Portable GIS equipment microwave non-contact vibration measurement method and device

Abstract

The present application relates to a kind of portable GIS equipment microwave non-contact vibration measurement method and device, method includes: transmitting linear frequency modulation continuous wave and receiving echo signal, then data pre-processing is carried out;Identify the vibration target in the echo signal after pre-processing, then the vibration target is analyzed using angle estimation algorithm, and angle dimension information is obtained;Based on the angle dimension information, coordinates are calculated, point cloud data is obtained, and clustering algorithm is used to the target classification of the point cloud data;For each target after classification, using phase estimation algorithm, extract its phase information;Based on the phase information, using arctangent demodulation method restores vibration waveform, generates vibration signal.Compared with prior art, the device of the present application is portable and easy to use, and can quickly and efficiently scan the field GIS equipment for global vibration measurement and diagnosis, greatly improving the detection efficiency.

Description

Technical Field

[0001] This invention relates to the field of power equipment condition monitoring technology, and in particular to a portable microwave non-contact vibration measurement method and device for GIS equipment, especially a portable integrated testing instrument that integrates microwave radar vibration measurement, inertial attitude compensation and intelligent diagnostic functions. Background Technology

[0002] Power equipment such as sulfur hexafluoride gas-insulated switchgear (GIS) often suffers from potential failures due to its complex mechanical structure and abnormal vibrations during operation, which can lead to equipment damage or even system power outages in severe cases. Currently, vibration monitoring of GIS equipment mainly relies on contact sensors (such as piezoelectric or fiber optic sensors), which suffers from problems such as complex installation, high cost, limited monitoring points, and susceptibility to environmental influences, making it difficult to achieve real-time vibration monitoring over a large area and at multiple measurement points.

[0003] In recent years, non-contact vibration measurement technologies have gradually attracted attention, such as laser Doppler vibrometers and computer vision methods. However, they still have limitations such as narrow beam width, single-point measurement capability, susceptibility to light interference, and complex image processing. Millimeter-wave radar, with its advantages of high resolution, wide field of view, sensitivity to micro-motions, strong penetration, and strong resistance to environmental interference, has become a potential technical means to achieve non-contact, full-field, and multi-point vibration measurement.

[0004] Although millimeter-wave radar has seen initial applications in fields such as automobiles and medical devices, research on micro-vibration detection in power equipment, especially GIS (Gas Insulated Switchgear) enclosures, remains relatively scarce. The normal vibration amplitude of GIS equipment typically ranges from 0.5 to 50 μm, with a frequency distribution between 10 Hz and 2 kHz. Traditional millimeter-wave radar systems still face challenges in terms of accuracy, multi-target resolution, and extraction of complex vibration signals. Therefore, there is an urgent need to develop a portable, high-precision, non-contact vibration measurement device and method specifically for GIS equipment to achieve rapid and accurate vibration monitoring and fault diagnosis at multiple points on the equipment surface.

[0005] The invention disclosed in CN111609920A is a handheld microwave vibration measurement system, including an indication and positioning module, a microwave radar transceiver module, a control module, a signal acquisition and processing module, a shake-stabilization module, a display and data storage module, and a power supply module. The power supply module provides power. The microwave radar transceiver module generates and transmits single-frequency continuous wave microwave signals and receives electromagnetic echoes scattered by the target to obtain a zero-IF baseband signal. The signal acquisition and processing module acquires the zero-IF baseband signal output by the microwave radar transceiver module and extracts and analyzes vibration information. The control module controls the system's start and stop, sets parameters, controls the operation of each module, and transmits data. The shake-stabilization module eliminates the influence of hand-held shaking on the measurement results. The indication and positioning module assists in indicating and locating the vibration test target and / or measurement point. This invention solves the technical problems of existing vibration measurement instruments, such as poor portability, large size and power consumption, high cost, high testing environment requirements, and limited applicability. However, this device has weak multi-target resolution and poor measurement accuracy. Summary of the Invention

[0006] The purpose of this invention is to overcome the defects of the prior art by providing a portable microwave non-contact vibration measurement method and device for GIS equipment. This device is based on a frequency modulated continuous wave (FMCW) millimeter-wave radar system and can realize non-contact, full-field, multi-point synchronous measurement of the vibration of the casing of power equipment such as gas-insulated switchgear (GIS). It has the advantages of high precision, high sensitivity, and strong portability.

[0007] The objective of this invention can be achieved through the following technical solutions: A portable microwave non-contact vibration measurement method for GIS equipment, used to test the vibration signal of GIS equipment, includes: Step 1: Transmit a linear frequency modulated continuous wave and receive the echo signal, then perform data preprocessing; Step 2: Identify the vibration target in the preprocessed echo signal, then use an angle estimation algorithm to analyze the vibration target and obtain angle dimension information; calculate the coordinates based on the angle dimension information to obtain point cloud data, and use a clustering algorithm to classify the target in the point cloud data; Step 3: For each classified target, use a phase estimation algorithm to extract its phase information; based on the phase information, use the arctangent demodulation method to restore the vibration waveform and generate a vibration signal.

[0008] Furthermore, the process of classifying the point cloud data using clustering algorithms specifically includes: First, initialize the cluster label of all data points to "unclassified" and the cluster number to 0. Then, iterate through the point cloud data: For each unclassified point found, it is marked as classified, and the number of points in its neighborhood p is compared with a pre-set minimum neighborhood point threshold. If the number is less than the minimum neighborhood point threshold, it is marked as a noise point, and the process continues to the next point. If the number is greater than the minimum neighborhood point threshold, it is marked as a core point, and a new cluster is created. All points in the point cloud data neighborhood p are added to the new cluster. The process continues to traverse all points in the neighborhood p, and for each data point found: If a data point is not visited, mark it as visited; search whether the number of points in the neighborhood q of the data point is greater than the minimum neighborhood point threshold; if it is greater, add all points in the neighborhood q of the data point to the new cluster; if it is less, mark the data point as a noise point and continue to traverse the next point.

[0009] Furthermore, the data preprocessing process specifically includes: First, the echo signal is mixed, filtered, and sampled by an ADC to obtain an intermediate frequency signal; then, the intermediate frequency signal is subjected to distance-dimensional spectrum analysis, and the spectrum is subdivided by linear frequency modulation Z-transform; The mean cancellation algorithm is used to filter out static clutter, and the signal-to-noise ratio is improved by incoherent accumulation.

[0010] Furthermore, step two also includes: after obtaining the point cloud data, selecting either a beam scanning method or a clustering algorithm to classify the point cloud data into targets, based on different measurement requirements.

[0011] Furthermore, step two also includes: A two-dimensional adaptive constant false alarm rate (CFAR) algorithm is used to identify potential vibration targets in the range-Doppler spectrum or range-angle spectrum. The identification process specifically includes: statistically analyzing the background energy of the reference cells surrounding the unit to be detected and determining the detection threshold; comparing the energy of the unit to be detected with the detection threshold to identify potential vibration targets. The angle estimation algorithm is specifically a multiple signal classification algorithm or a beamforming algorithm.

[0012] Furthermore, the clustering algorithm is a density-based noise-based spatial clustering algorithm; the density-based noise-based spatial clustering algorithm clusters data points based on the neighborhood distance between each data point in the point cloud data and the number of sample points in the neighborhood, dividing data points that meet the preset density connectivity conditions into the same target point cloud cluster, and determining discrete data points that do not meet the density connectivity conditions as noise points.

[0013] Furthermore, step three also includes: For each classified target, the maximum likelihood estimation method is used to reconstruct the vibration waveform and generate a vibration signal; After generating the vibration signal, the vibration signal is analyzed and defect identified based on the vibration feature database, and finally a test report containing the vibration distribution is generated.

[0014] The present invention also provides an apparatus for a portable GIS device microwave non-contact vibration measurement method as described in any of the above descriptions, comprising: Microwave transceiver circuit: used to generate frequency modulated continuous wave signals and receive echo signals; Antenna array: connected to the microwave transceiver circuit, used for transmitting signals and receiving echoes; Embedded processor: used to control the microwave transceiver circuit and process the echo signal, and calculate vibration information; Battery: Used to power the entire device; Touchscreen display: Used for human-computer interaction and displaying the final test report.

[0015] Furthermore, the device also integrates an inertial measurement unit for measuring the device's own attitude and jitter; the embedded processor uses an internal algorithm to compensate for measurement errors caused by hand-held jitter in real time based on the data from the inertial measurement unit.

[0016] Furthermore, the embedded processor has built-in various GIS models and typical measurement point distribution maps, which are displayed on the touch screen to guide the user to align the device and perform measurements.

[0017] Compared with the prior art, the present invention has the following advantages: (1) This invention transmits linear frequency modulated continuous waves and receives echo signals through millimeter-wave radar; targets are identified by the echo signals, and angle estimation algorithm, point cloud clustering algorithm and vibration extraction algorithm are combined to realize target classification of point cloud data in multi-target scenes and measurement of vibration distribution in different areas. This enables rapid and accurate vibration monitoring and fault diagnosis of multiple points on the surface of GIS equipment, and ensures that the device still has good accuracy and complex vibration signal extraction effect when performing multi-target discrimination.

[0018] (2) This invention designs a microwave non-contact vibration measurement device that includes an FMCW radar core board, a MIMO antenna array, an embedded processing unit, a battery module, and a display touch screen. All the devices are integrated into a carrying case. The device is small in size, light in weight, and low in power consumption. It can be used by hand or mounted on a bracket. It is suitable for various scenarios such as substations and laboratories. It can also be widely used for mechanical condition monitoring and early fault diagnosis of high-voltage equipment such as GIS disconnect switches and circuit breakers. It has good engineering application prospects.

[0019] (3) This invention utilizes a compact MIMO antenna layout and MIMO virtual aperture technology to design an antenna array that achieves high angular resolution within a limited aperture and miniaturized physical size. Furthermore, the device incorporates a high-precision inertial measurement unit to compensate for the impact of handheld operation on measurements, significantly improving measurement accuracy and precision through noise filtering. With a built-in vibration characteristic database of various typical GIS models and an automatic analysis APP, the device guides users through measurements and generates diagnostic reports instantly and automatically. This invention's device is lightweight and easy to use, enabling rapid and efficient full-area scanning vibration measurement and diagnosis of on-site GIS equipment, greatly improving detection efficiency.

[0020] (4) This invention can adapt to the needs of large surface measurement. The system supports beam scanning mode and controls the transmitting antenna beam to point to a specific angle area through phase encoding, realizing the measurement of vibration distribution in different areas. The system can also pre-store a variety of typical fault vibration feature libraries and support automatic alarm and fault type identification. This invention can be widely used in the mechanical condition monitoring and early fault diagnosis of high voltage equipment such as GIS disconnect switches and circuit breakers. It has the advantages of convenient installation, high measurement efficiency and strong anti-interference ability, and is suitable for on-site operation and maintenance and laboratory research. Attached Figure Description

[0021] Figure 1 This is a flowchart of a microwave non-contact vibration measurement method for a portable GIS device provided in an embodiment of the present invention; Figure 2 This is a time-frequency domain waveform of a linear frequency modulated wave from a microwave non-contact vibration measurement method for a portable GIS device provided in an embodiment of the present invention. Figure 3 This is a diagram of the FMCW millimeter-wave radar signal model of a portable GIS device microwave non-contact vibration measurement method provided in an embodiment of the present invention. Figure 4 This is a simplified flowchart of a microwave non-contact vibration measurement method for a portable GIS device provided in an embodiment of the present invention; Figure 5 This is a flowchart of the DBSCAN algorithm for a portable GIS device microwave non-contact vibration measurement method provided in an embodiment of the present invention; Figure 6 This is a DBSCAN simulation result diagram of a portable GIS device microwave non-contact vibration measurement method provided in an embodiment of the present invention; Figure 7 This is a schematic diagram of the beam scanning mode of a microwave non-contact vibration measurement method for a portable GIS device provided in an embodiment of the present invention; Figure 8This is a beam scanning simulation result diagram of a microwave non-contact vibration measurement method for a portable GIS device provided in an embodiment of the present invention; Figure 9 This is a right-side pulse vibration extraction result image of a microwave non-contact vibration measurement method for portable GIS equipment provided in an embodiment of the present invention; Figure 10 This is an image showing the intermediate pulse vibration extraction result of a microwave non-contact vibration measurement method for a portable GIS device provided in an embodiment of the present invention. Figure 11 This is a diagram showing the results of extracting complex vibrations from the center of a microwave non-contact vibration measurement method for a portable GIS device provided in this embodiment of the invention. Figure 12 This is a diagram showing the complex vibration extraction results of a microwave non-contact vibration measurement method for a portable GIS device provided in this embodiment of the invention, with the vibration source on the left. Figure 13 This is a radar system scanning diagram of a microwave non-contact vibration measurement method for a portable GIS device provided in an embodiment of the present invention. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0023] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0024] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0025] Definitions: Two-dimensional adaptive target detection (CFAR) algorithm: This is a core signal processing technique for automatically identifying real targets in the two-dimensional data plane of radar or sonar. The algorithm adaptively estimates the statistical characteristics of local background clutter and noise within a two-dimensional reference window centered on the target cell, and dynamically adjusts the detection threshold multiplier according to environmental changes. Its core lies in the "constant false alarm rate" characteristic, meaning that under non-uniform and non-stationary complex backgrounds (such as clutter edges and multi-target interference), a constant false alarm probability is maintained through variable parameter mechanisms (such as variable exponents, ordered statistics, or model selection). This allows for the stable extraction of real target points in the range-Doppler spectrum or range-angle spectrum while filtering out background interference, providing reliable detection results for subsequent tracking and identification.

[0026] Vibration extraction algorithms based on maximum likelihood estimation (ML) are statistically optimal methods for accurately estimating the micro-vibration parameters (such as frequency, amplitude, and phase) of a target from noisy observation signals. The core idea is to model the vibration process of the target as a sinusoidal or polynomial phase signal modulated by additive noise. By constructing a likelihood function of the observation data and finding the combination of vibration parameters that maximizes this function, the vibration characteristics can be extracted.

[0027] Spectrum subdivision algorithm: This is a high-precision frequency estimation technique designed to overcome the frequency resolution limitations caused by the picket fence effect in traditional Fourier transform. Its core idea is to obtain a rough frequency estimate through conventional spectrum analysis of the signal, and then use specific mathematical methods to perform fine interpolation or fitting on the local spectrum near the target frequency point. This allows for the calculation of the precise frequency, amplitude, and phase of the signal components with an accuracy far below one frequency resolution (typically reaching one percent or higher).

[0028] Incoherent accumulation is a signal processing technique that improves the signal-to-noise ratio (SNR) and stability of detection by directly superimposing multiple observed signals in the amplitude or power domain. Its core principle is to directly average the amplitude, power, or detection statistics of multiple independent observation samples (such as multiple pulses, multiple frames of data, or multiple channels) after alignment or compensation, rather than using their complete phase information for coherent superposition. This method has extremely low requirements for phase stability between observation units and can effectively combat random phase fluctuations caused by target flicker, platform jitter, or changes in the propagation environment, exhibiting strong robustness. However, because it does not utilize phase information, its SNR improvement efficiency is lower than that of coherent accumulation (theoretically, the gain of incoherent accumulation is proportional to the square root of the number of accumulations, while that of coherent accumulation is proportional to the number of accumulations).

[0029] Arctangent demodulation is a high-precision phase extraction technique used for orthogonally modulated signals (such as frequency-modulated continuous wave radar and laser interferometers). This method fully utilizes the orthogonality of the I / Q channels, effectively suppressing the influence of amplitude fluctuations on phase extraction, and achieving demodulation with high linearity and strong anti-interference capabilities. The calculated wrapped phase needs to be recovered using a phase unwrapping algorithm to recover the continuous phase, thereby deriving target information (such as distance, displacement, and velocity).

[0030] Maximum likelihood estimation is a parameter estimation criterion based on a probabilistic framework. Its core idea is to find the unknown parameter values ​​that maximize the probability of the occurrence of that data (i.e., the likelihood function), given a set of observed data. This method transforms the parameter estimation problem into an optimization problem, obtaining the optimal parameter estimate by solving the derivative of the likelihood function with respect to the parameters and setting it to zero (finding the extremum), or by directly using numerical optimization algorithms.

[0031] Beam scanning strategy refers to the planning and control method of phased array radar or smart antenna systems to control their radiated beams in space through electronic or mechanical means for directional search, tracking, or coverage. Its core objective is to optimize the beam allocation sequence in azimuth, elevation, and time dimensions under the constraints of limited time and energy resources, so as to achieve the most effective detection, tracking, or communication in the airspace of interest.

[0032] Chirp-Z transform is an efficient algorithm for calculating the Z-transform at equally spaced sampling points on any segment of a spiral arc on a unit circle. Essentially, it calculates a discrete Fourier transform with arbitrary starting frequency and frequency resolution. It is widely used in radar signal processing for frequency domain refinement analysis, accurate estimation of formants in speech signals, and extraction of characteristic frequencies from vibration signals—all requiring high-resolution, localized spectral analysis. It is one of the core tools for spectral refinement analysis.

[0033] The MUSIC algorithm is a high-resolution spectral estimation technique based on signal subspace decomposition, also known as the multiple signal classification algorithm. Its core idea is to utilize the eigenvalue decomposition of the received data covariance matrix to divide the observation space into a signal subspace and a noise subspace, and then construct a spatial spectral function using the orthogonality between the signal direction vector and the noise subspace. By searching for directions that produce peaks in the spectral function, super-resolution estimation of parameters such as angle, frequency, or time delay can be achieved, far exceeding that of traditional Fourier methods.

[0034] Beamforming algorithms are core algorithms used in sensor array signal processing. They aim to enhance the array's radiation pattern into a "beam" in a specific direction by weighting and delaying (or compensating for phase) the received signals of each array element, while suppressing interference and noise in other directions. Essentially, it is a spatial filter that improves the signal-to-noise ratio and spatial resolution through spatial processing.

[0035] Example 1 like Figure 1 As shown, this embodiment provides a microwave non-contact vibration measurement method for portable GIS equipment, which includes the following steps: S1: Transmit linear frequency modulated continuous wave and receive echo signal, then perform data preprocessing; Specifically, the data preprocessing process includes: First, the echo signal is mixed, filtered, and sampled by an ADC to obtain the intermediate frequency signal; then, the intermediate frequency signal is subjected to distance-dimensional spectrum analysis, and the spectrum is subdivided by linear frequency modulation Z-transform; The mean cancellation algorithm is used to filter out static clutter, and the signal-to-noise ratio is improved by incoherent accumulation.

[0036] S2: Identify the vibration target in the preprocessed echo signal, then use an angle estimation algorithm to analyze the vibration target and obtain angle dimension information; calculate the coordinates based on the angle dimension information to obtain point cloud data, and use a clustering algorithm to classify the target in the point cloud data; Specifically, After obtaining the point cloud data, depending on different measurement requirements, either beam scanning or clustering algorithms are selected to classify the target in the point cloud data.

[0037] Specifically, The process of classifying targets in point cloud data using clustering algorithms specifically includes: First, initialize the cluster label of all data points to "unclassified" and the cluster number to 0. Then, iterate through the point cloud data: For each unclassified point found, mark it as classified and compare the number of points in its neighborhood p with a pre-set minimum neighborhood point threshold; if it is less than the minimum neighborhood point threshold, mark it as a noise point and continue traversing to the next point; if it is greater than the minimum neighborhood point threshold, mark it as a core point and create a new cluster; add all points in the point cloud data neighborhood p to the new cluster; continue traversing all points in the neighborhood p, and for each data point found: If a point is not visited, mark it as visited; check if the number of points in the neighborhood q of the search data point is greater than the minimum neighborhood point threshold; if it is greater, add all points in the neighborhood q of the data point to a new cluster; if it is less, mark the data point as a noise point and continue to traverse the next point.

[0038] Preferred, A two-dimensional adaptive constant false alarm rate (CFAR) algorithm is used to identify potential vibration targets in the range-Doppler spectrum or range-angle spectrum; the angle estimation algorithm is specifically a multi-signal classification algorithm or a beamforming algorithm. The clustering algorithm is a density-based spatial clustering algorithm for noise applications.

[0039] S3: For each target after classification, use a phase estimation algorithm to extract its phase information; based on the phase information, use the arctangent demodulation method to restore the vibration waveform and generate a vibration signal.

[0040] Specifically, For each classified target, the maximum likelihood estimation method is used to reconstruct the vibration waveform and generate a vibration signal; After generating the vibration signal, the system performs feature analysis and defect identification on the vibration signal based on the vibration feature database, and finally generates an inspection report containing the vibration distribution.

[0041] Example 2 This embodiment provides a portable GIS device for microwave non-contact vibration measurement as described in any of Embodiment 1. The device is an integrated portable structure and integrates: Microwave transceiver circuit: used to generate FMCW signals and receive echoes; Antenna array: Connected to microwave transceiver circuitry, used for transmitting signals and receiving echoes; Embedded processor: Used to control microwave transceiver circuits and process echo signals, and calculate vibration information; Battery: Powers the entire device; Touchscreen display: Used for human-computer interaction and result display.

[0042] The device, integrated into a carrying case, mainly includes a millimeter-wave radar module, a signal processing module, a data acquisition and transmission module, a power supply module, and a human-machine interface. The millimeter-wave radar module employs a multiple-input multiple-output (MIMO) antenna array, operating in the 77–81 GHz frequency band, and possesses high-resolution angle and distance sensing capabilities. The signal processing module integrates high-precision ranging algorithms, a two-dimensional adaptive constant false alarm rate (CFAR) target detection algorithm, and a vibration extraction algorithm based on maximum likelihood estimation (ML), enabling real-time processing and feature extraction of vibration signals.

[0043] This embodiment also provides a specific portable microwave non-contact vibration measurement device for GIS equipment. The device includes an AWR1843 millimeter-wave radar sensor, a DCA1000 data acquisition board, an embedded processing unit (such as an ARM Cortex-A series processor), a lithium battery power supply module, and a touchscreen display interface. The radar sensor is connected to the data acquisition board via an LVDS interface, and the acquisition board transmits data in real time to a host computer or embedded processing unit for processing via gigabit Ethernet.

[0044] During measurement, the device is aimed at the surface of the GIS equipment under test. After the system is started, the radar begins to emit linear frequency modulated (LFM) waves in the 77–81 GHz band. The echo signal is mixed, filtered, and sampled by an ADC to obtain the intermediate frequency (IF) signal. The signal processing unit first performs a range-dimensional FFT on each chirp and then performs spectral subdivision using a chirp-Z transform to improve the range resolution to the millimeter level. Next, a mean cancellation algorithm is used to filter out static clutter, and incoherent accumulation is used to improve the signal-to-noise ratio.

[0045] In the target detection stage, a two-dimensional adaptive CFAR algorithm is used to identify potential vibrating targets in the range-Doppler spectrum or range-angle spectrum. For multi-target scenarios, high-precision angle estimation is further performed using the MUSIC algorithm or beamforming algorithm to generate point cloud data, and target classification is achieved using the DBSCAN clustering algorithm.

[0046] During the vibration extraction stage, the phase information of each identified target is extracted, and the vibration waveform is reconstructed using either the arctangent demodulation method or the maximum likelihood estimation method. The vibration signal can be displayed in real time on the touchscreen or exported via USB or wirelessly for subsequent analysis.

[0047] To meet the needs of large surface area measurements, the system supports beam scanning mode, using phase encoding to control the transmitting antenna beam to point to a specific angle region, enabling the measurement of vibration distribution in different areas. The system can also pre-store various typical fault vibration characteristic libraries, supporting automatic alarms and fault type identification.

[0048] The device of this invention is small in size, light in weight, and low in power consumption. It can be used handheld or mounted on a bracket and is suitable for various scenarios such as substations and laboratories, and has good prospects for engineering applications.

[0049] The measurement using this device mainly includes the following steps: transmitting a linear frequency modulated continuous wave via millimeter-wave radar and receiving the echo signal; performing range-dimensional FFT processing on the echo signal and combining it with a spectrum subdivision algorithm to improve ranging accuracy; using static clutter filtering and incoherent accumulation methods to enhance the signal-to-noise ratio; performing target detection and localization using range-Doppler spectrum or range-angle spectrum; extracting the target's micro-vibration signal using arctangent demodulation or maximum likelihood estimation methods; and for multi-target scenarios, combining angle estimation, point cloud clustering, and beam scanning strategies to achieve full-field vibration distribution measurement.

[0050] This invention employs a compact MIMO antenna layout to achieve high angular resolution within a limited aperture. The device incorporates a high-precision inertial measurement unit to compensate for the impact of handheld operation on measurements. It also includes a built-in vibration characteristic database for various typical GIS models and an automated analysis app, guiding users through measurements and generating immediate diagnostic reports. This invention is lightweight and easy to use, enabling rapid and efficient full-area vibration measurement and diagnosis of on-site GIS equipment, significantly improving detection efficiency.

[0051] The preferred embodiments of the present invention have been described in detail above. It should be understood that those skilled in the art can make numerous modifications and variations based on the concept of the present invention without creative effort. Therefore, all technical solutions that can be obtained by those skilled in the art based on the concept of the present invention through logical analysis, reasoning, or limited experimentation on the basis of existing technology should be within the scope of protection defined by the claims.

Claims

1. A portable microwave non-contact vibration measurement method for GIS equipment, used to test the vibration signal of GIS equipment, characterized in that, include: Step 1: Transmit a linear frequency modulated continuous wave and receive the echo signal, then perform data preprocessing; Step 2: Identify the vibration target in the preprocessed echo signal, then use an angle estimation algorithm to analyze the vibration target and obtain angle dimension information; calculate the coordinates based on the angle dimension information to obtain point cloud data, and use a clustering algorithm to classify the target in the point cloud data; Step 3: For each classified target, use a phase estimation algorithm to extract its phase information; Based on the phase information, the arctangent demodulation method is used to restore the vibration waveform and generate a vibration signal.

2. The portable GIS device microwave non-contact vibration measurement method according to claim 1, characterized in that, The process of classifying the point cloud data using a clustering algorithm specifically includes: First, initialize the cluster label of all data points to "unclassified" and the cluster number to 0. Then, iterate through the point cloud data: For each unclassified point found, it is marked as classified, and the number of points in its neighborhood p is compared with a pre-set minimum neighborhood point threshold. If the number is less than the minimum neighborhood point threshold, it is marked as a noise point, and the process continues to the next point. If the number is greater than the minimum neighborhood point threshold, it is marked as a core point, and a new cluster is created. All points in the point cloud data neighborhood p are added to the new cluster. The process continues to traverse all points in the neighborhood p, and for each data point found: If a data point is not visited, mark it as visited; search whether the number of points in the neighborhood q of the data point is greater than the minimum neighborhood point threshold; if it is greater, add all points in the neighborhood q of the data point to the new cluster; if it is less, mark the data point as a noise point and continue to traverse the next point.

3. The portable GIS device microwave non-contact vibration measurement method according to claim 1, characterized in that, The data preprocessing process specifically includes: First, the echo signal is mixed, filtered, and sampled by an ADC to obtain an intermediate frequency signal; then, the intermediate frequency signal is subjected to distance-dimensional spectrum analysis, and the spectrum is subdivided by linear frequency modulation Z-transform; The mean cancellation algorithm is used to filter out static clutter, and the signal-to-noise ratio is improved by incoherent accumulation.

4. The portable GIS device microwave non-contact vibration measurement method according to claim 1, characterized in that, Step two further includes: after obtaining the point cloud data, selecting either a beam scanning method or a clustering algorithm to classify the point cloud data into targets based on different measurement requirements.

5. The portable GIS device microwave non-contact vibration measurement method according to claim 1, characterized in that, Step two also includes: A two-dimensional adaptive constant false alarm rate (CFAR) algorithm is used to identify potential vibration targets in the range-Doppler spectrum or range-angle spectrum. The identification process specifically includes: statistically analyzing the background energy of the reference cells surrounding the unit to be detected and determining the detection threshold; comparing the energy of the unit to be detected with the detection threshold to identify potential vibration targets. The angle estimation algorithm is specifically a multiple signal classification algorithm or a beamforming algorithm.

6. The portable GIS device microwave non-contact vibration measurement method according to claim 4, characterized in that, The clustering algorithm is a density-based noise-based spatial clustering algorithm. The density-based noise-based spatial clustering algorithm clusters data points based on the neighborhood distance between each data point in the point cloud data and the number of sample points in the neighborhood. Data points that meet the preset density connectivity conditions are divided into the same target point cloud clusters, and discrete data points that do not meet the density connectivity conditions are identified as noise points.

7. The portable GIS device microwave non-contact vibration measurement method according to claim 1, characterized in that, Step three also includes: For each classified target, the maximum likelihood estimation method is used to reconstruct the vibration waveform and generate a vibration signal; After generating the vibration signal, the vibration signal is analyzed and defect identified based on the vibration feature database, and finally a test report containing the vibration distribution is generated.

8. An apparatus for a portable GIS device microwave non-contact vibration measurement method as described in any one of claims 1-7, characterized in that, include: Microwave transceiver circuit: used to generate frequency modulated continuous wave signals and receive echo signals; Antenna array: connected to the microwave transceiver circuit, used for transmitting signals and receiving echoes; Embedded processor: used to control the microwave transceiver circuit and process the echo signal, and calculate vibration information; Battery: Used to power the entire device; Touchscreen display: Used for human-computer interaction and displaying the final test report.

9. A portable GIS equipment microwave non-contact vibration measurement device according to claim 8, characterized in that, The device also integrates an inertial measurement unit for measuring the device's own attitude and jitter; the embedded processor uses an internal algorithm to compensate for measurement errors caused by hand-held jitter in real time based on the data from the inertial measurement unit.

10. A portable GIS equipment microwave non-contact vibration measurement device according to claim 8, characterized in that, The embedded processor has multiple GIS models and typical measurement point distribution maps built in, which are displayed on the touch screen to guide the user to align the device and perform measurements.

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