Acoustic wave imaging method based on unmanned aerial vehicle carrying technology

Abstract

The invention discloses a sound wave imaging method based on an unmanned aerial vehicle carrying technology, and relates to the technical field of sound wave imaging, and the method comprises the following steps: S1, decomposing an ultrasonic signal; s2, de-noising the signal; s3, performing frequency domain analysis; s4, feature extraction; and S5, accurate imaging. According to the method, the ultrasonic signal is decomposed into a plurality of IMF components with different center frequencies, the kurtosis of each IMF component is calculated, the noise component dominated by narrow-band interference is screened out, the interference of environmental noise on the acoustic signal is reduced, the attenuation of the acoustic intensity in the propagation process is prevented, and the wavelet denoising method has a good frequency selection characteristic and can be applied to the field of acoustic communication. Most of partial discharge characteristics of the denoised ultrasonic signals are still kept, the frequency domain characteristics of the partial discharge sound wave signals are fully extracted by analyzing the distribution condition of signal energy in the frequency domain, then the processed ultrasonic waves are sensed through the sound wave module, and waveform characteristics and imaging characteristics are obtained through sequential scanning, so that conditions are provided for sound wave imaging; and an accurate sound image cloud picture is obtained.

Description

Technical Field

[0001] The present invention relates to the technical field of acoustic wave imaging, and in particular to an acoustic wave imaging method based on unmanned aerial vehicle (UAV) carrying technology. Background Art

[0002] As one of the core means of equipment status detection and diagnosis, live detection of substation equipment has many advantages, such as no need for power outage, accurate positioning of defective parts, quantitative analysis of defect degree, and simple and portable equipment. It has become a key link in the operation and maintenance of power grids. The equipment in substations is complex, and substation equipment is affected by electricity, heat, and machinery at the same time. There are many potential hidden dangers and defects. Corona discharge is a common discharge fault. Corona often generates less heat and is easily dissipated in the air. It is difficult to detect early discharges only by infrared, but they will generate sound wave signals. The sound of discharge can be detected by sound wave technology. Non-electrical faults, such as mechanical faults such as loose screws and iron sheets, cannot be detected by infrared imaging and ultraviolet imaging. Ultrasonic detection is relatively vague in distinguishing mechanical low-frequency micro-vibrations, and it is not obvious to judge the position of abnormal sound sources from the amplitude. Acoustic imaging technology solves many problems. It is extremely sensitive to capture sound sources, and can simultaneously measure the positions of discharge sound sources and mechanical vibration sound sources, and can be displayed in real scenes. It has good application prospects in live detection of substations.

[0003] Acoustic signals are easily affected by environmental noise, and the intensity of the sound waves will decay during propagation, causing the sound signals to be often submerged in the noise. Common noise interference can be divided into periodic narrowband interference and white noise interference according to the waveform characteristics. Among them, periodic narrowband interference has a fixed bandwidth and center frequency, and the interference intensity is relatively large. White noise interference is a continuous and smooth Gaussian random signal, and its spectrum is distributed over the entire frequency band of the signal. The local discharge signal after denoising contains a large amount of local discharge characteristic information, so it needs to be analyzed. Summary of the invention

[0004] The purpose of the present invention is to overcome the shortcomings of the prior art and provide an acoustic wave imaging method based on drone-mounted technology.

[0005] A technical solution to achieve the above purpose is: an acoustic wave imaging method based on drone-mounted technology, comprising the following steps:

[0006] S1. Decomposition of ultrasonic signal: VMD decomposition is performed on the original local acoustic discharge signal, the ultrasonic signal is adaptively decomposed into multiple IMF components with different center frequencies, and the kurtosis of each IMF component is calculated to filter out the noise component dominated by narrowband interference;

[0007] S2, signal denoising: denoise the remaining IMF components and filter out the residual white noise to obtain the denoised signal;

[0008] S3. Frequency domain analysis: The partial discharge time domain signal is converted into a frequency domain signal with frequency as the horizontal axis through fast Fourier transform. The frequency spectrum is used to intuitively understand the frequency variation characteristics in the frequency domain signal. The signal spectrum is converted into a power spectrum to analyze the distribution of signal energy in the frequency domain.

[0009] S4, feature extraction: use the drone to fly around and scan to obtain waveform features and imaging features;

[0010] S5. Accurate imaging: Combining the distribution in the frequency domain with the collected ultrasonic waves to obtain waveform characteristics and imaging characteristics, complete imaging is performed to identify and locate the signal source.

[0011] Furthermore, in step S1, the kurtosis is a statistic reflecting the degree of data dispersion. The IMF component with a kurtosis greater than 3 is taken as the effective component dominated by the partial discharge signal, and the IMF component with a kurtosis not greater than 3 is eliminated, and the remaining noise component is removed to achieve narrowband interference suppression.

[0012] Furthermore, the kurtosis expression is:

[0013]

[0014] In the formula, Ku represents the kurtosis, μ represents the mean of the data, σ represents the standard deviation of the data, and x k Represents the th sequence value of the data, N represents the total number of IMF components, and k represents the cardinality.

[0015] Furthermore, in step S2, an improved wavelet denoising method is used to denoise the remaining IMF components one by one.

[0016] Furthermore, the improved wavelet denoising method refers to performing wavelet transform on the original local acoustic discharge signal to obtain wavelet coefficients, reconstructing the processed IMF components according to the wavelet coefficients to obtain the denoised signal, and the wavelet coefficient acquisition formula is:

[0017]

[0018] Where w is the wavelet coefficient, j and k represent the scale parameter and translation parameter respectively, ψ(x) represents the wavelet basis function, f(n) represents the discrete signal of the original local acoustic discharge signal collected, N represents the total number of original local acoustic discharge signals, and n represents the original local acoustic discharge signal variable.

[0019] Furthermore, in step S3, the ultrasonic wave generated during partial discharge is defined as a sequence and converted into a spectrum sequence, and then the spectrum sequence is converted into a power spectrum density, and the center frequency and frequency variance of the power spectrum are extracted respectively.

[0020] Furthermore, in step S3, the centroid frequency refers to the frequency of the signal component with a large component in the frequency spectrum, reflecting the distribution of the signal power spectrum, and the centroid frequency will shift in the direction of a larger power spectrum amplitude;

[0021] The frequency variance reflects the dispersion of the power spectrum energy distribution. If the power spectrum amplitude near the center of gravity is large, the frequency variance is small, otherwise the frequency variance is large.

[0022] Furthermore, the specific operation method of step S4 is that the drone flies around for scanning, and the acoustic wave module is connected to the dedicated gimbal of the drone. During the flight, the acoustic wave module senses the ultrasonic wave generated by the processed local discharge, and a reflected wave is generated from the transmitter to the receiver. The reflected wavelengths are converted into images one by one, and the reflected waves are returned to the real position to obtain waveform characteristics and imaging characteristics.

[0023] Furthermore, the step S4 is specifically as follows: S401, calculate the reflected wavelength from the transmitter to the receiver, the wavelength at this time is the processed data, project the wavelength data to the corresponding position, sum the wavelength data projections, calculate the number of projections at each position, obtain the average value of each position, and convert the reflected wavelengths into images one by one.

[0024] Furthermore, the step S4 further comprises the following steps:

[0025] S402, then automatically returning the reflected wave to the real position in the space, obtaining the superimposed amplitude of all points in sequence, and displaying it as a waveform;

[0026] S403, gridding the position space and assigning a grid number, setting each grid in the space as a reflection point, superimposing the amplitudes of all points and returning them to the grid points, and then scanning the entire grid to obtain waveform features and imaging features.

[0027] Compared with the prior art, the present invention has the following beneficial effects:

[0028] The present invention decomposes an ultrasonic signal into a plurality of IMF components with different center frequencies, calculates the kurtosis of each IMF component, screens out noise components dominated by narrowband interference, reduces the interference of environmental noise on the acoustic signal, and prevents the attenuation of the acoustic wave intensity during the propagation process. The wavelet denoising method has good frequency selection characteristics, so that the denoised ultrasonic signal still retains most of the local discharge characteristics, converts the local discharge time domain signal into a frequency domain signal, intuitively understands the frequency variation characteristics through the spectrum, transforms the signal spectrum into a power spectrum, analyzes the distribution of signal energy in the frequency domain, fully extracts the frequency domain characteristics of the local discharge acoustic wave signal, and then senses the processed ultrasonic wave through an acoustic wave module, sequentially scans to obtain waveform characteristics and imaging characteristics, provides conditions for acoustic wave imaging, and obtains accurate acoustic and image cloud maps. The application of the acoustic and image cloud maps can improve the detection rate of insulation defects of overhead lines, assist operation and maintenance personnel in quickly locating faulty equipment, and timely handles and repairs, thereby greatly improving work efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 A schematic diagram of the overall method flow provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0030] In order to better understand the technical solution of the present invention, the following is a detailed description through specific embodiments:

[0031] See also Figure 1 The present invention provides a technical solution: an acoustic wave imaging method based on drone-mounted technology, comprising the following steps:

[0032] S1. Decomposition of ultrasonic signal: VMD decomposition is performed on the original local acoustic discharge signal, the ultrasonic signal is adaptively decomposed into multiple IMF components with different center frequencies, and the kurtosis of each IMF component is calculated to filter out the noise component dominated by narrowband interference;

[0033] S2, signal denoising: using improved wavelet denoising method to denoise the remaining IMF components one by one, filtering out the residual white noise, and obtaining the denoised signal;

[0034] S3. Frequency domain analysis: The partial discharge time domain signal is converted into a frequency domain signal with frequency as the horizontal axis through fast Fourier transform. The frequency spectrum is used to intuitively understand the frequency variation characteristics in the frequency domain signal. The signal spectrum is converted into a power spectrum to analyze the distribution of signal energy in the frequency domain.

[0035] S4. Feature extraction: Use a drone to fly around for scanning. The acoustic wave module is connected to the drone's dedicated gimbal. During the flight, the acoustic wave module senses the ultrasonic waves generated by the processed partial discharge, and a reflected wave is generated from the transmitter to the receiver. The reflected wavelengths are converted into images one by one, and the reflected waves are returned to the real position. The waveform features and imaging features are obtained by scanning;

[0036] S5. Accurate imaging: Combining the distribution in the frequency domain with the collected ultrasonic waves to obtain waveform characteristics and imaging characteristics, complete imaging is performed to identify and locate the signal source.

[0037] In step S1, kurtosis is a statistic that reflects the degree of data dispersion. The IMF component with a kurtosis greater than 3 is taken as the effective component dominated by the partial discharge signal, and the IMF component with a kurtosis less than 3 is eliminated to remove the remaining noise components to achieve narrowband interference suppression;

[0038] The ultrasonic signal generated by partial discharge is manifested as multiple short pulse signals in the time domain, with a small degree of data dispersion, while narrowband interference is the superposition of multiple sinusoidal signals, with a wide time domain distribution and a large degree of data dispersion. Therefore, the kurtosis of the partial discharge signal is much greater than that of the narrowband interference signal.

[0039] The kurtosis expression is:

[0040]

[0041] In the formula, Ku represents the kurtosis, μ represents the mean of the data, σ represents the standard deviation of the data, and x k represents the data's th sequence value, N represents the total number of IMF components, and k represents the cardinality;

[0042] In step S2, the improved wavelet denoising method refers to performing wavelet transform on the original local acoustic discharge signal to obtain wavelet coefficients, and reconstructing the processed IMF components according to the wavelet coefficients to obtain a denoised signal. The wavelet coefficient acquisition formula is:

[0043]

[0044] Wherein, w is the wavelet coefficient, j and k represent the scale parameter and translation parameter respectively, ψ(x) represents the wavelet basis function, f(n) represents the discrete signal of the original local acoustic discharge signal collected, N represents the total number of original local acoustic discharge signals, and n represents the original local acoustic discharge signal variable;

[0045] In step S3, the ultrasonic wave generated during partial discharge is defined as a sequence and converted into a spectrum sequence, and then the spectrum sequence is converted into a power spectrum density, and the center frequency and frequency variance of the power spectrum are extracted respectively;

[0046] The spectrum sequence conversion formula is:

[0047]

[0048] In the formula, X(f) represents the spectrum sequence, X i represents the input sequence, e represents the natural exponential function, π refers to pi, N represents the length of the sequence, i represents the number of sequence bits, and f represents the ultrasonic wave generated during partial discharge;

[0049] The power spectrum density conversion formula is:

[0050]

[0051] In the formula, PSD(f) represents power spectrum density, X(f) represents spectrum sequence, N represents the length of the sequence, and f represents the ultrasonic wave generated during partial discharge;

[0052] In step S3, the centroid frequency refers to the frequency of the signal component with a large component in the frequency spectrum, reflecting the distribution of the signal power spectrum. The centroid frequency will shift in the direction of a larger power spectrum amplitude;

[0053] The frequency variance reflects the dispersion of the power spectrum energy distribution. If the power spectrum amplitude near the center of gravity is large, the frequency variance is small, otherwise the frequency variance is large.

[0054] Step S4 includes the following steps:

[0055] S401, calculate the reflected wavelength from the transmitter to the receiver, the wavelength at this time is the processed data, project the wavelength data to the corresponding position, sum the wavelength data projections, calculate the number of projections at each position, obtain the average value of each position, and convert the reflected wavelengths into images one by one;

[0056] S402, then automatically returning the reflected wave to the real position in the space, obtaining the superimposed amplitude of all points in sequence, and displaying it as a waveform;

[0057] S403, gridding the position space and assigning a grid number, setting each grid in the space as a reflection point, superimposing the amplitudes of all points and returning them to the grid points, and then scanning the entire grid to obtain waveform features and imaging features.

[0058] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0059] Those skilled in the art should recognize that the above embodiments are only used to illustrate the present invention, and are not intended to limit the present invention. As long as they are within the spirit of the present invention, any changes or modifications to the above embodiments will fall within the scope of the claims of the present invention.

Claims

1. The acoustic wave imaging method based on drone-mounted technology is characterized in that: The following steps are involved: S1. Decomposition of ultrasonic signal: VMD decomposition is performed on the original local acoustic discharge signal, the ultrasonic signal is adaptively decomposed into multiple IMF components with different center frequencies, and the kurtosis of each IMF component is calculated to filter out the noise component dominated by narrowband interference; S2, signal denoising: denoise the remaining IMF components and filter out the residual white noise to obtain the denoised signal; S3. Frequency domain analysis: The partial discharge time domain signal is converted into a frequency domain signal with frequency as the horizontal axis through fast Fourier transform. The frequency spectrum is used to intuitively understand the frequency variation characteristics in the frequency domain signal. The signal spectrum is converted into a power spectrum to analyze the distribution of signal energy in the frequency domain. S4, feature extraction: use the drone to fly around and scan to obtain waveform features and imaging features; S5. Accurate imaging: Combining the distribution in the frequency domain with the collected ultrasonic waves to obtain waveform characteristics and imaging characteristics, complete imaging is performed to identify and locate the signal source.

2. The method for acoustic imaging based on drone-mounted technology according to claim 1, characterized in that: In step S1, the kurtosis is a statistic reflecting the degree of data dispersion. The IMF component with a kurtosis greater than 3 is taken as the effective component dominated by the partial discharge signal, and the IMF component with a kurtosis not greater than 3 is eliminated to remove the remaining noise components to achieve narrowband interference suppression.

3. The method for acoustic imaging based on drone-mounted technology according to claim 2, characterized in that: The kurtosis expression is: In the formula, Ku represents the kurtosis, μ represents the mean of the data, σ represents the standard deviation of the data, and x k Represents the th sequence value of the data, N represents the total number of IMF components, and k represents the cardinality.

4. The method for acoustic imaging based on drone-mounted technology according to claim 1, characterized in that: In step S2, an improved wavelet denoising method is used to denoise the remaining IMF components one by one.

5. The method for acoustic imaging based on drone-mounted technology according to claim 4, characterized in that: The improved wavelet denoising method refers to performing wavelet transform on the original local acoustic discharge signal to obtain wavelet coefficients, reconstructing the processed IMF components according to the wavelet coefficients to obtain the denoised signal, and the wavelet coefficient acquisition formula is: Where w is the wavelet coefficient, j and k represent the scale parameter and translation parameter respectively, ψ(x) represents the wavelet basis function, f(n) represents the discrete signal of the original local acoustic discharge signal collected, N represents the total number of original local acoustic discharge signals, and n represents the original local acoustic discharge signal variable.

6. The method for acoustic wave imaging based on drone-mounted technology according to claim 1, characterized in that: In the step S3, the ultrasonic waves generated during partial discharge are defined as a sequence and converted into a spectrum sequence, and then the spectrum sequence is converted into a power spectrum density, and the center frequency and frequency variance of the power spectrum are extracted respectively.

7. The method for acoustic wave imaging based on drone-mounted technology according to claim 5, characterized in that: In step S3, the centroid frequency refers to the frequency of the signal component with a large component in the frequency spectrum, reflecting the distribution of the signal power spectrum. The centroid frequency will shift in the direction of a larger power spectrum amplitude; The frequency variance reflects the dispersion of the power spectrum energy distribution. If the power spectrum amplitude near the center of gravity is large, the frequency variance is small, otherwise the frequency variance is large.

8. The method for acoustic imaging based on drone-mounted technology according to claim 1, characterized in that: The specific operation method of step S4 is that the drone flies around to scan, and the acoustic wave module is connected to the dedicated gimbal of the drone. During the flight, the acoustic wave module senses the ultrasonic wave generated by the processed local discharge, and a reflected wave is generated from the transmitter to the receiver. The reflected wavelengths are converted into images one by one, and the reflected waves are returned to the real position to obtain waveform characteristics and imaging characteristics.

9. The method for acoustic imaging based on drone-mounted technology according to claim 8, characterized in that: The step S4 is specifically as follows: S401, calculate the reflected wavelength from the transmitter to the receiver, the wavelength at this time is the processed data, project the wavelength data to the corresponding position, sum the wavelength data projections, calculate the number of projections at each position, obtain the average value of each position, and convert the reflected wavelengths into images one by one.

10. The method for acoustic wave imaging based on drone-mounted technology according to claim 8, characterized in that: The step S4 further comprises the following steps: S402, then automatically returning the reflected wave to the real position in the space, obtaining the superimposed amplitude of all points in sequence, and displaying it as a waveform; S403, gridding the position space and assigning a grid number, setting each grid in the space as a reflection point, superimposing the amplitudes of all points and returning them to the grid points, and then scanning the entire grid to obtain waveform features and imaging features.

Images