnetic sensing device comprises a supporting body (1), a climbing mechanism (2), a magnetic detection mechanism (3) and a video monitoring mechanism (4); the supporting body (1) is a columnar structure, the climbing mechanism (2) is arranged around both ends of the supporting body (1), the magnetic detection mechanism (3) is arranged around the middle section of the supporting body (1), and the video monitoring mechanism (4) is arranged at the upper end of the supporting body; the supporting body (1) adopts an openable and closable columnar structure, and the bridge cable (5) to be detected passes through the central axis of the supporting body (1); the climbing mechanism (2) comprises a plurality of climbing sub-units corresponding to each other and arranged in a closed manner. The bridge cable is locked therein and moved along the bridge cable; the magnetic detection mechanism (3) includes a plurality of magnetic detection sub-units surrounding the bridge cable (5) to be detected, and the detection end of the magnetic detection mechanism (3) is facing the bridge cable at the center; the detection data of the magnetic detection mechanism (3) is summarized and analyzed by the central controller and then wirelessly transmitted to the detection terminal; at the same time, the detection terminal wirelessly sends control instructions to control the operation of the climbing mechanism (2); the video monitoring mechanism (4) includes four high-definition cameras arranged at the four corners of the upper end of the supporting body, symmetrically arranged in a square, and performs 360-degree visual monitoring of the cable.

Images