Insulating layer compactness detection method for mineral cable

Abstract

The invention relates to the field of electrical variable measurement, in particular to an insulating layer compactness detection method for a mineral cable, which comprises the following steps: firstly, acquiring a time domain reflection signal of the cable, and carrying out denoising pretreatment by utilizing synchronous average and wavelet transform; and then, carrying out spectrum analysis on the discrete signal segments, calculating a spectrum centroid offset rate to determine high-frequency energy attenuation, and constructing a dynamic compensation gain coefficient according to the high-frequency energy attenuation. And then, improving a traditional layer stripping algorithm by using the gain coefficient, eliminating calculation deviation caused by transmission loss, and accurately reconstructing characteristic impedance distribution along the cable. And finally, by comparing the deviation between the reconstructed impedance and the nominal impedance, positioning and judging the abnormities of the compactness of the insulating layer of the cable and the eccentricity defect of the cable core are realized.

Description

Technical Field

[0001] This invention relates to the field of electrical variable measurement, and more specifically to a method for detecting the insulation density of mineral-insulated cables. Background Technology

[0002] Mineral-coated cables consist of copper conductors, a magnesium oxide powder insulation layer, and a metal sheath. Due to their unique structure, the density of the magnesium oxide powder directly determines the cable's insulation performance, fire resistance, and mechanical strength. During the production or laying of multi-core mineral-coated cables, the internal conductors are prone to misalignment due to drawing processes or bending stress, leading to localized thinning of the insulation layer. This change in geometry is equivalent to an abnormality in insulation density, which can severely cause breakdown accidents. Therefore, analyzing the impedance distribution along the cable using time-domain reflectometry is an important method for detecting internal defects in cables.

[0003] Among existing impedance reconstruction techniques, the layer peeling algorithm is a core and commonly used algorithm. This algorithm recursively calculates the reflection coefficient at each location of the transmission line layer by layer using reflection data collected at the test port, thereby reconstructing the characteristic impedance distribution curve. However, traditional layer peeling algorithms are usually based on the ideal assumption of lossless transmission lines. For mineral-insulated cables, due to the skin effect and insulation losses, they are inherently high-loss transmission lines, and the signal amplitude attenuates significantly with increasing distance during transmission. Traditional algorithms ignore the two-way attenuation experienced by the signal during round-trip transmission, resulting in an underestimation of the deep reflection coefficient when dealing with long-distance mineral-insulated cables. This error accumulates with distance, causing severe distortion of the reconstructed far-end impedance value of the cable, making it difficult to accurately characterize the insulation state and physical defects deep within the cable. Summary of the Invention

[0004] To address the problem that the aforementioned layer stripping algorithm is insufficient to accurately characterize the insulation state and physical defects deep within the cable, this invention proposes a method for detecting the insulation density of mineral-insulated cables. The method includes: acquiring the time-domain reflection signal generated by an excitation signal propagating along the mineral-insulated cable; dividing the time-domain reflection signal into multiple signal segments according to the transmission time, with each signal segment corresponding to a layer of the cable; using a gain coefficient to perform amplitude compensation on the reflection coefficient of the signal segments to obtain the compensated true reflection coefficient; recursively calculating the characteristic impedance distribution along the mineral-insulated cable using the layer stripping algorithm based on the true reflection coefficient; marking locations in the characteristic impedance distribution that exceed a preset impedance threshold as insulation state anomaly points; the gain coefficient is inversely proportional to the magnitude of the spectral centroid offset rate and directly proportional to the positive square root of the physical positioning distance of the corresponding layer along the cable length; the spectral centroid offset rate is positively correlated with the spectral centroid of the corresponding signal segment and inversely correlated with the spectral centroid reference value of the excitation signal at the cable incident end.

[0005] Existing layer stripping algorithms often neglect frequency-dependent losses and distance-dependent attenuation during cable transmission, leading to a rapid accumulation of impedance reconstruction errors as cable length increases, making it difficult to accurately detect deep defects. This invention calculates the spectral centroid offset rate of a time-domain reflected signal segment and creatively utilizes the positive square root of this offset rate with the physical location distance to construct a dynamic compensation gain. This adaptively corrects signal attenuation and distortion at high frequencies and over long distances. Applying this dynamic compensation to the layer stripping algorithm for reconstructing characteristic impedance distribution significantly reduces accumulated reconstruction errors, thereby achieving high-precision detection of insulation density and defect location across the entire length of mineral-insulated cables.

[0006] Furthermore, acquiring the time-domain reflection signal includes: injecting a step pulse or Gaussian pulse excitation signal between the conductor and the metal sheath of the mineral cable, and acquiring the reflection waveform data of the cable test port.

[0007] Compared to the pulse signals with slow rising edges commonly used in existing technologies, this invention limits the use of broadband step pulse signals or Gaussian pulse signals as excitation, which means that the signal has a wider spectral coverage and higher high-frequency components. This high-frequency characteristic significantly improves the temporal and spatial resolution of time-domain reflectometry, enabling the detection system to capture minute impedance changes over shorter distances, thereby more sensitively identifying subtle insulation density defects or minor physical structural damage.

[0008] Furthermore, it also includes preprocessing the time-domain reflection signal, specifically: synchronously averaging the reflection waveform data collected multiple times, and using wavelet transform to denoise the averaged signal.

[0009] Directly processing the original time-domain reflection signal is easily affected by environmental electromagnetic noise and equipment thermal noise, impacting the accuracy of feature extraction. This invention first performs wavelet denoising to obtain a preprocessed signal during discretization, then uses a sliding window for segmentation. This method effectively filters out background noise, improves the signal-to-noise ratio, and ensures the continuity of signal analysis in the time domain through the sliding window. This guarantees the purity of the data and the integrity of local features when calculating the spectral centroid offset rate, avoiding spurious offset calculations caused by noise.

[0010] Furthermore, the wavelet transform includes performing 3-5 levels of wavelet decomposition using the Symlet wavelet basis or the Daubechies wavelet basis, and reconstructing the high-frequency detail coefficients after processing them with a soft thresholding function.

[0011] Furthermore, the specific method for calculating the gain coefficient is as follows: ; in Indicates that for the first Gain coefficient of the reflected signal; This represents the centroid offset rate of the spectrum; Represents the natural exponential function; This represents the set attenuation constant; Indicates the first The physical positioning distance of the layer; This represents the smallest positive number that prevents the denominator from being zero.

[0012] The energy center shift of each level of signal spectrum relative to the initial pulse spectrum is calculated by integration. Compared to traditional methods that rely solely on signal amplitude attenuation to assess loss, this invention reflects the frequency-selective attenuation caused by insulation dielectric loss, providing a precise indicator consistent with physical laws for subsequent dynamic gain compensation. This allows the compensation strategy to better reflect the true transmission characteristics within the cable.

[0013] Furthermore, the method for calculating the physical positioning distance is as follows: ;in Indicates the first The physical location distance of the layer; Indicates cable wave speed; Indicates a hierarchical index; This indicates the time step of the sliding window.

[0014] Furthermore, the method for calculating the spectral centroid offset rate is as follows: ; in Indicates the first The spectral centroid offset of the layer; Indicates the first The signal spectrum corresponding to the layer; For frequency variables; The set effective bandwidth limit; This represents the reference value of the spectral centroid.

[0015] Furthermore, the specific method for calculating the characteristic impedance distribution is as follows: ; in Indicates the current number The characteristic impedance of the layer, This is the characteristic impedance of the previous layer; Indicates the current number The actual reflectance coefficient of the layer; This indicates the preset parameter tuning factor.

[0016] Furthermore, it also includes determining, based on the degree to which the characteristic impedance value in the characteristic impedance distribution is lower than the nominal impedance, whether there is a core eccentricity or insulation layer moisture defect at the corresponding location.

[0017] This invention compares the reconstructed characteristic impedance distribution with a set nominal impedance and uses a threshold to determine defects. Compared to the subjective and inefficient method of relying on manual waveform observation, this technical solution automates and digitizes defect detection. By quantifying impedance deviation, the system can objectively determine the presence of defects and output location information, greatly improving detection efficiency and ensuring the consistency and repeatability of detection results.

[0018] Furthermore, the preset impedance threshold is set to to .

[0019] The technical effects of this invention are as follows: This invention addresses the drawback of traditional TDR technology, which suffers from large impedance reconstruction errors due to high-frequency attenuation during long-distance detection. It utilizes the spectral centroid offset of a signal segment to quantize frequency loss and constructs an adaptive dynamic gain model based on the positive square root of the physical distance. This method effectively corrects signal distortion, significantly improves the accuracy of characteristic impedance reconstruction, and enables precise location and identification of defects such as core eccentricity and insufficient insulation density. Attached Figure Description

[0020] Figure 1 This is a schematic flowchart illustrating an embodiment of the present invention for detecting the insulation density of a mineral-insulated cable; Figure 2 This is a schematic diagram illustrating the cross-sectional structure of a mineral cable according to an embodiment of the present invention; Figure 3This is a schematic diagram illustrating the TDR detection principle and signal propagation of an embodiment of the present invention; Figure 4 This is a waveform comparison diagram illustrating the time-domain reflection signal preprocessing process in an embodiment of the present invention; Figure 5 This is a schematic diagram illustrating the variation of the spectral centroid offset rate with the cable transmission distance in an embodiment of the present invention; Figure 6 This is a schematic diagram showing a comparison of signal power spectral density at different transmission distances in an embodiment of the present invention; Figure 7 This is a schematic diagram illustrating the change of gain coefficient with transmission distance in an embodiment of the present invention. Detailed Implementation

[0021] 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, not all, of the embodiments of the present invention. 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.

[0022] The specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0023] An example of a method for testing the insulation density of mineral-insulated cables: like Figure 1 As shown, a method for detecting the insulation density of mineral-insulated cables according to the present invention includes: S1. Collect the original time-domain reflection waveform data of the mineral cable, filter out random noise interference using synchronous averaging and wavelet transform techniques, and use sliding window techniques to perform time-domain discretization segmentation of the denoised signal to obtain discrete signal segments corresponding to different layer order indices and characterizing local reflection features.

[0024] In this embodiment, the testing environment is first set up by connecting one end of the multi-core mineral cable to be tested to the test port of a high-precision vector network analyzer (VNA) or time domain reflectometer (TDR). To ensure that the signal can propagate effectively in this type of high-loss transmission line, the testing instrument injects an excitation signal between the cable conductor and the metal sheath.

[0025] The mineral cable structure addressed in this embodiment is as follows: Figure 2 As shown, it consists of an inner copper conductor, a middle layer of magnesium oxide (MgO) insulation, and an outer metal sheath. Figure 3This demonstrates the detection principle: the TDR tester injects a pulse signal from one end of the cable. During the transmission of the signal along the line, if it encounters an impedance discontinuity point caused by core eccentricity or changes in insulation density (as shown in the "Impedance Abnormal Point" in the figure), a reflected signal echo will be generated.

[0026] As a preferred embodiment, the excitation signal has an extremely short rise time, for example... Broadband step pulse signals or Gaussian pulse signals within a certain range. These signals have rich spectral components and can capture subtle impedance changes within the cable.

[0027] Then, the raw time-domain reflection waveform data at the cable port was collected. Considering the complexity of the electromagnetic environment at the site, the raw signal is usually mixed with Gaussian white noise. To improve the signal-to-noise ratio, this step first processes the continuously acquired signals... The original waveforms are synchronously averaged, and in this embodiment, it is preferred to perform such averaging. There are 128 groups.

[0028] To further filter out high-frequency random noise while preserving signal edge features reflecting impedance abrupt changes, this embodiment employs wavelet transform for signal denoising. Specifically, a Symlet wavelet basis with good symmetry and tight support (e.g., ) or Daubechies wavelet basis (e.g. ), and perform averaging on the signal Wavelet decomposition of the layers. A soft thresholding function is used to process the high-frequency detail coefficients, thereby performing wavelet reconstruction to obtain the preprocessed time-domain reflection signal sequence. .

[0029] Finally, the sliding window technique is used to process the preprocessed time-domain reflection signal. Perform time-domain discretization. Set the time step of the sliding window to be... Segmenting a continuous signal into A discrete signal segment. Definition For discrete layer sequence index ( This yields a hierarchical index. A sequence of discrete signal segments arranged in an array, denoted as ,in Characterizing the first Local reflection characteristics of the layer.

[0030] like Figure 4 As shown, this demonstrates the effect of the signal preprocessing stage. The original acquired signal contained random noise, which was initially suppressed after N=128 sets of synchronous averaging processing.

[0031] S2. Perform a fast Fourier transform on each discrete signal segment to obtain the instantaneous spectral distribution. By calculating the normalized offset relative to the spectral centroid reference value of the initial excitation pulse, obtain the spectral centroid offset rate, which can quantitatively characterize the high-frequency energy attenuation state of the signal during transmission along the cable.

[0032] In response to the phenomenon that high-frequency components attenuate faster than low-frequency components in mineral-insulated cables due to the skin effect and dielectric loss of the magnesium oxide insulation layer, this embodiment evaluates the degree of spectral distortion of the signal during transmission.

[0033] First, regarding step S1... layer signal fragments Performing a Fast Fourier Transform (FFT) converts the time-domain signal into a frequency-domain signal, obtaining its instantaneous spectral distribution. .

[0034] As the signal propagates through the cable, high-frequency energy is gradually dissipated, causing the energy centroid of the spectrum to shift towards lower frequencies. Based on this physical characteristic, this embodiment can calculate the spectral centroid shift rate to quantitatively characterize the... The degree of signal dispersion in a layer is calculated using the following formula: ; in Indicates the first The spectral centroid offset of the layer; Indicates the first The signal spectrum corresponding to the layer; For frequency variables; As the upper limit of the effective bandwidth, in this embodiment, based on the characteristics of the excitation pulse and the cutoff frequency of the cable, it is preferably set to [value missing]. ; This is the reference value for the centroid of the spectrum of the initial pulse signal injected into the cable head. As a specific example, when the excitation signal is a broadband step pulse with a rise time of 200 ps, ​​its effective spectral energy is concentrated in DC (…). )to Within the frequency band. At this time. The preferred numerical range is This reference value serves as a normalization benchmark, used to quantify the degree of high-frequency energy attenuation relative to the initial spectral distribution as transmission distance increases.

[0035] As can be seen from the above formula, the spectral centroid offset rate actually represents the weighted average frequency (i.e., centroid) of the power spectral density at the current position, and is normalized relative to the input signal. The smaller the value, the more high-frequency components are retained when the sample is near the test end, resulting in a larger integral value in the molecule. close to As the transmission distance increases, high-frequency components... The significant reduction causes the numerator integral value to decrease much faster than the denominator, making... Monotonically decreasing. Therefore, The numerical change can linearly and sensitively reflect the signal distortion trend caused by the physical properties of the cable material. For example... Figure 5 As shown, it illustrates the trend of the calculated spectral centroid offset rate as a function of cable position. It can be seen that as the transmission distance increases, due to high-frequency losses... It exhibits a monotonically decreasing trend. And... Figure 6 This further illustrates the signal spectrum at different locations, showing that as distance increases, the high-frequency components of the signal ( The significant attenuation causes the center of gravity of the spectrum to shift to lower frequencies.

[0036] S3. Establish the spatial mapping relationship between the discrete layer sequence index and the physical positioning distance of the cable, and construct an adaptive exponential compensation model by combining the calculated spectral centroid offset rate, and solve for the gain coefficient used to offset the reverse process of energy dissipation generated by the propagation of the signal in the lossy transmission line.

[0037] Traditional layer peeling algorithms assume that the transmission line is lossless, which can lead to an underestimation of the deep reflection coefficient when dealing with long-distance mineral-insulated cables due to signal attenuation. To correct this bias, this embodiment obtains a mapping relationship relative to the amplitude compensation amount based on spectral characteristics.

[0038] In real-world physical conditions, there is a non-linear relationship between signal amplitude attenuation and the shift of the spectral centroid. Therefore, in this embodiment, the gain coefficient for dynamic transmission compensation is calculated, and its mathematical model is shown below: ; in Indicates that for the first Gain coefficient of the reflected signal; The attenuation constant is represented by the value of which is related to the physical properties of the conductor and insulation materials of the mineral cable. In this embodiment, the conductor material is copper and the insulation material is magnesium oxide powder. This value can be obtained through calibration experiments on standard short sample cables, with a preferred range of [value missing]. ; Indicates the first The physical location distance of the layer in the cable, assuming the cable wave velocity is... , due to the The time delay corresponding to the layer is Then it can be deduced that the first... Physical positioning distance of the layer for ; To prevent extremely small positive numbers with a denominator of zero, this embodiment preferably uses... .

[0039] From the above formula, it can be seen that when near When there is no attenuation at the cable head end, the fractional term... Approaching This makes the exponential term ,get No signal compensation is provided; however, as distance increases... The increase in losses leads to As the fraction term gradually decreases, its value increases rapidly. The term also increases with distance, and the combined effect of both makes... It exhibits nonlinear exponential growth, thus adaptively fitting and offsetting the inverse process of energy dissipation in lossy transmission lines. For example... Figure 7 As shown, its gain coefficient increases non-linearly and exponentially with distance to compensate for signal transmission attenuation.

[0040] S4. The amplitude of the measured reflection coefficient of each layer is corrected by the gain coefficient to restore the true signal strength. The improved layer stripping algorithm to eliminate multiple reflection interference is used to recursively reconstruct the characteristic impedance distribution curve along the entire length of the cable that accurately reflects the changes in the geometric structure of the cable.

[0041] In the traditional recursive layer-stripping process, the impedance of the next layer is directly calculated using the measured reflection coefficient, ignoring the two-way attenuation experienced by the reflected wave during its return journey. Therefore, in this embodiment, the gain coefficient calculated in step S3 is used to define the true reflection coefficient after loss decoupling. The calculation formula is as follows: ; in Indicates the preprocessed signal The extracted first The true reflectance coefficient of the layer; Indicates the preceding order to The residual effects of multiple layer reflections are calculated using the standard method of existing layer stripping algorithms to eliminate the interference of multiple reflections.

[0042] By measuring the attenuated reflectance coefficient With dynamic compensation gain Multiplying these components recovers the signal amplitude lost due to transmission loss. Then, the corrected true reflection coefficient is used. The specific calculation method for reconstructing the characteristic impedance distribution along the line is as follows: ; in Indicates the first The characteristic impedance of the layer, This is the characteristic impedance of the previous layer; This represents the parameter adjustment factor to avoid total internal reflection in extreme cases, i.e., the denominator is 0 when there is an open circuit or short circuit. Through layer-by-layer recursion, the reconfigured impedance distribution curve of the entire cable is finally obtained. ,in For distance variables.

[0043] S5. The reconstructed characteristic impedance distribution data along the line is compared and analyzed with the nominal impedance value of the standard cable along the entire length. Based on the impedance deviation, the change in the core eccentricity or the dielectric constant of the insulation material is deduced, thereby realizing the full-length quantitative detection and defect location of the cable insulation layer density and uniformity.

[0044] The contents obtained in step S4 impedance sequence at discrete points Compared with the physical location distance sequence calculated in step S3 Perform spatial mapping. (Based on the first...) Physical positioning distance of the layer Using x as the x-axis, and y as the x-axis, with the y-axis as the x-axis. characteristic impedance of the layer Using the vertical axis as the ordinate, construct the full-length reconfiguration impedance distribution curve. Then compare it with the nominal impedance of a standard mineral cable. ,For example or Compare them.

[0045] Because conductor misalignment in multi-core mineral-insulated cables alters the spacing between the conductor and the metal sheath, it causes local characteristic impedance to deviate from the reference value. This embodiment determines the insulation density state based on the following logic: First, set a preset impedance threshold. For example, for a nominal impedance of The cable, Can be set to .

[0046] Next, calculate the absolute value of the impedance deviation at each location along the line: ; If in position Place, satisfy If so, it is determined that there is a core misalignment or a loose insulation layer defect at that location.

[0047] Furthermore, based on the impedance calculation formula for coaxial transmission lines or multi-core transmission lines, the impedance deviation is used. The eccentricity of the wire core can be deduced. The change in the equivalent dielectric constant of the insulation layer can be used to achieve full-length quantitative detection of the uniformity of the density of the internal insulation layer of the cable.

[0048] For example, when the impedance value Significantly lower than This usually indicates that the conductor has shifted towards the sheath or that the insulation powder is damp / uncompacted, suggesting a risk of breakdown at that location. For example, assume the nominal impedance of the mineral cable under test is... for Preset impedance threshold for .

[0049] In one embodiment, when the cable length is detected... At this point, the local characteristic impedance value of the system reconfiguration Calculate the absolute value of its impedance deviation. .because The insulation layer density distribution was determined to be uniform, and the coaxiality of the core and sheath met the process requirements.

[0050] In another embodiment, when the cable length is detected... At this point, the local characteristic impedance value of the system reconfiguration Calculate the absolute value of its impedance deviation. .because Furthermore, the impedance decreased significantly, indicating that there was an abnormal density of the insulation layer or a core eccentricity defect at that location.

[0051] According to the impedance characteristic equation of coaxial transmission line (in For the inner diameter of the sheath, The outer diameter of the conductor. (where the dielectric constant of the insulating material is), at this location The impedance drop indicates that: If the dielectric constant of the insulating material If the distance remains unchanged, the conductor undergoes a significant eccentric displacement, resulting in a reduction in the equivalent spacing between the conductor and the metal sheath. Alternatively, the equivalent dielectric constant may be affected by moisture or insufficient packing density of the magnesium oxide insulating powder. The abnormal increase is observed. Based on this, the system outputs the positioning coordinates as follows: The risk warning signal of breakdown.

Claims

1. A method for detecting the insulation density of mineral-insulated cables, characterized in that, The method includes: acquiring a time-domain reflected signal generated by an excitation signal propagating along a mineral cable, and dividing the time-domain reflected signal into multiple signal segments according to the transmission time, with each signal segment corresponding to a layer of the cable; The reflection coefficient of the signal segment is compensated by a gain coefficient to obtain the compensated true reflection coefficient. Based on the true reflection coefficient, the characteristic impedance distribution along the mineral cable is recursively calculated using a layer stripping algorithm. The locations in the characteristic impedance distribution that exceed a preset impedance threshold are marked as abnormal points in the insulation layer condition. The gain coefficient is inversely proportional to the magnitude of the spectral centroid offset rate and directly proportional to the square root of the physical positioning distance along the cable length of the corresponding layer; the spectral centroid offset rate is positively correlated with the spectral centroid of the corresponding signal segment and inversely correlated with the spectral centroid reference value of the excitation signal at the cable incident end.

2. The method for detecting the insulation density of mineral-insulated cables according to claim 1, characterized in that, Obtaining the time-domain reflection signal includes: A step pulse or Gaussian pulse excitation signal is injected between the conductor and the metal sheath of the mineral cable, and the reflected waveform data of the cable test port is collected.

3. A method for detecting the insulation density of mineral-insulated cables according to claim 1 or 2, characterized in that, It also includes preprocessing the time-domain reflection signal, specifically: synchronously averaging the reflection waveform data collected multiple times, and using wavelet transform to denoise the averaged signal.

4. The method for detecting the insulation density of mineral-insulated cables according to claim 3, characterized in that, The wavelet transform includes wavelet decomposition of 3 to 5 levels using the Symlet wavelet basis or the Daubechies wavelet basis, and reconstruction after processing the high-frequency detail coefficients using a soft thresholding function.

5. The method for detecting the insulation density of mineral-insulated cables according to claim 1, characterized in that, The specific method for calculating the gain coefficient is as follows: ； in Indicates that for the first Gain coefficient of the reflected signal; This represents the centroid offset rate of the spectrum; Represents the natural exponential function; This represents the set attenuation constant; Indicates the first The physical positioning distance of the layer; This represents the smallest positive number that prevents the denominator from being zero.

6. The method for detecting the insulation density of mineral-insulated cables according to claim 5, characterized in that, The specific method for calculating the physical positioning distance is as follows: ;in Indicates the first The physical location distance of the layer; Indicates cable wave speed; Indicates a hierarchical index; This indicates the time step of the sliding window.

7. The method for detecting the insulation density of mineral-insulated cables according to claim 5, characterized in that, The specific method for calculating the spectral centroid offset rate is as follows: ； in Indicates the first The spectral centroid offset of the layer; Indicates the first The signal spectrum corresponding to the layer; For frequency variables; The set effective bandwidth limit; This represents the reference value of the spectral centroid.

8. The method for detecting the insulation density of mineral-insulated cables according to claim 1, characterized in that, The specific method for calculating the characteristic impedance distribution is as follows: ； in Indicates the current number The characteristic impedance of the layer, This is the characteristic impedance of the previous layer; Indicates the current number The actual reflectance coefficient of the layer; This indicates the preset parameter tuning factor.

9. The method for detecting the insulation density of mineral-insulated cables according to claim 1, characterized in that, It also includes determining, based on the degree to which the characteristic impedance value in the characteristic impedance distribution is lower than the nominal impedance, whether there is a core eccentricity or insulation layer moisture defect at the corresponding location.

10. A method for detecting the insulation density of mineral-insulated cables according to claim 9, characterized in that, The preset impedance threshold is set as follows: to .

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