A cable material line diameter detection method based on eddy current impedance effect

Abstract

The application discloses a cable material and wire diameter detection method based on an eddy current impedance effect and particularly relates to the field of cable nondestructive detection, and comprises the following steps: S1, a double-frequency excitation eddy current sensor is adopted to synchronously collect the resistance and inductance of the coil under low-frequency signals, the resistance and inductance of the coil under high-frequency signals, and the environmental temperature and the lift-off distance between the sensor and the cable; S2, a combination algorithm is used for denoising, and then the sensor coil resistance, inductance and cable core conductivity are temperature compensated; S3, the material sensitivity factor is calculated under low frequency, the wire diameter sensitivity factor is calculated under high frequency, and the lift-off distance deviation is corrected; S4, the material deviation rate is calculated to determine the material, a linear regression model is fitted, the skin depth is corrected in combination with the conductivity, and the wire diameter is calculated; and S5, the material, wire diameter and comprehensive judgment are progressively output, and corresponding alarms are triggered when an abnormality occurs. The application solves the problems of incomplete parameter coupling and temperature compensation, and improves the detection precision and environmental adaptability.

Description

Technical Field

[0001] This invention relates to the field of nondestructive testing technology for cables, and more specifically, to a method for detecting the wire diameter of cable material based on the eddy current impedance effect. Background Technology

[0002] In the fields of cable production quality control and power grid operation and maintenance testing, non-destructive testing technology based on the eddy current impedance effect has become one of the mainstream technologies for cable material identification and wire diameter measurement due to its advantages such as non-contact operation, no mechanical wear, and fast testing speed. Its core principle is to generate an alternating magnetic field by passing an alternating current through the coil of an eddy current sensor. When the cable (conductive material) enters the magnetic field, eddy currents are induced on its surface. The reverse magnetic field of the eddy currents changes the coil impedance, and the cable characteristics are detected by analyzing the impedance change.

[0003] Currently, most existing eddy current cable testing solutions employ a single-frequency excitation mode. This involves driving a sensor coil with an alternating current of a single frequency and relying on machine learning algorithms such as neural networks and support vector machines to process the impedance signal and extract material and wire diameter information. This type of solution is widely used in identifying aluminum-to-copper substitution in copper / aluminum core cables, online wire diameter monitoring in high-speed wire drawing production lines, and offline sampling inspection of overhead cables and cables in cable wells during power grid maintenance, attempting to meet the industrial sector's need for rapid cable quality verification.

[0004] However, in practical use, it still has some shortcomings. For example, under single-frequency excitation, the change in coil impedance is affected by the cable material, wire diameter, and sensor-cable lift-off distance. It is impossible to distinguish the contribution of each parameter to the impedance change. Especially when the cable wire diameter fluctuates or the lift-off distance changes slightly, problems such as misjudgment of material and wire diameter exceeding tolerance are easy to occur. It is difficult to meet the requirements of GB / T3956-2008 for cable wire diameter Class A accuracy (error ≤ ±0.005mm). Existing technology only compensates for the temperature drift of the sensor coil itself (such as the change of coil wire resistance and skeleton inductance with temperature), but ignores the key change of cable core conductivity with temperature. For example, for every 10°C increase in temperature, the conductivity σ of copper core cable will decrease by about 3.9%, and the conductivity of aluminum core cable will decrease by about 4.29%. This results in the wire diameter measurement error increasing by more than double when the temperature fluctuates by 10°C in industrial settings. It is unsuitable for testing environments with unstable temperatures, such as workshops and outdoors, requiring the construction of additional temperature control devices, which increases testing costs. Most solutions collect ≥1000 sets of samples to train machine learning models to achieve correlation analysis between material and wire diameter. However, the black-box nature of the algorithm makes it impossible to trace the detection error. Once abnormal results occur, it is difficult to determine whether the error is due to parameter settings, hardware failure, or model deviation. At the same time, model training consumes a lot of time and sample resources. When maintaining industrial settings, samples need to be collected and trained again. The calibration time is usually ≥4 hours, resulting in high maintenance costs and an inability to quickly adapt to the testing needs of different cable specifications. Summary of the Invention

[0005] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide a method for detecting the wire diameter of cable material based on the eddy current impedance effect, which solves the problems mentioned in the background art through the following solutions.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a method for detecting the wire diameter of cable material based on the eddy current impedance effect, comprising: S1. Data acquisition: using a dual-frequency excitation eddy current sensor to simultaneously acquire the resistance R of the coil under the low-frequency excitation signal f1. 10 Inductor L 10 The resistance R of the coil under the high-frequency excitation signal f2 20 Inductor L 20 The ambient temperature T0 and the lift-off distance h0 between the sensor and the cable are also considered. The f1 is set to 500kHz~1MHz according to the cable material, so that the eddy current skin depth δ1 ≤ cable diameter d / 3; the f2 is set to 3MHz~5MHz according to the cable diameter, so that the eddy current skin depth δ2 ≥ cable diameter d / 3.

[0007] S2. Data Preprocessing:

[0008] S2.1. Noise Suppression: For the acquired R 10 L10 R 20 L 20 The denoised data R is obtained by using a combination of moving average filtering and wavelet threshold filtering algorithms. 12 L 12 R 22 L 22 ;

[0009] S2.2. End-to-end temperature compensation: Perform temperature compensation on the resistance and inductance of the sensor coil to obtain the compensated resistance R. t1 R t2 and inductor L t1 L t2 Simultaneously, temperature compensation is applied to the conductivity of the cable core wires to obtain the compensated conductivity σ. T ;

[0010] S3. Parameter decoupling and extraction:

[0011] S3.1. Material Sensitive Factor Extraction: Based on R under low-frequency f1 t1 L t1 Calculate the coil impedance change rate ΔZ1r, and combine it with the coil structure constant K1 to extract the material sensitivity factor S1=μ. r ・σ, where μ r σ is the relative permeability of the cable, and σ is the electrical conductivity of the cable;

[0012] S3.2. Extraction of wire diameter sensitive factor: Based on R at high frequency f2 t2 L t2 Calculate the coil impedance change ΔZ2, and define the wire diameter sensitivity factor S2 = ΔZ2; if h0 is separated from the reference distance h ref =1mm has a deviation, so S2 is corrected to get S2'=S2×h ref / h0;

[0013] S4. Mathematical Model Analysis:

[0014] S4.1. Material Identification: Calculate the material deviation rate η between the cable to be tested and the standard copper or aluminum test block. Cu η Al If η Cu ≤5% is considered copper material, if η Al If the content is ≤5%, it is determined to be aluminum; otherwise, it is determined to be a material abnormality.

[0015] S4.2. Wire Diameter Measurement: A linear regression model of S2 and wire diameter d for the standard conductor is fitted using the least squares method. ref =a・S2+b, combined with σ T By adjusting the skin depth, we obtain the wire diameter d = d ref ×√(σ T / σref ), where σ ref is the standard conductivity of the cable at 20℃, and a and b are regression coefficients;

[0016] S5. Progressive output and anomaly handling: First, output the material type and deviation rate. If the material is qualified, output the wire diameter value and accuracy. Finally, output the comprehensive qualification judgment. If the data is abnormal, the material is abnormal, or the wire diameter is out of tolerance, trigger the corresponding alarm mechanism.

[0017] The technical effects and advantages of this invention are as follows:

[0018] 1. This invention designs a dual-frequency excitation scheme that focuses on the material at low frequency and on the wire diameter at high frequency: the low frequency is set to make the skin depth of the eddy current ≤ the wire diameter d / 3, ensuring that the impedance change is mainly dominated by the material; the high frequency is set to make the skin depth ≥ d / 3, ensuring that the impedance change is mainly dominated by the wire diameter, effectively avoiding the problem of false detection and missed detection.

[0019] 2. This invention constructs a full-link compensation mechanism for sensor coil, cable core, and skin depth. The sensor coil resistance / inductance is compensated by temperature coefficient of copper wire and polytetrafluoroethylene skeleton, and the cable core conductivity is compensated by material-specific temperature coefficient. Combined with the compensated conductivity, the high-frequency skin depth is corrected, further eliminating the influence of temperature on wire diameter measurement. No additional constant temperature device is required, and it can adapt to complex temperature scenarios such as workshops and outdoors.

[0020] 3. All detection logics in this invention are based on explicit mathematical models derived from electromagnetic theory: material identification adopts a standard test block deviation rate model, wire diameter measurement adopts a linear regression and skin depth correction model, model parameters can be directly calibrated through standard test blocks without sample training, reducing calibration time and maintenance costs, while errors can be traced back through formulas, facilitating rapid fault location, and when adapting to different cable specifications, only the excitation frequency and standard parameters need to be adjusted, significantly improving flexibility. Attached Figure Description

[0021] Figure 1 This is a schematic diagram of the overall process of the present invention;

[0022] Figure 2 This is an exploded view of the steps of the present invention. Detailed Implementation

[0023] 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.

[0024] As attached Figure 1 The cable material diameter detection method based on eddy current impedance effect shown includes:

[0025] S1. Data Acquisition: Using data acquisition equipment, the resistance R of the coil under the low-frequency excitation signal f1 is acquired synchronously. 10 Inductor L 10 The resistance R of the coil under the high-frequency excitation signal f2 20 Inductor L 20 The ambient temperature T0 and the lift-off distance h0 between the sensor and the cable are also considered. The f1 is set to 500kHz~1MHz according to the cable material, so that the eddy current skin depth δ1 ≤ cable diameter d / 3; the f2 is set to 3MHz~5MHz according to the cable diameter, so that the eddy current skin depth δ2 ≥ cable diameter d / 3.

[0026] The data acquisition device includes a dual-frequency excitation eddy current sensor, a dual-frequency excitation module, an impedance acquisition module, an environmental parameter acquisition module, and a data processing unit.

[0027] It should be further explained that the dual-frequency excitation eddy current sensor adopts a solenoid structure with a tightly wound coil and a pitch controlled at 0.1mm. The conductor is oxygen-free copper enameled wire with a diameter of 0.15mm, and the insulation layer is made of polyimide to ensure high-temperature resistance and insulation performance. The number of coil turns is adjusted according to the diameter of the cable being tested: 60 turns for cables with a diameter ≤1mm, and 30 turns for cables with a diameter >1mm. The inner diameter of the coil is twice the maximum diameter of the cable being tested, and the coil length is five times the inner diameter. The sensor has a built-in magnetic core made of Mn-Zn ferrite with an initial permeability μ. i =1000, the magnetic core diameter is 0.8 times the inner diameter of the coil. Under no-load conditions, the coil inductance ranges from 100μH to 500μH, the Q value at 1MHz is not less than 50, and the lift-off distance measurement accuracy can reach ±0.01mm.

[0028] The core chip of the dual-frequency excitation module is the AD9850DDS signal generator, which has two independent output channels, used to output low-frequency f1 and high-frequency f2 signals respectively. The module is powered by ±12V DC, with a power supply ripple of no more than 1%. The output frequency stability of the module can reach ±1ppm / ℃, the output current is 50mA±5mA, and the distortion of the output sine wave does not exceed 0.5%.

[0029] The core chip of the impedance acquisition module is the AD5933 impedance converter, covering a measurement range of resistance from 0 to 100Ω and inductance from 0 to 1000μH. The sampling rate is adjusted according to the detection scenario, with 1kHz for static detection and increased to 10kHz for dynamic online detection. The module's measurement accuracy is ±0.05%FS for resistance and ±0.1%FS for inductance, with a signal conversion time of no more than 1ms.

[0030] The environmental parameter acquisition module includes a temperature sensor and a lift-off distance sensor. The temperature sensor is a DS18B20 digital sensor with a measurement accuracy of ±0.05℃. The lift-off distance sensor is a KeyenceIL-300 laser displacement sensor with a measurement range of 0 to 10mm and a measurement accuracy of ±0.01mm.

[0031] The data processing unit uses an STM32H743 embedded CPU with a main frequency of 480MHz to ensure real-time data processing; the storage unit is equipped with an SD card of ≥32GB to support real-time data storage and can cache 1000 sets of historical data for easy subsequent tracing and analysis.

[0032] The data collection steps are as follows:

[0033] During the sensor installation and calibration process, the operation method needs to be adjusted according to the differences in the detection scenario. In the online detection scenario, the sensor is fixed at the detection station on the production line, and the coaxiality between the coil center and the cable conveying axis is calibrated by the laser displacement sensor to ensure that the deviation does not exceed 0.1mm.

[0034] Meanwhile, the lift-off distance between the sensor and the cable surface is set to 1mm, which is achieved by micrometer fine adjustment, with the error controlled within ±0.01mm. In static sampling scenarios, the cable is fixed to a V-shaped clamp made of polytetrafluoroethylene (to avoid introducing electromagnetic interference). The sensor is moved so that the cable passes through the coil in the center, while keeping the lift-off distance constant at 1mm. The excitation parameter settings need to be combined with the cable characteristics. The low frequency f1 is determined according to the cable material. For copper / aluminum core cables, f1 is taken as 800kHz±50kHz. At this time, the skin depth δ1 of copper is about 0.2mm, and the δ1 of aluminum is about 0.25mm, which can ensure that δ1≤d / 3, so that the influence of the material on the impedance change is dominant.

[0035] The high-frequency f2 is determined based on the cable diameter. When the cable diameter is ≤1mm, f2 is taken as 3MHz±100kHz, and when the cable diameter is >1mm, f2 is taken as 5MHz±100kHz. At this time, the δ2 ​​of copper is about 0.08mm and the δ2 ​​of aluminum is about 0.1mm, which can ensure that δ2≥d / 3, so that the influence of the cable diameter on the impedance change is dominant.

[0036] During data acquisition, the excitation module is first activated, synchronously outputting f1 and f2 signals; then the impedance acquisition module continuously acquires 5 sets of data, each set of data including resistance R1 and inductance L1 at low frequency f1, and resistance R2 and inductance L2 at high frequency f2; at the same time, the environmental parameter module synchronously acquires the ambient temperature T and the lift-off distance h.

[0037] During the data acquisition process, the validity of each set of data needs to be determined. If the deviation of the lift-off distance h exceeds 0.02 mm, it is considered invalid data and re-acquisition is required. Finally, the average value of the valid data is taken as the original data, denoted as [R]. 10 ,L 10 ,R 20 ,L 20 ,T0,h0].

[0038] S2. Data Preprocessing:

[0039] S2.1. Noise Suppression: To address common industrial noise issues such as 50Hz power frequency interference, sensor thermal noise, and cable vibration noise, a combined filtering scheme of moving average filtering and wavelet threshold filtering is employed, achieving noise suppression in two steps. The first step is moving average filtering, applied to the original impedance data sequence {x}. n (where n=1 to 5, corresponding to 5 sets of collected data), select a window size k=2 (i.e., 3-point moving average), and calculate using the following formula: Where, when n=1, x1=x2; when n=5, x5=x4, the filtered data R is obtained through this calculation. 11 L 11 R 21 L 21 .

[0040] The second step is wavelet threshold filtering. The db4 wavelet is selected as the wavelet basis, which balances time-domain locality and frequency-domain resolution, making it suitable for processing non-stationary eddy current signals. The decomposition level is set to 3 levels. After decomposition, the high-frequency coefficients correspond to the noise signal, and the low-frequency coefficients correspond to the effective signal. The threshold is calculated using Stein's unbiased risk estimation, with the following formula: (Where N is the data length, N=5 here); Soft thresholding is applied to high-frequency coefficients, with the formula: w′=sign(w)⋅max(∣w∣−λ,0). Finally, the denoised data R is obtained through wavelet reconstruction. 12 L 12 R 22 L 22 ;

[0041] S2.2. End-to-end temperature compensation: This is performed simultaneously on both the sensor coil and the cable core, in two steps to ensure the completeness of the compensation.

[0042] The first step is sensor coil temperature compensation, which includes resistance temperature compensation and inductance temperature compensation. Resistance temperature compensation is based on the temperature coefficient α of the copper wire. Cu =0.00393 / ℃ (according to GB / T3956-2008 standard), the calculation formula is: Inductor temperature compensation is based on the coefficient of thermal expansion α of the coil frame (polytetrafluoroethylene). PTFE =1.2×10 -4 / ℃, the calculation formula is: T ref =20℃ is the reference temperature. Similarly, for R at high frequencies... 22 L 22 Temperature compensation is performed to obtain the compensated R. t2 L t2 .

[0043] The second step is cable core temperature compensation (pre-compensation, preparing for subsequent wire diameter calculation), based on the temperature coefficient of conductivity γ of the cable material, calculated using the formula: σ T =σ ref ×(1−γ(T0−T ref )), where σ ref The standard conductivity of the cable material at 20℃ (according to GB / T351-2021, copper σ) Curef =58×10 6 S / m, aluminum σ Alref =38×10 6 S / m); γ is the temperature coefficient of conductivity of the cable material (copper γ). Cu =0.00393 / ℃, aluminum γ Al =0.00429 / ℃);

[0044] S3. Parameter decoupling and extraction:

[0045] S3.1. Material Sensitive Factor Extraction: At low frequency f1, the eddy current skin depth δ1 is relatively large, and its calculation formula is as follows: (where μ0 = 4π × 10⁻) 7 H / m is the vacuum permeability. At this point, the eddy current penetration depth can cover the cable core, and the impedance change is mainly determined by the relative permeability μ of the cable material. r The effect of wire diameter on impedance change is negligible, as determined by the conductivity σ (experiments show that when d≥3δ1, the contribution of wire diameter to impedance change does not exceed 2%).

[0046] First, calculate the change in coil impedance. The reference value for coil impedance (pre-calibrated) without cable is: Z 01 =√(R 01 ²+(2πf1L 01 )²); When there is a cable, the coil impedance is: Zt1 =√(R t1 ²+(2πf1L t1 )²); and thus the rate of change of impedance is obtained: .

[0047] Subsequently, the material sensitivity factor S1 was derived. Through Maxwell's equations, it can be seen that at low frequencies, ΔZ1r and μ... r The relationship between σ and ΔZ1r is: ΔZ1r = −K1μ r •σ, where K1 is the coil structure constant, which is only related to the coil parameters and can be obtained through calibration using a standard test block. Its calculation formula is: (N is the number of coil turns, D is the inner diameter of the coil, and l is the length of the coil). Rearranging the above formula, we obtain the material sensitivity factor: S1 = μ r •σ=−K1ΔZ1r;

[0048] S3.2. Extraction of Line Diameter Sensitive Factor: At high frequency f2, the eddy current skin depth δ2 is relatively small, and the calculation formula is as follows: At this point, the eddy currents are concentrated only on the cable surface, and the impedance change is mainly determined by the cable diameter d (experiments show that when d ≤ 3δ², the material's contribution to the impedance change does not exceed 1.5%). First, define the cable diameter sensitivity factor S². The coil impedance reference value (pre-calibrated) without cable is: Z 02 =√(R 02 ²+(2πf2L 02 )²); When there is a cable, the coil impedance is: Z t2 =√(R t2 ²+(2πf2L t2 )²); The wire diameter sensitivity factor is the impedance change: S² = ΔZ² = Z t2 -Z 02 (Unit: Ω), if the actual lift-off distance h0 is different from the reference lift-off distance h ref If there is a deviation of 1mm, S2 needs to be corrected. The correction formula is: S2' = S2 × h ref / h0. Experimental verification shows that for every 0.1 mm change in the lift-off distance, S2 changes by approximately 5%. This correction can reduce the lift-off error to within 0.5%, ensuring the accuracy of the wire diameter sensitivity factor.

[0049] S4. Mathematical Model Analysis:

[0050] S4.1. Material Identification: First, standard material sensitivity factor calibration is performed. Prepare pure copper and pure aluminum test blocks, with the pure copper block having a purity of no less than 99.99% and the pure aluminum block no less than 99.95%. Both blocks have a diameter of 5mm and a length of 20mm. The test blocks are then tested at a reference temperature T. ref =20℃, reference lift-off distance h refUnder the condition of 1 mm, the material sensitivity factor S1 of two standard test blocks was measured, and the standard sensitivity factor S1 of copper was obtained respectively. 1Cu With aluminum's standard sensitivity factor S 1Al To ensure calibration accuracy, each material was measured 20 times, and the standard deviation σ was calculated. S1 (usually σ) S1 ≤0.5×10 6 S・H / m).

[0051] The material deviation rate of the cable under test is then calculated, and the copper material deviation rate is defined as: η Cu =|(S1-S 1Cu ) / S 1Cu |×100%, the deviation rate of aluminum material is: η Al =|(S1-S 1Al ) / S 1Al |×100%.

[0052] Finally, the judgment rule is established: if η Cu ≤2σ S1 / S 1Cu If η = ×100% (usually this threshold does not exceed 5%), then the cable is determined to be made of copper; if η = ×100% (usually this threshold does not exceed 5%), then the cable is determined to be made of copper. Al ≤2σ S1 / S 1Al If η = ×100% (usually this threshold does not exceed 5%), then the cable is determined to be made of aluminum; if η = ×100%, then the cable is determined to be made of aluminum. Cu With η Al If all exceed 5%, then the deviation between S1 and the standard value of the impurity material (such as copper alloy or aluminum alloy) is further calculated. If all still exceed the tolerance, the cable is judged to be of abnormal material (containing impurities or non-target material).

[0053] S4.2. Wire Diameter Measurement: Select five standard wires of known diameters as calibration samples. The diameters of the standard wires should cover 10%, 30%, 50%, 70%, and 90% of the range of wire diameters to be tested—for example, when the range of wire diameters to be tested is 0.5mm to 2mm, the standard wire diameters should be 0.5mm, 0.9mm, 1.2mm, 1.6mm, and 2.0mm, respectively. At the reference temperature T... ref =20℃, reference lift-off distance h ref Under the condition of 1 mm, the wire diameter sensitivity factor S2 of each standard conductor was measured, and 5 sets of calibration data pairs were obtained (S 201 ,d 01 ) to (S 205 ,d 05 The calibration data were fitted using linear regression with the least squares method, requiring a goodness of fit R² ≥ 0.999. The final formula for calculating the wire diameter was obtained: d ref=a・S2+b, where a is the slope (in mm / Ω) and b is the intercept (in mm). For example, through calibration, a=0.002mm / Ω and b=0.05mm can be obtained.

[0054] The second stage is skin depth correction (temperature compensation). At high frequencies, the wire diameter sensitivity factor S2 is related to the skin depth δ2, and δ2 is affected by the cable conductivity σ. T The influence, therefore, needs to be considered when dealing with d obtained from linear regression. ref The correction is made, and the correction formula is: d=d ref ×δ ref / δ T , where δ ref Reference temperature T ref The skin depth is calculated using the following formula: ;δ T The skin depth at the actual temperature T0 is calculated using the following formula: , will σ T Substituting the calculation formula and simplifying, we get: This correction eliminates the effect of temperature on cable conductivity, ensuring the accuracy of wire diameter measurement.

[0055] S5. Progressive output and anomaly handling: First, output the material type and deviation rate. If the material is qualified, output the wire diameter value and accuracy. Finally, output the comprehensive qualification judgment. If the data is abnormal, the material is abnormal, or the wire diameter is out of tolerance, trigger the corresponding alarm mechanism.

[0056] A three-tiered anomaly handling mechanism has been established to address potential anomalies during the testing process:

[0057] One is data anomaly handling; if the standard deviation of the collected data exceeds 0.5% (e.g., R...). 10 If the standard deviation of the 5 sets of data exceeds 0.06Ω, the system will automatically re-acquire the data 3 times; if the data is still abnormal after 3 re-acquisitions, the system will output a message indicating detection failure (data instability) and suggest checking the sensor connection status to rule out hardware failure.

[0058] Secondly, there is the handling of material abnormalities. When the cable is determined to be of abnormal material, the system outputs a warning message that the material is unqualified (not copper / aluminum or contains impurities) and triggers a red audible and visual alarm. The alarm frequency is 1kHz and the duty cycle is 50%, so as to remind the inspection personnel to deal with it in time with a strong warning signal.

[0059] Thirdly, there is the handling of wire diameter out-of-tolerance. When the wire diameter value exceeds the preset acceptable range, the system outputs a prompt indicating that the wire diameter is out of tolerance. In online inspection scenarios, the system simultaneously outputs a 24V switch signal to trigger the production line to stop, preventing the continuous production of unqualified products. In offline sampling inspection scenarios, a yellow audible and visual alarm is triggered, prompting the inspectors to mark the cable as unqualified.

[0060] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A cable material diameter detection method based on eddy current impedance effect, characterized in that, Comprise: S1. Data acquisition: using a dual-frequency excitation eddy current sensor, synchronously collecting the resistance R 10 , inductance L 10 of the coil under low-frequency excitation signal f1, the resistance R 20 , inductance L 20 of the coil under high-frequency excitation signal f2, and the ambient temperature T0, the lift-off distance h0 between the sensor and the cable, wherein f1 is set to 500 kHz-1 MHz according to the cable material, so that the eddy current skin depth δ1≤cable diameter d / 3; f2 is set to 3 MHz-5 MHz according to the cable diameter, so that the eddy current skin depth δ2≥cable diameter d / 3; S2. Data preprocessing: S2.

1. Noise suppression: the collected R 10 , L 10 , R 20 , L 20 The combination algorithm of moving average filtering and wavelet threshold filtering is adopted to process, and the denoised data R 12 , L 12 , R 22 , L 22 ; S2.

2. Full-link temperature compensation: resistive temperature compensation and inductive temperature compensation are performed on the sensor coil to obtain the compensated resistance R t1 , R t2 and inductance L t1 , L t2 ; at the same time, the cable core is subjected to conductivity temperature compensation to obtain the compensated conductivity σ T ; S3. Parameter decoupling extraction: S3.

1. Material sensitive factor extraction: based on R t1 , L t1 , calculate the coil impedance change rate ΔZ1r, combine the coil structure constant K1, and extract the material sensitive factor S1=μ r ・σ, where μ r is the relative permeability of the cable, and σ is the cable conductivity; S3.

2. Wire diameter sensitivity factor extraction: based on R at high frequency f2 t2 , L t2 , calculate the coil impedance change amount ΔZ2, define the wire diameter sensitivity factor S2 = ΔZ2; if there is a deviation between h0 and the reference lift-off distance h ref =1mm, correct S2 to obtain S2'=S2×h ref / h0; S4. Mathematical model analysis: S4.

1. Material identification: calculate the material deviation rate η of the cable to be detected and the standard copper or aluminum test block Cu Al If η Cu ≤ 5%, it is determined as copper material, if η Al ≤ 5%, it is determined as aluminum material, otherwise it is determined as material abnormality;​ S4.

2. Wire diameter measurement: the linear regression model d = a・S2+b of the standard wire and the wire diameter d is fitted by the least square method, combined with σ ref T correcting the skin depth to obtain the wire diameter d = d ref ×√(σ T / σ ref ), where σ ref is the standard conductivity of the cable at 20℃, and a and b are regression coefficients;​ S5. Progressive output and abnormal processing: first output material category and deviation rate, material qualified output line diameter value and precision, finally output comprehensive qualified judgment, if data is abnormal, material is abnormal or line diameter is out of tolerance, trigger corresponding alarm mechanism; A three-level abnormal processing mechanism is established for abnormal situations occurring in the detection process.

2. The cable material diameter detection method based on the eddy current impedance effect according to claim 1, characterized in that: The coil of the dual-frequency excitation eddy current sensor is a solenoid type tightly wound structure, the number of turns of the coil corresponding to a cable with a wire diameter ≤1mm is 60 turns, and the number of turns of the coil corresponding to a cable with a wire diameter >1mm is 30 turns; the inner diameter of the coil is 2 times the maximum wire diameter of the measured cable, and the length of the coil is 5 times the inner diameter of the coil; the coil wire is 0.15mm oxygen-free copper enameled wire, and an Mn-Zn ferrite core is built in, the initial magnetic permeability μ i =1000.

3. The cable material and line diameter detection method based on the eddy current impedance effect according to claim 1, characterized in that: The window size k of the sliding average filter is 2, and the calculation formula is where x n is the original data sequence, n=1~5; the wavelet threshold filter adopts a db4 wavelet base, the decomposition layer is 3 layers, the threshold is calculated through Stein unbiased risk estimation, and the formula is , N is the data length, and the soft threshold processing w'=sign(w)⋅max(|w|-λ,0) is adopted for the high-frequency coefficient.

4. The cable material and line diameter detection method based on the eddy current impedance effect according to claim 1, characterized in that: The formula of resistance temperature compensation is The formula of inductance temperature compensation is Wherein, α Cu =0.00393 / ℃ is the temperature coefficient of copper wire, α PTFE =1.2×10 -4 / ℃ is the thermal expansion coefficient of polytetrafluoroethylene skeleton, T ref =20℃ is the reference temperature; the conductivity temperature compensation formula is σ T =σ ref ×(1−γ(T0−T ref )), γ Cu =0.00393 / ℃ for copper core cable, γ Al =0.00429 / ℃ for aluminum core cable.

5. The cable material diameter detection method based on the eddy current impedance effect according to claim 1, characterized in that: The formula for calculating the coil impedance change rate ΔZ1r is wherein Z 01 =√(R 01 ²+(2πf1L 01 )²) is the coil impedance reference value without cable, Z t1 =√(R t1 ²+(2πf1L t1 )²) is the coil impedance with cable, and the formula for calculating the coil structure constant K1 is , N is the number of turns, D is the inner diameter of the coil, l is the length of the coil, μ0=4π×10 -7 H / m is the vacuum permeability.

6. The cable material diameter detection method based on the eddy current impedance effect according to claim 1, characterized in that: The formula for calculating the line diameter sensitivity factor S2 is S2 = Z t2 - Z 02 , where Z 02 = √(R 02 ² + (2πf2L 02 )²) is the high frequency coil impedance reference value without a cable, and Z t2 = √(R t2 ² + (2πf2L t2 )²) is the high frequency coil impedance with a cable.

7. The cable material and line diameter detection method based on the eddy current impedance effect according to claim 1, characterized in that: The material deviation rate η Cu = |(S1-S 1Cu ) / S 1Cu | × 100%, η Al = |(S1-S 1Al ) / S 1Al | × 100%, wherein S 1Cu , S 1Al are material sensitivity factors of standard copper and aluminum test blocks under the conditions of 20℃ and h ref = 1mm, and the purity of the standard copper and aluminum test blocks is copper ≥ 99.99% and aluminum ≥ 99.95%, respectively, and the size is 5mm in diameter and 20mm in length.

8. The cable material diameter detection method based on the eddy current impedance effect according to claim 1, characterized in that: The goodness of fit R² of the linear regression model is greater than or equal to 0.999, and the calibration samples are five standard wires with known diameters, covering 10%, 30%, 50%, 70%, and 90% of the range of diameters to be detected; and the σ ref is the standard conductivity at 20°C, and the copper σ Curef = 58 x 10 6 S / m, and the aluminum σ Alref = 38 x 10 6 S / m.

9. The cable material diameter detection method based on the eddy current impedance effect according to claim 1, characterized in that: The progressive output includes three levels: the first level output material category, deviation rate and confidence, confidence = 1 - deviation rate, the second level output wire diameter value, measurement accuracy ± 0.003mm and temperature correction amount, the third level output comprehensive qualified judgment, the wire diameter is qualified within [d min ,d max ] range, otherwise it is out of tolerance.

10. The cable material diameter detection method based on the eddy current impedance effect according to claim 1, characterized in that: The abnormal processing mechanism comprises: automatically reacquiring 3 times when data is abnormal, still abnormal, output detection failure, material abnormal, trigger red audible and light alarm, line diameter out of tolerance, online scene linkage production line shutdown, offline scene trigger yellow audible and light alarm.

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