Cable fault positioning method based on wavelet transform and linear frequency modulation Z inverse transform

Abstract

The invention discloses a cable fault positioning method based on wavelet transform and linear frequency modulation Z inverse transform, and belongs to the technical field of communication measurement instruments and meters. The cable fault positioning method based on wavelet transform and linear frequency modulation Z inverse transform comprises the following steps: measuring reflection parameter data of a to-be-detected fault cable port through a vector network analyzer; denoising the measured reflection parameter data through wavelet transform; converting the denoised reflection parameter data into time domain impulse response data through linear frequency modulation Z inverse transformation; converting the time domain pulse response data into distance domain pulse response data; and obtaining a module value of the distance domain pulse response data to obtain a defect positioning spectrum of the to-be-detected fault cable. The method is not limited by scanning equipment, existing hardware does not need to be transformed, and high-precision cable defect positioning spectrum measurement can be achieved only on existing equipment.

Description

Technical Field

[0001] The present invention belongs to the technical field of communication measurement instruments, and particularly relates to a method for cable fault location based on wavelet transform and inverse chirp Z-transform. Background Art

[0002] Frequency domain reflectometry (FDR) is widely used in the fields of power electronics, radio frequency, and microwave. Users inject a detection signal (swept-frequency signal) into the cable under test, and calculate the impulse response of the cable under test based on the amplitude difference and angle difference between the input signal and the reflected signal of the cable under test. Due to its simple principle, its dynamic range and fault recognition are higher than those of detection devices based on time domain reflectometry, and it is very easy to be made into detection or monitoring devices. Currently, it has a wide range of applications in cable integrity testing during the construction or maintenance of various long cables including antenna feeders, factory inspections of cable manufacturers, impedance line testing of microwave PCB board planar microstrip circuits, waveguide / filter device debugging, and other fields.

[0003] In the process of implementing the above measurement scheme, there are the following problems in the existing cable fault location based on FDR technology: 1) The test instruments on the current market are easily affected by external noise, reducing the fault recognition. 2) For every 10-fold increase in the high-resolution ranging measurement accuracy on the current market, the working frequency range of the device based on FDR technology needs to be increased by 10 times, which will result in extremely high prices and costs of the test equipment, and it is not suitable for large-scale promotion and use in small and medium-sized enterprises and educational institutions such as universities. 3) The test instruments on the current market need to spend more time on hardware measurement and data acquisition to achieve the required resolution effect and measurement accuracy effect. Summary of the Invention

[0004] Aiming at the deficiencies of the existing technology, the purpose of the present invention is to provide a method for cable fault location based on wavelet transform and inverse chirp Z-transform, which solves the problems in the existing technology.

[0005] The purpose of the present invention can be achieved through the following technical solutions:

[0006] A method for cable fault location based on wavelet transform and inverse chirp Z-transform includes the following steps:

[0007] Measure the reflection parameter data of the ports of the cable under test with a vector network analyzer;

[0008] Denoise the measured reflection parameter data through wavelet transform;

[0009] Convert the denoised reflection parameter data into time-domain impulse response data through inverse chirp Z-transform;

[0010] Convert the time-domain impulse response data into distance-domain impulse response data;

[0011] Take the modulus value of the distance-domain impulse response data to obtain the defect location spectrum of the cable under test for faults.

[0012] Further, the specific steps of wavelet transform denoising include:

[0013] Decompose the reflection parameter data through wavelet transform;

[0014] Process the decomposed high-frequency coefficients with an improved threshold denoising function, and do not process the low-frequency coefficients;

[0015] Based on the low-frequency coefficients and the processed high-frequency coefficients, reconstruct through inverse wavelet transform to obtain the denoised data.

[0016] Further, the formula of wavelet transform is:

[0017]

[0018] where j represents the scaling parameter, k represents the k-th level of waveform analysis, N represents the number of data sampling points, the DWT(j,k) function represents taking the transformation coefficient at the j-th position of the k-th layer for the current data, and Ψ j,k (t) is the wavelet basis function at the j-th position of the k-th layer, and ω(t) j,k represents the final coefficient of the current wavelet transform.

[0019] Further, the improved threshold denoising function is:

[0020]

[0021] where |ω(t) j,k | is the j-th coefficient at the k-th level of waveform analysis, λ k is the threshold of the k-th layer of wavelet decomposition, and λ is the general threshold.

[0022] Further, the formula of the inverse chirp Z-transform is:

[0023]

[0024] z k = AW -k , k = 0, 1, …, M - 1

[0025]

[0026] Among them, ICZT(z) represents the result of the inverse chirp Z-transform, and the conj(·) function represents taking the conjugate of the current parameter; N represents the number of data sampling points, x(n) represents a finite-length sequence containing N data sampling points, and z k are M points evenly distributed on a spiral in the Z-plane; k represents the serial number of the points distributed on the spiral in the Z-plane, and M represents the total number of points distributed on the spiral in the Z-plane; A 0 , θ 0 respectively represent the radius and phase of the starting position in the Z-plane, φ 0 is the equal division angle between the sampling points on the spiral, W 0 is the stretching rate of the spiral, and A and W represent the radius and phase of the actual points in the Z-plane.

[0027] Furthermore, the formula for converting the time-domain impulse response data into the distance-domain impulse response data is:

[0028] Dist = T × C × V p

[0029] Among them, Dist represents the distance sequence of the inverse chirp Z-transform, T represents the time sequence of the inverse chirp Z-transform, C represents the speed of light, and V p represents the velocity factor of the cable with the fault to be measured.

[0030] Furthermore, the formula for the spectral sequence of the defect location spectrum of the cable with the fault to be measured is:

[0031] CableMag = 20 * log10(|ICZT(z)|)

[0032] Among them, CableMag represents the logarithmic spectral sequence of the defect location spectrum of the cable with the fault to be measured.

[0033] A cable fault location system based on wavelet transform and inverse chirp Z-transform includes:

[0034] Data measurement module: Measuring the reflection parameter data of the ports of the cable with the fault to be measured through a vector network analyzer;

[0035] Data denoising module: Denoising the measured reflection parameter data through wavelet transform;

[0036] Inverse chirp Z-transform module: Converting the denoised reflection parameter data into time-domain impulse response data through inverse chirp Z-transform;

[0037] Time-domain to distance-domain module: Converting the time-domain impulse response data into distance-domain impulse response data;

[0038] And, a location module: Taking the modulus value of the distance-domain impulse response data to obtain the defect location spectrum of the cable with the fault to be measured.

[0039] A computer storage medium stores a readable program which, when running, can execute the above-mentioned cable fault location method based on wavelet transform and inverse chirp Z-transform.

[0040] An electronic device includes: a processor, a memory, a communication interface, and a communication bus. The processor, the memory, and the communication interface complete mutual communication through the communication bus;

[0041] The memory is used to store at least one executable instruction, and the executable instruction causes the processor to execute the operations corresponding to the above-mentioned cable fault location method based on wavelet transform and inverse chirp Z-transform.

[0042] Advantages of the present invention:

[0043] 1. The method and device of the present invention denoise the obtained reflection parameter data through wavelet transform, further improving the quality and reliability of the signal.

[0044] 2. The wavelet transform of the present invention is a multi-scale analysis method, which can analyze cable signals at different scales. This multi-scale analysis ability makes wavelet transform have good applicability in cable fault detection and can capture fault characteristics at different scales.

[0045] 3. The present invention converts the input data into high-precision time-domain impulse response data through inverse chirp Z-transform to obtain more accurate real impedance information of the cable under test.

[0046] 4. The present invention does not need to purchase expensive test instruments, and can improve the accuracy and resolution of measurement results only on the basis of existing instruments, reducing the production cost of manufacturers and the usage cost of users.

[0047] 5. The present invention has the characteristics of strong anti-noise ability and high dynamic range. The fault location has extremely high distance resolution, and the ranging distance resolution is increased by at least one order of magnitude. Description of the Drawings

[0048] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, for those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0049] Figure 1 It is a flowchart of the cable fault location method based on wavelet transform and inverse chirp Z-transform of the present invention;

[0050] Figure 2It is the flowchart of wavelet denoising in the present invention;

[0051] Figure 3 It is the comparison diagram of the actual effects measured by the method based on wavelet transform and inverse chirp Z transform and the conventional method in the present invention. Specific implementation mode

[0052] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without making creative efforts belong to the scope of protection of the present invention.

[0053] Embodiment 1

[0054] As Figure 1 shown, the cable fault location method based on wavelet transform and inverse chirp Z transform includes the following steps:

[0055] S1. Measure the reflection parameter data of the port of the cable with the fault to be measured through a vector network analyzer;

[0056] S2. Denoise the obtained reflection parameter data through wavelet transform;

[0057] To eliminate the interference signal and noise signal on the reflection parameter and obtain clean and real reflection parameter data, the reflection parameter data is denoised through wavelet transform. The denoising process is as Figure 2 shown and includes the following steps:

[0058] S21. Decompose the reflection parameter data through wavelet transform;

[0059] The wavelet basis function can adjust its characteristics in the time domain and frequency domain through scale transformation and translation transformation; it can be represented by the following formula:

[0060]

[0061] Among them, a represents the scale parameter and b represents the translation parameter;

[0062] The basic formula of discrete wavelet transform

[0063]

[0064] Among them, j represents the scaling parameter, k represents the kth level of waveform analysis, N represents the number of data sampling points, the DWT(j,k) function represents taking the transformation coefficient at the jth place of the kth layer for the current data, and ω(t) j,k represents the final coefficient of the current wavelet transform.

[0065] S22. Process the decomposed high-frequency coefficients with the improved threshold denoising function, and do not process the low-frequency coefficients.

[0066] In the result of wavelet transform, in order to remove noise and interference signals and retain the effective information of the signal, it is necessary to perform threshold processing on the wavelet coefficients. The wavelet coefficients after threshold processing are inversely transformed to obtain the denoised signal, improving the quality and reliability of the signal.

[0067] Improved threshold function:

[0068]

[0069] where |ω(t) j,k | is the k-th level and j-th coefficient of waveform analysis, and λ k is the threshold of the k-th layer of wavelet decomposition, and λ is the general threshold.

[0070] This improved threshold function can adaptively adjust the threshold according to the characteristics of the signal and the noise level; it has non-linear characteristics, can more flexibly suppress noise and retain the important features of the signal, and improve the denoising effect. Inverse transformation of the wavelet coefficients after threshold processing can obtain the denoised data.

[0071] S23. Based on the low-frequency coefficients and the processed high-frequency coefficients, perform reconstruction through inverse wavelet transform to obtain the denoised data. Inverse transformation of the wavelet coefficients after threshold processing can obtain the denoised data.

[0072] S3. Convert the denoised reflection parameter data into high-precision time-domain impulse response data through inverse chirp Z transform;

[0073] Frequency-domain to time-domain operations often use FFT to implement circular convolution operations. However, due to the influence of the inherent fence effect of the FFT algorithm, the analysis accuracy will be greatly limited. Although the resolution can be improved by increasing the sampling time and the number of data samples, it will cause the problem of slow data refreshing of the measuring instrument, which is unacceptable for measurement sites with high real-time requirements. By using inverse chirp Z transform, the resolution of the result can be improved without increasing the sampling time.

[0074] The formula for inverse chirp Z transform is as follows:

[0075]

[0076] where N represents the number of data sampling points, x(n) represents a finite-length sequence containing N data sampling points, z k is M points evenly distributed on a spiral line in the Z plane, and X(z) is the result after inverse chirp Z transform.

[0077]

[0078] Among them, A 0 , θ 0 respectively represent the radius and phase of the starting position in the Z-plane, and φ 0 is the equal division angle between the sampling points on the helix, and W 0 is the expansion and contraction rate of the helix. A and W represent the radius and phase of the actual points in the Z-plane.

[0079] M points z evenly distributed on a helix in the Z-plane can be obtained k :

[0080] z k = AW -k , k = 0, 1,..., M - 1

[0081] Among them, k represents the serial number of the points distributed on the helix in the Z-plane, and M represents the total number of points distributed on the helix in the Z-plane, which is also the number of data output points.

[0082] Thus, it can be obtained that

[0083]

[0084] From this, the inverse chirp Z-transform formula can be obtained:

[0085]

[0086] Among them: ICZT(z) represents the result of the inverse chirp Z-transform, and the conj(·) function represents taking the conjugate of the current parameter. Inputting the denoised reflection parameter data into the inverse chirp Z-transform formula, the inverse transform result can be obtained.

[0087] The time resolution R of the inverse chirp Z-transform ICZT is:

[0088]

[0089] It can be seen from the above formula that by increasing the number of M points so that the number of M points is greater than the number of data sampling points N, the effect of interpolation can be achieved, that is, extremely high time resolution can be achieved.

[0090] S4. Convert the time-domain impulse response data into range-domain impulse response data;

[0091] The time-domain to range-domain formula is as follows:

[0092] Dist = T × C × V p

[0093] Where: Dist represents the distance sequence of the inverse chirp Z-transform, T represents the time sequence of the inverse chirp Z-transform, C represents the speed of light, and V p represents the velocity factor of the cable with a fault to be measured.

[0094] S5. Take the modulus value of the impulse response data in the distance domain to obtain the defect location spectrum of the cable with a fault to be measured;

[0095] The formula for the spectrum sequence of the defect location spectrum of the cable with a fault to be measured finally is as follows:

[0096] Cab leMag = 20 * log10(|ICZT(z)|)

[0097] Where, Cab leMag represents the logarithmic spectrum sequence of the defect location spectrum of the cable with a fault to be measured, and ICZT(z) represents the result of the inverse chirp Z-transform.

[0098] Display the spectrum sequence Cab l eMag of the defect location spectrum of the cable with a fault to be measured on the vertical axis of the rectangular coordinate system of the display screen, and display the distance sequence Dist of the inverse chirp Z-transform on the horizontal axis of the rectangular coordinate system of the display screen, then the final high-precision cable fault location measurement result based on wavelet transform and inverse chirp Z-transform can be obtained. The area with the maximum peak of the spectral line is the area with the most serious cable fault.

[0099] Based on a similar inventive concept, an embodiment of the present invention further provides a computer storage medium storing a readable program, which can execute the above-mentioned cable fault location method based on wavelet transform and inverse chirp Z-transform when the program runs.

[0100] Based on a similar inventive concept, an embodiment of the present invention provides an electronic device, including: a processor, a memory, a communication interface, and a communication bus. The processor, the memory, and the communication interface complete mutual communication through the communication bus;

[0101] The memory is used to store at least one executable instruction, and the executable instruction enables the processor to execute the operations corresponding to the above-mentioned cable fault location method based on wavelet transform and inverse chirp Z-transform.

[0102] Based on a similar inventive concept, an embodiment of the present invention further provides a computer program product including computer instructions, and the computer instructions direct a computing device to execute the operations corresponding to the above-mentioned cable fault location method based on wavelet transform and inverse chirp Z-transform.

[0103] Embodiment 2

[0104] In this embodiment, a comparative test is carried out on the fault location method of the present invention and a conventional method. The test steps are as follows:

[0105] Step 1, measuring reflection parameter data of the faulty cable port to be tested by a vector network analyzer, wherein the number of reflection parameter data sampling points is N;

[0106] Step 2, directly perform IFFT operation on the S parameter to obtain the operation result A1, perform logarithmic amplitude operation on the result to obtain the final result E1, where E1 = 20*log10(|A1|);

[0107] Step 3, performing wavelet transform on the reflection parameter data of the faulty cable port to be tested measured by the vector network analyzer to decompose the data, and obtaining the coefficient A2 after wavelet transform;

[0108] Step 4, the high-frequency coefficients in the coefficient A2 are filtered by the improved threshold denoising function, and the low-frequency coefficients are not processed, to obtain the result B2, and the inverse wavelet transform is performed on B2 to obtain the denoised S parameter data C2;

[0109] Step 5, convert the S parameter data C2 into time domain impulse response data D2 through linear frequency modulation Z inverse transform, and obtain M output data lengths; by setting the value of the parameter M of the linear frequency modulation Z inverse transform so that M>N, it is equivalent to interpolating the input data C2;

[0110] Step 6, perform logarithmic amplitude operation on the time domain impulse response data D2 to obtain the final result E2 after the defect location spectrum is refined, where E2 = 20*log10(|D2|).

[0111] Depend on Figure 3 As shown, the results after processing based on wavelet transform and linear frequency modulation Z inverse transform method, where the blue solid line is the result E1 measured by the conventional method, and the red dotted line is the result E2 measured by the method of this embodiment. The actual distance of the fault point of the cable to be tested measured by a ruler is 0.813 meters (including the length of the adapter connector). It can be seen that the fault location method of the present invention has higher measurement resolution, noise resistance and higher dynamic range than the conventional method.

[0112] Example 3

[0113] Based on the cable fault location method based on wavelet transform and linear frequency modulation Z inverse transform mentioned in Example 1, in this embodiment, a cable fault location system based on wavelet transform and linear frequency modulation Z inverse transform is proposed, which specifically includes:

[0114] Data measurement module: measures the reflection parameter data of the faulty cable port to be tested through a vector network analyzer;

[0115] Data denoising module: denoises the measured reflection parameter data through wavelet transform;

[0116] Inverse chirp Z-transform module: converting the denoised reflection parameter data into time-domain impulse response data through inverse chirp Z-transform;

[0117] Time-domain to range-domain module: converting the time-domain impulse response data into range-domain impulse response data;

[0118] And a positioning module: taking the modulus value of the range-domain impulse response data to obtain the defect positioning spectrum of the cable under test for faults.

[0119] The method of the present invention can be implemented in hardware, firmware, or be implemented as software or computer code that can be stored in a recording medium (such as a CDROM, RAM, floppy disk, hard disk, or magneto-optical disk), or be implemented as computer code originally stored in a remote recording medium or a non-transitory machine-readable medium and downloaded through a network and to be stored in a local recording medium, so that the method described herein can be stored in such software processing on a recording medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware (such as an ASIC or FPGA). It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component (such as RAM, ROM, flash memory, etc.) that can store or receive software or computer code, and when the software or computer code is accessed and executed by the computer, the processor, or the hardware, the method described herein is implemented. In addition, when a general-purpose computer accesses the code for implementing the method shown herein, the execution of the code converts the general-purpose computer into a dedicated computer for executing the method shown herein.

[0120] The above shows and describes the basic principles, main features, and advantages of the present invention. Those skilled in the art of this industry should understand that the present invention is not limited by the above embodiments. The above embodiments and the descriptions in the specification only illustrate the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed.

Claims

1. A cable fault location method based on wavelet transform and linear frequency modulation Z inverse transform, characterized in that: The following steps are involved: Measure the reflection parameter data of the faulty cable port to be tested by a vector network analyzer; The measured reflection parameter data are denoised by wavelet transform; The denoised reflection parameter data is converted into time domain impulse response data through linear frequency modulation Z inverse transform; Converting time domain impulse response data into distance domain impulse response data; The modulus of the distance domain pulse response data is taken to obtain the defect location spectrum of the faulty cable to be tested.

2. The cable fault location method based on wavelet transform and linear frequency modulation Z inverse transform according to claim 1 is characterized in that: The specific steps of wavelet transform denoising include: Decompose the reflection parameter data by wavelet transform; The decomposed high-frequency coefficients are processed by the improved threshold denoising function, and the low-frequency coefficients are not processed; Based on the low-frequency coefficients and the processed high-frequency coefficients, the denoised data can be obtained by reconstructing through inverse wavelet transform.

3. The cable fault location method based on wavelet transform and linear frequency modulation Z inverse transform according to claim 2 is characterized in that: The formula for wavelet transform is: Where j represents the scaling parameter, k represents the kth level of waveform analysis, N represents the number of data sampling points, and the DWT(j,k) function represents the transformation coefficient of the kth layer and the jth position of the current data. j,k (t) is the wavelet basis function at the jth position in the kth layer, ω(t) j,k Represents the final coefficients of the current wavelet transform.

4. The cable fault location method based on wavelet transform and linear frequency modulation Z inverse transform according to claim 3 is characterized in that: The improved threshold denoising function is: Among them, |ω(t) j,k | is the kth level jth coefficient of waveform analysis, λ k is the threshold of the kth layer of wavelet decomposition, and λ is the universal threshold.

5. The cable fault location method based on wavelet transform and linear frequency modulation Z inverse transform according to claim 1, characterized in that: The linear frequency modulation Z inverse transform formula is: z k =AW -k ,k=0,1,…,M-1 Where ICZT(z) represents the result of the inverse linear frequency modulation Z transform, the conj(·) function represents the conjugation of the current parameter, N represents the number of data sampling points, x(n) represents a finite length sequence containing N data sampling points, and z k are M points evenly distributed on a spiral line in the Z plane; k represents the serial number of the points distributed on the spiral line in the Z plane, and M represents the total number of points distributed on the spiral line in the Z plane; A0 and θ0 represent the radius and phase of the starting position of the Z plane respectively, φ0 is the bisector angle between the sampling points on the spiral line, W0 is the expansion and contraction rate of the spiral line, and A and W represent the radius and phase of the actual point in the Z plane.

6. The cable fault location method based on wavelet transform and linear frequency modulation Z inverse transform according to claim 1, characterized in that: The formula for converting time domain impulse response data into distance domain impulse response data is: Dist=T×C×V p Where Dist represents the distance sequence of the linear frequency modulation Z inverse transform, T represents the time sequence of the linear frequency modulation Z inverse transform, C represents the speed of light, V p Indicates the velocity factor of the faulty cable to be tested.

7. The cable fault location method based on wavelet transform and linear frequency modulation Z inverse transform according to claim 5, characterized in that: The defect location spectrum sequence formula of the faulty cable to be tested is: CableMag=20*log10(|ICZT(z)|) Wherein, CableMag represents the logarithmic spectrum sequence of the defect location spectrum of the faulty cable to be tested.

8. A cable fault location system based on wavelet transform and linear frequency modulation Z inverse transform, characterized in that: include: Data measurement module: measures the reflection parameter data of the faulty cable port to be tested through a vector network analyzer; Data denoising module: denoises the measured reflection parameter data through wavelet transform; Linear frequency modulation Z inverse transform module: converts the denoised reflection parameter data into time domain impulse response data through linear frequency modulation Z inverse transform; Time domain to distance domain module: convert time domain impulse response data into distance domain impulse response data; And, the positioning module: takes the modulus value of the distance domain pulse response data to obtain the defect positioning spectrum of the faulty cable to be tested.

9. A computer storage medium storing a readable program, characterized in that: When the program is running, the cable fault location method based on wavelet transform and linear frequency modulation Z inverse transform as described in any one of claims 1 to 7 can be executed.

10. An electronic device, characterized in that: include: A processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other via the communication bus; The memory is used to store at least one executable instruction, and the executable instruction enables the processor to perform operations corresponding to the cable fault location method based on wavelet transform and linear frequency modulation Z inverse transform according to any one of claims 1 to 7.

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