Novel steel bar corrosion magnetic flaw detection device and method
By integrating a novel magnetic flaw detection device for steel reinforcement corrosion with a data processing platform and a smart algorithm, the problem of non-destructive testing of steel reinforcement corrosion inside concrete has been solved, achieving high-sensitivity and low-power non-destructive testing, which is applicable to bridges, towers, and buildings.
Patent Information
- Application Number
- CN202511245329.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-09-02
- Publication Date
- 2025-12-12
AI Technical Summary
Existing technologies are difficult to accurately detect the corrosion status of steel bars inside concrete without damaging the concrete structure, and the operation is complex and lacks operability.
A novel magnetic flaw detection device for steel reinforcement corrosion is adopted, which uses a cobalt-based amorphous wire sensor to detect weak magnetic field signals inside reinforced concrete. Combined with the fusion intelligent algorithm of the data processing platform, data analysis is performed to achieve non-destructive testing.
It enables non-destructive and harmless detection of steel corrosion inside reinforced concrete, providing data on the degree and location of corrosion. It features high detection sensitivity, low power consumption, small size, and simple and reliable operation.
Smart Images
Figure CN121114197A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of steel reinforcement corrosion detection technology, specifically a novel magnetic flaw detection device and method for steel reinforcement corrosion. Background Technology
[0002] In construction engineering, the reinforcing steel bars inside concrete are one of the main load-bearing structural materials, and their damage has a significant impact on the safety and durability of buildings. Over time, wind, sun, and rain can cause corrosion damage to the reinforcing steel bars inside reinforced concrete. This damage significantly affects the strength of the reinforced concrete, directly leading to a shortened lifespan and safety issues. Therefore, accurately detecting the corrosion state of the reinforcing steel bars without damaging the concrete structure has become an important problem. Currently, commonly used methods in the field of non-destructive testing of reinforcing steel corrosion include: visual inspection, manual tapping, ultrasonic testing, electrochemical corrosion testing, magnetic detection, acoustic emission detection, and infrared thermography.
[0003] Although there are various non-destructive testing methods for steel reinforcement corrosion, some current methods are difficult to effectively detect corrosion hidden inside concrete, while others require professional skills and experience and lack operability in practical applications. They are far from meeting the requirements for non-destructive testing of steel reinforcement in reinforced concrete. Therefore, there is an urgent need for a simple, practical and reliable testing method. Summary of the Invention
[0004] The purpose of this invention is to provide a novel magnetic flaw detection device for steel reinforcement corrosion, so as to solve the problems mentioned in the background art.
[0005] To achieve the above objectives, one aspect of the present invention provides a novel magnetic flaw detection device for steel reinforcement corrosion, comprising: A data processing platform is used for analyzing and processing magnetic detection data; A detection unit is used to detect corrosion damage to the reinforcing bars inside reinforced concrete in real time. The PoE power supply unit connects the data processing platform and the detection unit. The PoE power supply unit is used to supply power to the detection unit and transmit the magnetic detection data of the detection unit to the data processing platform.
[0006] Preferably, the detection unit includes: A base on which multiple cobalt-based amorphous wire sensors are arranged along the length direction, and a data acquisition board connected to the multiple cobalt-based amorphous wire sensors is also installed on the base. An aluminum alloy housing is fixedly connected to the top of the base, and the plurality of cobalt-based amorphous wire sensors and the acquisition board are located inside the aluminum alloy housing; A handle is provided on the top of the base.
[0007] Preferably, the base has a front support wheel at its front end, a rear support wheel at its rear end, and guide wheels at its four corners.
[0008] Preferably, a stroke encoder is fixedly connected to the aluminum alloy housing, and the stroke encoder has a synchronous pulley.
[0009] Another aspect of the present invention provides a novel magnetic testing method for steel reinforcement corrosion, applicable to the novel magnetic testing device for steel reinforcement corrosion described above, comprising the following steps: S1. The operator holds the handle and pushes the detection unit at a constant speed along the surface of the reinforced concrete. The cobalt-based amorphous wire sensor inside the aluminum alloy shell detects the weak magnetic field signal of the steel bars inside the reinforced concrete. The synchronous wheel of the stroke encoder is pressed against the surface of the reinforced concrete by a spring. During the movement of the detection unit, the synchronous wheel rotates synchronously at the same time. S2. The magnetic detection data detected by the detection unit is sent to the data processing platform in real time through the PoE power supply unit. The data processing platform uses a fusion intelligent algorithm to analyze and process the magnetic detection data. When steel corrosion is detected, the data processing platform sends a corrosion defect signal. The data processing platform can observe the corrosion of the steel inside the reinforced concrete and record the location information of the corrosion. S3. The data processing platform records and stores the magnetic detection data. When playback and analysis are needed, the stored data file is reloaded onto the screen for operator analysis and processing.
[0010] Preferably, the fusion intelligent algorithm specifically includes: S101, Denoising the original data; S102. Process the magnetic detection data in segments and obtain a frame of data. Select the signal value of the channel with the largest signal peak and the signal values of the two adjacent cobalt-based amorphous wire sensors for cumulative smoothing. S103. Perform continuous wavelet transform on the signal after cumulative smoothing. S104. For the signal after continuous wavelet transform, the signal-to-noise ratio is used to determine the corrosion defect signal.
[0011] Preferably, step S101 specifically includes: Based on the phase consistency of each cobalt-based amorphous wire sensor, the signals from multiple cobalt-based amorphous wire sensors are directly accumulated and averaged after DC removal to obtain the synthesized and enhanced signal, as shown in the following formula: (1); In equation (1), This indicates the synthesized and enhanced signal.M Indicates the number of sensor channels. m Indicates the first m Road sensor, t Indicates time, Indicates the first m Road sensor in time t The weak magnetic field signal of the steel reinforcement.
[0012] Preferably, step S103 specifically includes: The continuous wavelet transform definition for weak magnetic field signals from reinforcing bars is given below: (2); Equation (2) represents the convolution operation between the weak magnetic field signal of the steel bar and the wavelet function, where Describe the wavelet basis functions. a The scaling factor determines the size of the time-frequency window and its position in the frequency domain. b This represents the displacement factor, which determines the position of the time-frequency window in the time domain; A smooth Gaussian function with low-pass properties is used as the wavelet function, and its first derivative is used as the wavelet basis function to perform continuous wavelet transform on the signal. The first derivative wavelet basis function is as follows: (3); Substituting equation (3) into equation (2) to perform continuous wavelet transform, and selecting a scale factor... a After the value is obtained, the signal value after wavelet transform is obtained. xx ( n ), n Represents a point in the time domain.
[0013] Preferably, step S104 specifically includes: The signal-to-noise ratio (SNR) is calculated according to the following rules: the signal magnitude is taken as the peak value of the signal in this frame of data, and the floor noise is selected at a certain interval before and after the peak value. l Then, each took The result is obtained by summing and averaging the points, using the following formula: (4); In equation (4), Indicates the base noise; k Indicates the location of the signal peak; N Indicates the number of interval points. N The value should be selected based on the number of valid signal points. l Indicates the number of base noise points. l The value is obtained based on experience; The formula for calculating the signal-to-noise ratio is as follows: (5); In equation (5), Indicates the signal-to-noise ratio. This indicates the peak signal value in this frame of data; The formula for calculating the threshold is as follows: (6); In equation (6), Indicates the threshold for judgment. This represents the signal value after a defect-free continuous wavelet transform. Indicates the length of this frame of data. c Indicates coefficient; When the signal-to-noise ratio Greater than the judgment threshold When the data processing platform detects that there is steel corrosion at the detection location corresponding to the peak point of the data signal in this frame, it issues a corrosion defect signal.
[0014] Compared with the prior art, the beneficial effects of the present invention are: The present invention provides a novel magnetic flaw detection device for steel reinforcement corrosion. The detection unit is based on the giant magnetoresistance effect and performs non-destructive testing of steel reinforcement corrosion in reinforced concrete. It can be widely used in the non-destructive testing of steel reinforcement in reinforced concrete in bridges, towers, buildings and other fields. It can provide magnetic detection anomaly data and waveforms of steel reinforcement corrosion. Through the fusion intelligent algorithm of the data processing platform, the magnetic detection data is analyzed and processed to provide data such as the degree of corrosion and the location of corrosion of the steel reinforcement. Because the cobalt-based amorphous wire sensor in this novel magnetic testing device for steel reinforcement corrosion has extremely high sensitivity, it can detect very weak magnetic signals. Therefore, this detection method is completely passive, requiring no external magnetic field for magnetization or any pretreatment such as purification of the tested material; it can be used directly for detection. The cobalt-based amorphous wire sensor is also very small in size and consumes very little power, resulting in a novel magnetic testing device for steel reinforcement corrosion that is both small in size and consumes very little power. Attached Figure Description
[0015] Figure 1 A perspective view of a novel magnetic flaw detection device for steel bar corrosion provided by the present invention; Figure 2 A top view of a novel magnetic flaw detection device for steel bar corrosion provided by the present invention; Figure 3 A flowchart of a novel magnetic flaw detection method for steel bar corrosion provided by the present invention; Figure 4 The flowchart shows the integration of intelligent algorithms in a novel magnetic flaw detection method for steel reinforcement corrosion provided by this invention.
[0016] In the diagram: 1. Base; 2. Aluminum alloy shell; 3. Handle; 4. Front support wheel; 5. Rear support wheel; 6. Guide wheel; 7. Stroke encoder; 8. Synchronizing wheel. Detailed Implementation
[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0018] Figure 1 This is a perspective view of a novel magnetic flaw detection device for steel reinforcement corrosion provided by the present invention. Figure 2 This is a top view of a novel magnetic flaw detection device for steel reinforcement corrosion provided by the present invention. Figure 1 and Figure 2 As shown, an embodiment of the present invention provides a novel magnetic flaw detection device for steel reinforcement corrosion, comprising: A data processing platform is used for analyzing and processing magnetic detection data; A detection unit is used to detect corrosion damage to the reinforcing bars inside reinforced concrete in real time. The PoE power supply unit connects the data processing platform and the detection unit. The PoE power supply unit is used to supply power to the detection unit and transmit the magnetic detection data of the detection unit to the data processing platform.
[0019] The data processing platform can be a tablet computer or a data center backend. It is the operating platform that loads data processing and analysis software and is responsible for all data processing and analysis. The PoE power supply unit is responsible for powering the detection unit. It connects the data processing platform and the detection unit via a network cable and supplies power to the detection unit via the network cable.
[0020] Specifically, in one embodiment of the present invention, such as Figure 1 and Figure 2 As shown, the detection unit includes: A base 1, on which multiple cobalt-based amorphous wire sensors are arranged along the length direction, and a data acquisition board connected to the multiple cobalt-based amorphous wire sensors is also installed on the base 1. An aluminum alloy housing 2 is fixedly connected to the top of the base 1, and the plurality of cobalt-based amorphous wire sensors and the acquisition board are located inside the aluminum alloy housing 2. Handle 3 is disposed on the top of the base 1.
[0021] Furthermore, the base 1 has a front support wheel 4 at its front end, a rear support wheel 5 at its rear end, and guide wheels 6 at its four corners. A stroke encoder 7 is fixedly connected to the aluminum alloy housing 2, and the stroke encoder 7 has a synchronous pulley 8.
[0022] Figure 3 A flowchart illustrating a novel magnetic flaw detection method for steel reinforcement corrosion provided by this invention. Figure 3 As shown, the embodiments of the present invention also provide a novel magnetic testing method for steel reinforcement corrosion, applicable to the novel magnetic testing device for steel reinforcement corrosion described above, comprising the following steps: S1. The operator holds the handle 3 and pushes the detection unit at a constant speed along the surface of the reinforced concrete. The cobalt-based amorphous wire sensor inside the aluminum alloy shell 2 detects the weak magnetic field signal of the steel bars inside the reinforced concrete. The synchronous wheel 8 of the stroke encoder 7 is pressed against the surface of the reinforced concrete by a spring. During the movement of the detection unit, the synchronous wheel 8 rotates synchronously at the same time. S2. The magnetic detection data detected by the detection unit is sent to the data processing platform in real time through the PoE power supply unit. The data processing platform uses a fusion intelligent algorithm to analyze and process the magnetic detection data. When steel corrosion is detected, the data processing platform sends a corrosion defect signal. The data processing platform can observe the corrosion of the steel inside the reinforced concrete and record the location information of the corrosion. S3. The data processing platform records and stores the magnetic detection data. When playback and analysis are needed, the stored data file is reloaded onto the screen for operator analysis and processing.
[0023] During the movement of the detection unit, the synchronous wheel 8 drives the coaxial stroke encoder 7 to count synchronously, thereby recording the distance traveled by the detection unit; the magnetic detection data is sent to the data processing platform in real time, and a corrosion defect signal is emitted when steel corrosion is detected. In this way, the corrosion of the steel inside the reinforced concrete can be observed intuitively from the data processing platform. The lateral position of the corrosion can be determined from which cobalt-based amorphous wire sensor the corrosion defect signal comes from, and the longitudinal position can be determined from the synchronous count of the stroke encoder 7, thus recording the location information of the corrosion occurrence.
[0024] Figure 4 This invention provides a flowchart of a novel magnetic testing method for steel reinforcement corrosion that incorporates an intelligent algorithm. Figure 4 As shown, in one embodiment of the present invention, the fusion intelligent algorithm specifically includes: S101, Denoising the original data; S102. Process the magnetic detection data in segments and obtain a frame of data. Select the signal value of the channel with the largest signal peak and the signal values of the two adjacent cobalt-based amorphous wire sensors for cumulative smoothing. A frame of data can be 256 points or 512 points, depending on the actual situation. S103. Perform continuous wavelet transform on the signal after cumulative smoothing. S104. For the signal after continuous wavelet transform, the signal-to-noise ratio is used to determine the corrosion defect signal.
[0025] Specifically, in one embodiment of the present invention, step S101 specifically includes: Based on the phase consistency of each cobalt-based amorphous wire sensor, the signals from multiple cobalt-based amorphous wire sensors are directly accumulated and averaged after DC removal to obtain the synthesized and enhanced signal, as shown in the following formula: (1); In equation (1), This indicates the synthesized and enhanced signal. M Indicates the number of sensor channels. m Indicates the first m Road sensor, t Indicates time, Indicates the first m Road sensor in time t The weak magnetic field signal of the steel reinforcement.
[0026] In one embodiment of the present invention, step S103 specifically includes: The continuous wavelet transform definition for weak magnetic field signals from reinforcing bars is given below: (2); Equation (2) represents the convolution operation between the weak magnetic field signal of the steel bar and the wavelet function, where Describe the wavelet basis functions. a The scaling factor determines the size of the time-frequency window and its position in the frequency domain. b This represents the displacement factor, which determines the position of the time-frequency window in the time domain; Since the first derivative of the wavelet function is very sensitive to abrupt changes in the signal, it is chosen as the wavelet basis function. A smooth Gaussian function with low-pass properties is used as the wavelet function, and its first derivative is used as the wavelet basis function to perform continuous wavelet transform on the signal. The first derivative wavelet basis function is as follows: (3); Substituting equation (3) into equation (2) to perform continuous wavelet transform, and selecting a scale factor... a After the value is obtained, the signal value after wavelet transform is obtained. xx ( n ),n This represents a point in the time domain. For corrosion defects, since their frequency is generally low, a large scale factor can be chosen. a For example, take 4 or 5.
[0027] In one embodiment of the present invention, step S104 specifically includes: The signal-to-noise ratio (SNR) is calculated according to the following rules: the signal magnitude is taken as the peak value of the signal in this frame of data, and the floor noise is selected at a certain interval before and after the peak value. l Then, each took The result is obtained by summing and averaging the points, using the following formula: (4); In equation (4), Indicates the base noise; k Indicates the location of the signal peak; N Indicates the number of interval points. N The value should be selected based on the number of valid signal points. l Indicates the number of base noise points. l The value is obtained based on experience; The formula for calculating the signal-to-noise ratio is as follows: (5); In equation (5), Indicates the signal-to-noise ratio. This indicates the peak signal value in this frame of data; The threshold can be calculated by first taking a segment of defect-free signal data, calculating its basis noise, and multiplying it by a coefficient of 3 to 5 as the threshold. The specific calculation formula is as follows: (6); In equation (6), Indicates the threshold for judgment. This represents the signal value after a defect-free continuous wavelet transform. Indicates the length of this frame of data. c This represents a coefficient, with values ranging from 3 to 5; When the signal-to-noise ratio Greater than the judgment threshold When the data processing platform detects that there is steel corrosion at the detection location corresponding to the peak point of the data signal in this frame, it issues a corrosion defect signal.
[0028] This invention provides a novel magnetic flaw detection device for reinforcing steel corrosion. The detection unit is based on the giant magnetoresistance effect, enabling real-time detection of corrosion damage to reinforcing steel within reinforced concrete. This process is non-destructive, requiring no damage to the concrete surface and no excitation magnetic field. It relies solely on a cobalt-based amorphous wire sensor to detect weak leakage magnetic field signals, making it a completely passive detection method. It features low power consumption, high sensitivity, high accuracy, and good repeatability. It is also cost-effective and can be widely applied to the detection of reinforcing steel corrosion in various reinforced concrete structures. The device provides magnetic detection anomaly data and waveforms of reinforcing steel corrosion. Through a data processing platform and its integrated intelligent algorithm, the magnetic detection data is analyzed and processed to provide information such as the degree and location of corrosion. Because the cobalt-based amorphous wire sensor in this novel magnetic testing device for steel reinforcement corrosion has extremely high sensitivity, it can detect very weak magnetic signals. Therefore, this detection method is completely passive, requiring no external magnetic field for magnetization or any pretreatment such as purification of the tested material; it can be used directly for detection. The cobalt-based amorphous wire sensor is also very small in size and consumes very little power, resulting in a novel magnetic testing device for steel reinforcement corrosion that is both small in size and consumes very little power.
[0029] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.
Claims
1. A novel magnetic flaw detection device for steel reinforcement corrosion, characterized in that, include: A data processing platform is used for analyzing and processing magnetic detection data; A detection unit is used to detect corrosion damage to the reinforcing bars inside reinforced concrete in real time. The PoE power supply unit connects the data processing platform and the detection unit. The PoE power supply unit is used to supply power to the detection unit and transmit the magnetic detection data of the detection unit to the data processing platform.
2. The novel magnetic flaw detection device for steel reinforcement corrosion according to claim 1, characterized in that, The detection unit includes: A base (1) is provided with a plurality of cobalt-based amorphous wire sensors arranged along the length direction on the base (1), and a data acquisition board connected to the plurality of cobalt-based amorphous wire sensors is also installed on the base (1). An aluminum alloy housing (2) is fixedly connected to the top of the base (1), and the plurality of cobalt-based amorphous wire sensors and the acquisition board are located inside the aluminum alloy housing (2). A handle (3) is disposed on the top of the base (1).
3. The novel magnetic flaw detection device for steel reinforcement corrosion according to claim 2, characterized in that, The base (1) is provided with a front support wheel (4) at the front end, a rear support wheel (5) at the rear end, and guide wheels (6) at the four corners of the base (1).
4. The novel magnetic flaw detection device for steel reinforcement corrosion according to claim 3, characterized in that, The aluminum alloy housing (2) is fixedly connected to a stroke encoder (7), which has a synchronous pulley (8).
5. A novel magnetic flaw detection method for steel reinforcement corrosion, characterized in that, The novel magnetic flaw detection device for steel reinforcement corrosion according to claim 4 includes the following steps: S1. The operator holds the handle (3) and pushes the detection unit at a constant speed along the surface of the reinforced concrete. The cobalt-based amorphous wire sensor inside the aluminum alloy shell (2) detects the weak magnetic field signal of the steel bars inside the reinforced concrete. The synchronous wheel (8) of the stroke encoder (7) is pressed against the surface of the reinforced concrete by the spring. During the movement of the detection unit, the synchronous wheel (8) rotates synchronously at the same time. S2. The magnetic detection data detected by the detection unit is sent to the data processing platform in real time through the PoE power supply unit. The data processing platform uses a fusion intelligent algorithm to analyze and process the magnetic detection data. When steel corrosion is detected, the data processing platform sends a corrosion defect signal. The data processing platform can observe the corrosion of the steel inside the reinforced concrete and record the location information of the corrosion. S3. The data processing platform records and stores the magnetic detection data. When playback and analysis are needed, the stored data file is reloaded onto the screen for operator analysis and processing.
6. A novel magnetic testing method for steel reinforcement corrosion according to claim 5, characterized in that, The fusion intelligent algorithm specifically includes: S101, Denoising the raw data; S102. Process the magnetic detection data in segments and obtain a frame of data. Select the signal value of the channel with the largest signal peak and the signal values of the two adjacent cobalt-based amorphous wire sensors for cumulative smoothing. S103. Perform continuous wavelet transform on the signal after cumulative smoothing. S104. For the signal after continuous wavelet transform, the signal-to-noise ratio is used to determine the corrosion defect signal.
7. A novel magnetic testing method for steel reinforcement corrosion according to claim 6, characterized in that, Step S101 specifically includes: Based on the phase consistency of each cobalt-based amorphous wire sensor, the signals from multiple cobalt-based amorphous wire sensors are directly accumulated and averaged after DC removal to obtain the synthesized and enhanced signal, as shown in the following formula: （1）； In equation (1), This indicates the synthesized and enhanced signal. M Indicates the number of sensor channels. m Indicates the first m Road sensor, t Indicates time, Indicates the first m Road sensor in time t The weak magnetic field signal of the steel reinforcement.
8. A novel magnetic testing method for steel reinforcement corrosion according to claim 6, characterized in that, Step S103 specifically includes: The continuous wavelet transform definition for weak magnetic field signals from reinforcing bars is given below: （2）； Equation (2) represents the convolution operation between the weak magnetic field signal of the steel bar and the wavelet function, where Describe the wavelet basis functions. a The scaling factor determines the size of the time-frequency window and its position in the frequency domain. b This represents the displacement factor, which determines the position of the time-frequency window in the time domain; A smooth Gaussian function with low-pass properties is used as the wavelet function, and its first derivative is used as the wavelet basis function to perform continuous wavelet transform on the signal. The first derivative wavelet basis function is as follows: （3）； Substituting equation (3) into equation (2) to perform continuous wavelet transform, and selecting a scale factor... a After the value is obtained, the signal value after wavelet transform is obtained. xx ( n ), n Represents a point in the time domain.
9. A novel magnetic flaw detection method for steel reinforcement corrosion according to claim 6, characterized in that, Step S104 specifically includes: The signal-to-noise ratio (SNR) is calculated according to the following rules: the signal magnitude is taken as the peak value of the signal in this frame of data, and the floor noise is selected at a certain interval before and after the signal peak value. l Then, each took The result is obtained by summing and averaging the points, using the following formula: （4）； In equation (4), Indicates the base noise; k Indicates the location of the signal peak; N Indicates the number of interval points. N The value should be selected based on the number of valid signal points. l Indicates the number of base noise points. l The value is obtained based on experience; The formula for calculating the signal-to-noise ratio is as follows: （5）； In equation (5), Indicates the signal-to-noise ratio. This indicates the peak signal value in this frame of data; The formula for calculating the threshold is as follows: （6）； In equation (6), Indicates the threshold for judgment. This represents the signal value after a defect-free continuous wavelet transform. Indicates the length of this frame of data. c Indicates coefficient; When the signal-to-noise ratio Greater than the judgment threshold When the data processing platform detects that there is steel corrosion at the detection location corresponding to the peak point of the data signal in this frame, it issues a corrosion defect signal.