A low-power voiceprint sensor design method for mechanical fault detection of high-voltage switchgear circuit breakers

Abstract

The present invention discloses a low-power consumption voiceprint sensor design method suitable for detecting mechanical faults of high-voltage switch cabinet circuit breakers. The method comprises the following steps: S1, designing a high-sensitivity sensor structure; S2, designing a micro-power consumption of the high-sensitivity sensor; S3, evaluating the installation position of the high-sensitivity sensor; S4, collecting audio signals by the high-sensitivity sensor; S5, processing the audio by the high-sensitivity sensor; and S6, processing the audio signals. The high-sensitivity wireless audio sensor of the present invention shields random noise interference and electromagnetic interference in the environment, thus overcoming the problems of random noise and electromagnetic interference. The method also applies micro-power consumption technology to the wireless audio sensor. The near-sound field of the circuit breaker is reconstructed using the discontinuous Galerkin finite element method to analyze the temporal variation of sound pressure in the sound field. The relationship between the energy value and a preset threshold is judged to determine whether the device connection is normal. The extracted features are then reduced in dimensionality to improve the analysis accuracy and computational efficiency of the audio.

Description

Technical Field

[0001] The present invention belongs to the technical field of low-power voiceprint sensors, and in particular relates to a design method of a low-power voiceprint sensor suitable for mechanical fault detection of a high-voltage switch cabinet circuit breaker. Background Art

[0002] The purpose of circuit breaker mechanical fault monitoring is to accurately determine the fault type or analyze the development trend of its operating status, thereby enabling reasonable condition-based maintenance. Commonly monitored characteristic parameters include the circuit breaker's opening (closing) coil current, main circuit current, and contact travel. Fault type determination is generally based on threshold range comparison. However, when one or more components of the operating mechanism exhibit early defects, their weak dynamic information is difficult to reflect using the characteristic quantities extracted by commonly used monitoring methods. Therefore, it is necessary to use multiple monitoring methods and multi-dimensional state characteristic parameter fusion processing to identify the circuit breaker's operating status.

[0003] Among the many methods for monitoring the mechanical condition of circuit breakers, voiceprint signal analysis, as a non-invasive monitoring method, is gaining popularity in the industry due to its stable and reliable feature parameter extraction. The method for monitoring circuit breaker voiceprint vibrations uses a number of voiceprint sensors to acquire voiceprint signals during operation. Signal processing and other methods are used to extract the characteristic information contained within these signals. This information is then supplemented with fault identification methods to locate the fault type or analyze the development trend of the circuit breaker's operating status.

[0004] Achieving true low power consumption is a systematic project, requiring intensified research and the use of appropriate methods and technologies at every level. Often, many performance indicators, such as speed, accuracy, and load capacity, are in conflict. Sometimes, to achieve higher speed, accuracy, and load capacity, further research and trade-offs between power consumption and speed are necessary based on actual conditions. Low-power design must be considered from the very beginning. Using targeted technologies to achieve power reduction impacts other design parameters, such as area and timing, and more importantly, has a significant impact on design methodology. Methodological impacts encompass architecture selection, verification, synthesis, testing, and implementation. Methodological validation and research are essential for the design of low-power systems. For large-scale application systems, systematic, hardware, and software approaches have been summarized. At the micro level, down to the individual circuit components, power reduction is achieved through a variety of circuit models, control models, mathematical models, and algorithm optimization. To this end, we propose a low-power voiceprint sensor design method for mechanical fault detection in high-voltage switchgear circuit breakers. Summary of the Invention

[0005] The purpose of the present invention is to provide a low-power consumption voiceprint sensor design method suitable for high-voltage switch cabinet circuit breaker mechanical fault detection, so as to solve the problems raised in the above background technology.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a method for designing a low-power voiceprint sensor suitable for detecting mechanical faults in high-voltage switchgear circuit breakers, characterized in that it includes the following steps:

[0007] S1. High-sensitivity sensor structure design: The high-sensitivity sensor is designed based on the principles of stethoscope and air circulation. It forms a closed environment when in use. The high-sensitivity sensor includes a cone-shaped sound collector, mounting ring, energy converter, upper pressure plate and isolator.

[0008] S2. Micro-power design for highly sensitive sensors: The selected master microcontroller has a sleep function, and the system is in sleep mode most of the time; low-power integrated circuits are used; unused pins are grounded or connected to a high level; pull-up or pull-down resistors are used as little as possible on IO pins; the power supply of each functional module in the circuit is reasonably divided and managed, and the microcontroller performs real-time control; and the use of peripheral components is reduced;

[0009] S3. Evaluation of the installation location of high-sensitivity sensors: First, the circuit breaker acoustic signal is collected to form an equivalent sound source. Then, the discontinuous Galerkin finite element method is used to reconstruct the near-sound field of the circuit breaker to analyze the temporal variation of the sound pressure within the sound field. Finally, the Pearson equilibrium similarity coefficient is constructed to calculate the spectral similarity between the soundprint at the alternative measurement point and the equivalent sound source, thereby achieving a quantitative assessment of the soundprint monitoring location.

[0010] S4. High-sensitivity sensor collects audio signals: The cone-shaped sound collector fits the device, forming a closed measurement environment that prevents the entry of external noise. The closed cavity allows all energy to be concentrated and pushed to the PVDF film.

[0011] S5. High-sensitivity sensor audio processing: The audio signal sampling and compression processes are combined, and the audio signal is directly sampled at a low speed at a frequency far below the Nyquist frequency at the audio signal acquisition node. This low-speed random sampling of the audio signal can significantly reduce the energy consumption of node data acquisition and transmission.

[0012] S6. Audio signal processing: Detect the collected audio signal, then collect and preprocess the audio signal, and finally perform multi-scale feature extraction.

[0013] 2. A low-power voiceprint sensor design method suitable for mechanical fault detection of high-voltage switch cabinet circuit breakers according to claim 1, characterized in that: the cone-shaped sound collector of the high-sensitivity sensor in S1 uses the gas compression principle to amplify the original audio signal, the sound collector is made of metal copper material, and the cable is the transmission line, which forms a closed environment when in use, so that the collected audio has the advantages of stability and accuracy; there is a mounting hole on the top of the sound collector for fixing the mounting ring, energy converter and upper pressing plate; the mounting ring and the upper pressing plate are made of polyformaldehyde thermoplastic crystalline polymer, the material has a hard texture and good insulation effect, is conducive to wireless signal transmission, and is easy to process and manufacture; the mounting ring is a circular concave structure as a whole, and the concave part is used to install the energy converter, and the diameter of the energy converter is equal to the diameter of the concave part of the mounting ring; the upper pressing plate is a circular convex structure as a whole. , the diameter of the convex part is equal to the diameter of the concave part of the mounting ring; the energy converter is placed in the concave structure of the mounting ring, and is embedded and squeezed and fixed by the convex part of the upper pressing plate; the connecting seam between the mounting ring and the upper pressing plate is sealed and fixed as a whole in the mounting hole at the top of the cone-shaped structure sound collector; there are corresponding groove structures in the mounting ring and the upper pressing plate for leading out cables, one end of the two cables is respectively connected to the upper and lower surfaces of the energy converter, and the other end is respectively connected to the positive and negative inputs of the amplifier in the hardware processing circuit; the isolator is made of polyformaldehyde thermoplastic crystalline polymer, the bottom is a hollow cylindrical structure, and the top is a semi-hollow spherical structure, which is connected to the cone-shaped structure sound collector through threads; the hollow cylindrical structure is used to store the hardware processing circuit, and the top of the semi-hollow spherical structure is provided with a vent to ensure the fluidity of the surrounding air when the energy converter vibrates, and the isolator is connected to the sound collector through threads.

[0014] 3. The low-power voiceprint sensor design method for high-voltage switchgear circuit breaker mechanical fault detection according to claim 1 is characterized in that the evaluation of the installation location of the high-sensitivity sensor in S3 is generally divided into signal measurement, sound field calculation, and measurement point analysis. The signal measurement portion mainly provides the sound field reconstruction data basis for the sound field calculation, and the sound field calculation results and the collected sound source data are used together to calculate the evaluation index of the monitoring point;

[0015] The signal measurement is used to measure the distance space parameters between the circuit breaker surface and the switch cabinet; the circuit breaker surface is divided into a number of equivalent source areas, and the equivalent sound source signal is collected using a microphone; the phase of the circuit breaker equivalent sound source signal is corrected; the equivalent sound source signal is fast Fourier transformed to calculate the frequency domain information of the equivalent sound source;

[0016] The sound field calculation simplifies and constructs a three-dimensional model of the calculation domain based on measured information, and then divides the grid; defines the parameters of the calculation domain and defines the environmental boundary conditions based on the simplified model; uses the discontinuous Galerkin finite element method to calculate the transient change process of the sound field; extracts the time domain signal of the sound pressure of the candidate monitoring point and performs a fast Fourier transform to obtain its corresponding frequency domain information;

[0017] The measuring point analysis calculates the Pearson correlation matrix of the frequency spectrum between all candidate measuring points and the equivalent sound source; calculates the Pearson balanced correlation coefficient based on the calculation result of the Pearson correlation matrix; and selects the candidate monitoring point with the highest Pearson balanced correlation coefficient as the optimal monitoring point.

[0018] 4. The low-power soundprint sensor design method for high-voltage switchgear circuit breaker mechanical fault detection according to claim 3 is characterized in that: when performing signal measurement to collect equivalent sound sources, it is necessary to simultaneously obtain the circuit breaker surface acoustic signals at all measuring points, and the equivalent sound source signals are corrected using the phase correction method used in operating deformation mode measurement; a fixed measuring point is used as a reference signal, and the fixed phase angle relationship between the signals of other measuring points and the reference signal is recorded. After all collection work is completed, all measuring points are phase-corrected based on the anchor phase of the reference measuring point;

[0019] Vibration and sound are essentially mechanical waves. Mechanical waves manifest as vibration waves in the circuit breaker medium. The process of mechanical wave transmission to the air is relatively complex, and it will stimulate additional vibration when transmitting to the circuit breaker. As the final stage of mechanical wave transmission, to avoid errors in the sound field calculation caused by complex mechanical parameters and internal propagation processes, the circuit breaker near-acoustic field analysis is regarded as a transient sound field calculation problem with the circuit breaker surface as the vibration source and the air medium as the calculation domain.

[0020] 5. The method for designing a low-power voiceprint sensor suitable for mechanical fault detection of a high-voltage switch cabinet circuit breaker according to claim 4, wherein the simplification of the calculation domain is:

[0021] Simplification of acoustic field boundaries: There are boundaries around the circuit breaker, which are simplified as hard acoustic field boundaries and hard acoustic field interfaces with sound-absorbing pores.

[0022] Principle of transient sound field calculation based on DGFEM:

[0023] DGFEM has obvious advantages in transient sound field analysis within a large spatial scale. The spatial discretization form of the sound pressure conduction equation is:

[0024] Mp+Cp+Kp=Q(t),

[0025] Where: M is the fluid mass matrix; C is the fluid damping matrix; K is the fluid stiffness matrix; p is the fluid node pressure matrix; Q(t) is the sound source vector at each node; the time domain discretization of the sound wave propagation equation is: 0<… <t n <… <t N ;

[0026] For the discontinuous time t n , its step function is expressed as:

[0027]

[0028] Where:

[0029]

[0030] For any time step In, the sound pressure distribution function p is calculated using the following cubic Hermit interpolation function:

[0031]

[0032] Where p represents the node sound pressure at time t, and the expressions of A(t) and B(t) are:

[0033]

[0034]

[0035] The derivative v of the sound pressure distribution function is linearly interpolated within the time step:

[0036]

[0037] The sound source Q also uses linear interpolation:

[0038]

[0039] Based on the equilibrium equations and boundary conditions, the weak form of the discontinuous Galerkin finite element method for acoustic wave propagation is obtained as follows:

[0040]

[0041] It is further converted into a decoupled formula, which is the basic solution formula of the time-domain discontinuous Galerkin finite element method:

[0042]

[0043] in,

[0044]

[0045] 6. The method for designing a low-power voiceprint sensor suitable for detecting mechanical faults in high-voltage switchgear circuit breakers according to claim 1, wherein the audio signal detection in S6 includes energy value calculation and the relationship between the energy value and a preset threshold value;

[0046] Calculate the energy value E,

[0047] The audio energy value of the extracted signal to be detected is calculated; when calculating the energy value E of the collected audio signal, it is shown in the following formula:

[0048]

[0049] Among them, N represents the total number of frames of the time domain audio data of the collected audio signal; k represents the frame sequence number of one frame in the time domain data of the collected signal; M represents the number of time domain sampling point data of each frame in the time domain data information of the collected signal; the mathematical expression x(i) represents the obtained value of the i-th time domain sampling point data of each frame in the time domain data of the collected signal.

[0050] 7. The method for designing a low-power voiceprint sensor suitable for detecting mechanical faults in high-voltage switchgear circuit breakers according to claim 1, wherein the relationship between the energy value and the preset threshold is:

[0051] If the calculated energy value E is less than the preset energy threshold Eth, the device connection is in an abnormal state and an alarm signal is generated;

[0052] If the calculated E≥Eth, first convert the N frames of time-domain data of the collected audio signal into frequency-domain data for detection using a corresponding method; the spectral peak frequency of each frame in the N frames of frequency-domain data must be extracted, that is, the amplitude of P frequency-domain sampling points in each frame of the converted frequency-domain information data. When the frequency of the frequency-domain sampling points is 16kHz, P is 1024; at the same time, record the frequency value with the largest amplitude among the P sampling points and record this point as the spectral peak frequency of the N frames of frequency-domain data; determine whether the spectral peak frequency of the frequency-domain audio data remains stable based on the calculation results. The calculation of the first stability value Dif of the spectral peak frequency of the frequency-domain data information is as follows:

[0053]

[0054] The second stability value St of the spectrum peak frequency is as follows:

[0055]

[0056] Wherein, N represents the total number of frames of the collected audio signal; k represents the frame sequence number of a frame of data in the time domain audio of the collected audio signal; index(k) represents the kth frame of frequency domain audio data in the data information of the collected audio signal; index(k+1) represents the spectrum peak frequency of the k+1th frame of data information in the data information of the collected audio signal;

[0057] Compare the calculated Dif with the first stability preset threshold, and the calculated St with the second stability preset threshold in turn; if the calculated Dif value is less than the first stability threshold Difth, and the calculated St value is less than the second stability preset threshold, then the spectrum peak frequency of the collected audio signal is definitely stable, which means that the device connection is abnormal, and an alarm signal is generated at the same time.

[0058] 8. The method for designing a low-power voiceprint sensor suitable for detecting mechanical faults in high-voltage switchgear circuit breakers according to claim 1, wherein the data collection and preprocessing steps in S6 are as follows:

[0059] The preprocessing process mainly includes four steps: pre-emphasis, framing, windowing and speed matching; among them, pre-emphasis processing is mainly to compensate for the loss of high-frequency components; framing is to facilitate local batch processing of audio data; when framing, each frame signal must contain at least one rotation cycle of the component, and the frame shift cannot exceed half of the frame length; due to the non-periodic truncation of the signal, the spectrum will cause serious tailing in the entire frequency band, resulting in very serious errors, called leakage. In order to ensure that each frame signal after framing still meets the continuity and reduce the impact of truncation effect, each frame data needs to be multiplied by a window function, that is, windowing processing; and the Hamming window is used.

[0060] 9. The method for designing a low-power voiceprint sensor for mechanical fault detection of high-voltage switchgear circuit breakers according to claim 1, wherein the multi-scale feature extraction in S6 includes extracting features from time domain, frequency domain, and cepstrum domain comprehensively, performing a comprehensive analysis, and then performing dimensionality reduction on the extracted features to screen out effective features.

[0061] The calculation formula of time domain characteristics is as follows:

[0062]

[0063] Where: y i (n) The signal amplitude of the nth point of the audio frame i after framing; L is the frame length; n is the point number of each frame; sgn is the sign function;

[0064] Among them, the sgn symbol function is defined as:

[0065]

[0066] Frequency domain features are used to convert time domain signals into frequency domain. They are defined as the signal power within a unit frequency band and intuitively show the functional relationship between signal variance and frequency.

[0067] The cepstral domain feature is the Mel-scale cepstral coefficient feature, which is a perceptual frequency domain cepstral coefficient. Based on the human auditory mechanism, a group of H triangular bandpass filter groups are arranged from low frequency to high frequency according to the size of the critical bandwidth. The number of filter groups and the number of critical bands must be close, generally 22-26.

[0068] After preprocessing the input signal, perform Fourier transform, then pass it through the filter group, calculate the logarithmic energy of each filter group output, and then perform discrete cosine transform to obtain the MFCC coefficients;

[0069] MFCC feature calculation formula:

[0070]

[0071] Where: S represents the Mel filter energy, kg / s 2 ; Subscript i represents the frame number; h represents the Mel filter number; n represents the spectrum line after discrete cosine transform;

[0072] It is used to perform automatic gain control on voiceprint signals, and is mainly composed of two parts: a gain amplifier and a feedback loop. The gain amplifier component uses a certain AGC algorithm to adjust the gain value according to the amplitude and threshold of the feedback loop in the system. That is, the AGC adjusts the automatic control of the signal by the strength of the automatic gain signal in the amplifying circuit. When the signal strength increases, the control of the AGC system feedback loop is reduced accordingly according to a certain relationship. Conversely, when the signal amplitude in the AGC system gain amplifier decreases, the gain of the feedback loop will be increased accordingly according to a certain relationship between the two. Through this method, after the AGC algorithm amplifies and adjusts, it is ensured that the amplitude of the communication system signal output can be basically maintained at a constant state.

[0073] 10. The method for designing a low-power voiceprint sensor suitable for detecting mechanical faults of high-voltage switchgear circuit breakers according to claim 9, wherein the automatic gain control step is as follows:

[0074] S601: Determine the dynamic range of the audio signal output: The design of the audio signal processing based on the use of the AGC algorithm must be determined according to the actual situation of the audio signal. If, during the design process, the expected value in the practical AGC system is a fixed value, the amplitude of the audio signal output will approach the expected value of the AGC system. In the process of approaching this expected value, the output signal will be continuously adjusted, thereby causing instability in the amplitude of the signal output. To ensure the stability of the audio signal output, based on the AGC expected value, a dynamic range of stable audio signal output is designed and determined with the expected value as the center. If the amplitude of the audio signal is within this dynamic range during the AGC algorithm adjustment, the output amplitude of the audio signal is considered to be stable.

[0075] S602: Determine the gain adjustment speed: To determine, ensure, and present the normal change trend of the voice signal amplitude, it is necessary to ensure that its gain remains unchanged or varies within a small range, thereby ensuring that the amplitude of the audio signal is not distorted during transmission. The interference encountered during audio signal transmission makes the average amplitude of the audio signal relatively small, so its gain value needs to be increased to ensure that the overall amplitude of the signal is increased, thereby ensuring that the audio signal is not distorted. When applying practical AGC to audio signals, its gain value needs to change slowly and quickly, changing suddenly and quickly with the slow change of the signal amplitude. When the audio signal gain adjustment time is longer than the normal adjustment time of the audio signal, the gain value will not change significantly at this time. The gain adjustment time should be set to 4 seconds based on the characteristics of the audio signal output. If the audio signal output is large, the gain should be reduced quickly at this time. Failure to reduce the gain adjustment will cause damage to the device. When the signal output value exceeds the upper limit of the expected value, the gain adjustment time should be set to 0.5ms.

[0076] The gain calculation formula of the practical AGC algorithm is:

[0077]

[0078] Among them, A(n+1) is the gain value after signal transmission adjustment; A(n) is the gain value before adjustment; A in is the signal amplitude value; A0 is the expected amplitude value; 2 -a is a constant; different values ​​of a will result in different AGC adjustment speeds. According to the actual requirements of the practical AGC algorithm, when the signal output amplitude is less than the upper limit of the expected value, a=16, the gain adjustment will be slower. If a=3, the gain adjustment speed will be faster.

[0079] S603: Determine the dynamic range of the input signal: During the audio signal input process, if the input value range is set too large, the signal will generate greater noise during transmission, affecting the input and output quality of the signal; on the contrary, if the set range of the audio input signal is smaller, the signal will be ignored due to its small size, thereby causing distortion of the audio input signal; based on the input time of the audio signal, a signal strength of 36dB is determined as noise; the input dynamic range of the audio signal is determined using the AGC algorithm, and the noise can be judged whether it occurs in the gap of the audio signal transmission based on the existence time of the noise. If the existence time of the noise is relatively short, it can be regarded as a gap in the audio signal; usually, the noise judgment time is set to 5s for the best; based on the existence time of the noise and the actual situation of the signal input, the range of about 6dB between the minimum value of the audio signal and the noise threshold is determined as the dynamic range of the audio signal input; within this range, the interference intensity of the noise on the audio signal is relatively high, so the gain value needs to be fixed;

[0080] S604: Limit the gain value: To avoid excessive gain and damage to signal transmission devices or equipment, the gain value needs to be controlled within a range. If the value calculated by the AGC is greater than the upper limit of the range, the gain value takes the maximum value, otherwise it takes the minimum value. Based on the actual transmission of the audio signal, the gain range is -3-30dB.

[0081] Compared with the prior art, the present invention has the following beneficial effects:

[0082] The cone-shaped sound collector of the high-sensitivity wireless audio sensor of the present invention uses the gas compression principle to amplify the original audio signal. The sound collector is made of metal copper material, and the cable is the transmission line. When in use, a closed environment is formed to shield the random noise interference and electromagnetic interference in the environment, overcome the problems of random noise and electromagnetic interference, and improve the signal-to-noise ratio of the original collected signal, so that the collected audio has the advantages of stability and accuracy; the polyvinylidene fluoride piezoelectric film is the core of the high-sensitivity audio signal sensor, which converts the sound signal into an electrical signal; the material is light and thin, soft, chemically stable, has strong toughness, is easy to be processed into various shapes, and is simple to make and cost-effective. Low cost; PVDF material has a wide response frequency range of 0-400MHz. Traditional microphones are limited to a low frequency of 20Hz. Using PVDF material can capture more low-frequency information. In addition, its acoustic impedance is similar to that of human tissue, making it often used in signal acquisition structures of precision instruments and equipment. When the surrounding air vibrates, the PVDF piezoelectric film vibrates with it, generating an electrical signal between the upper and lower surfaces of the film. When the static film voltage is used as a reference, equal and opposite charges are generated on the upper and lower surfaces of the film, sending the signal to the input of the differential amplifier. Due to the symmetry of the original signal, the differential mode operation cancels out the common-mode noise in the two signals.

[0083] The power consumption of highly sensitive wireless audio sensors is reduced through various hardware components, and micro-power consumption technology is applied to wireless audio sensors. This involves merging the sampling and compression processes of audio signals, performing low-speed random sampling of audio signals at a frequency far below the Nyquist frequency directly on the audio signal acquisition node. Signal reconstruction is then performed on the user end, which has strong computing and storage capabilities. While ensuring the quality of signal reconstruction, low-speed random sampling of audio signals can significantly reduce the energy consumption of node data acquisition and transmission, effectively solving the network congestion and high energy consumption problems caused by large data volumes in wireless audio sensor networks.

[0084] The near-sound field of the circuit breaker was reconstructed using the discontinuous Galerkin finite element method to analyze the temporal variation of the sound pressure within the sound field. Finally, the Pearson equilibrium similarity coefficient was constructed to calculate the spectral similarity between the soundprint at the alternative measurement point and the equivalent sound source, enabling quantitative assessment of the soundprint monitoring location.

[0085] By calculating the energy value and judging the relationship between the energy value and the preset threshold, it is determined whether the device connection is normal; and before analyzing the audio signal data, it is necessary to first ensure that valid data is collected; unified standards must be achieved during the collection process to reduce errors caused by various factors; audio feature extraction is extremely important. According to the characteristics of the sound signal, comprehensive and multi-scale feature extraction is performed from the time domain, frequency domain, cepstrum domain, etc., and a comprehensive analysis is performed. The extracted features are then reduced in dimensionality to facilitate the screening of effective features and improve the analysis accuracy and computational efficiency of the audio. BRIEF DESCRIPTION OF THE DRAWINGS

[0086] Figure 1 Schematic diagram of the steps of the present invention;

[0087] Figure 2 This is a schematic diagram of the structure of the highly sensitive sensor of the present invention;

[0088] Figure 3 The circuit breaker acoustic field reconstruction and sensor placement evaluation process of the present invention;

[0089] Figure 4 A schematic diagram of the detection process of the audio signal acquisition device of the present invention;

[0090] Figure 5 Schematic diagram of a triangular filter group from dense to sparse in the Mel scale frequency domain of the present invention. DETAILED DESCRIPTION

[0091] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0092] See also Figure 1-Figure 5 The present invention provides a suitable technical solution: a low-power voiceprint sensor design method suitable for high-voltage switch cabinet circuit breaker mechanical fault detection, including the following steps:

[0093] S1. High-sensitivity sensor structure design: The high-sensitivity sensor is designed based on the principles of stethoscope and air circulation. It forms a closed environment when in use. The high-sensitivity sensor includes a cone-shaped sound collector, mounting ring, energy converter, upper pressure plate and isolator.

[0094] S2. Micro-power design for highly sensitive sensors: The selected master microcontroller has a sleep function, and the system is in sleep mode most of the time; low-power integrated circuits are used; unused pins are grounded or connected to a high level; pull-up or pull-down resistors are used as little as possible on IO pins; the power supply of each functional module in the circuit is reasonably divided and managed, and the microcontroller performs real-time control; and the use of peripheral components is reduced;

[0095] S3. Evaluation of the installation location of high-sensitivity sensors: First, the circuit breaker acoustic signal is collected to form an equivalent sound source. Then, the discontinuous Galerkin finite element method is used to reconstruct the near-sound field of the circuit breaker to analyze the temporal variation of the sound pressure within the sound field. Finally, the Pearson equilibrium similarity coefficient is constructed to calculate the spectral similarity between the soundprint at the alternative measurement point and the equivalent sound source, thereby achieving a quantitative assessment of the soundprint monitoring location.

[0096] S4. High-sensitivity sensor collects audio signals: The cone-shaped sound collector fits the device, forming a closed measurement environment that prevents the entry of external noise. The closed cavity allows all energy to be concentrated and pushed to the PVDF film.

[0097] S5. High-sensitivity sensor audio processing: The audio signal sampling and compression processes are combined, and the audio signal is directly sampled at a low speed at a frequency far below the Nyquist frequency at the audio signal acquisition node. This low-speed random sampling of the audio signal can significantly reduce the energy consumption of node data acquisition and transmission.

[0098] S6. Audio signal processing: Detect the collected audio signal, then collect and preprocess the audio signal, and finally perform multi-scale feature extraction.

[0099] The closed measurement environment prevents the ingress of external noise, significantly reducing the impact of random external noise. Due to the closed cavity, all energy is concentrated and pushed onto the PVDF film. The signal collected by the relatively large collection surface acts on the relatively small film, which in itself results in physical amplification. In a closed environment, energy loss is very small, making smaller audio signals easier to collect and detect. In a complex electromagnetic environment, power frequency interference (50Hz) is the most significant. The design of the entire copper material is to shield this interference as much as possible, creating a small electromagnetic interference-free environment. In order to reduce the antenna effect of the signal transmission line, the wires connected on both sides of the PVDF film are all coaxial cables. In this embodiment, preferably, the cone-shaped sound collector of the high-sensitivity sensor in S1 uses the gas compression principle to amplify the original audio signal. The sound collector is made of metal copper material, and the cable is the transmission line. When in use, a closed environment is formed, so that the collected audio has the advantages of stability and accuracy; there is a mounting hole on the top of the sound collector to fix the mounting ring, energy converter and upper pressing plate; the mounting ring and the upper pressing plate are made of polyformaldehyde thermoplastic crystalline polymer, the material has a hard texture and good insulation effect, is conducive to wireless signal transmission, and is easy to process and manufacture; the mounting ring is a circular concave structure as a whole, and the concave part is used to install the energy converter, and the diameter of the energy converter is equal to the diameter of the concave part of the mounting ring; the upper pressing plate is a circular convex structure as a whole, The diameter of the convex part is equal to the diameter of the concave part of the mounting ring; the energy converter is placed in the concave structure of the mounting ring and is embedded and squeezed in place by the convex part of the upper pressing plate; the connecting seam between the mounting ring and the upper pressing plate is sealed and fixed as a whole in the mounting hole at the top of the cone-shaped sound collector; there are corresponding groove structures in the mounting ring and the upper pressing plate for leading out cables, one end of the two cables is respectively connected to the upper and lower surfaces of the energy converter, and the other end is respectively connected to the positive and negative inputs of the amplifier in the hardware processing circuit; the isolator is made of polyformaldehyde thermoplastic crystalline polymer, with a hollow cylindrical structure at the bottom and a semi-hollow spherical structure at the top, which is connected to the cone-shaped sound collector through threads; the hollow cylindrical structure is used to store the hardware processing circuit, and a vent is provided on the top of the semi-hollow spherical structure to ensure the fluidity of the surrounding air when the energy converter vibrates, and the isolator is connected to the sound collector through threads.

[0100] In order to explore the distribution law of the near-sound field of the circuit breaker and optimize the location of the soundprint monitoring point, the circuit breaker sound signal is first collected to form the equivalent sound source of the circuit breaker; then the discontinuous Galerkin finite element method is used to reconstruct the near-sound field of the circuit breaker to analyze the time-varying law of the sound pressure in the sound field; finally, the Pearson equilibrium similarity coefficient is constructed to calculate the spectral similarity between the soundprint and the equivalent sound source at the alternative measuring point, so as to achieve a quantitative evaluation of the soundprint monitoring position. This method can evaluate the sensor's ability to perceive the overall soundprint state of the circuit breaker when it is in different positions, and has a certain reference value for the deployment of the circuit breaker online monitoring device. In this embodiment, preferably, the installation position evaluation of the high-sensitivity sensor in S3 is divided into signal measurement, sound field calculation, and measurement point analysis. The signal measurement part mainly provides the sound field reconstruction data basis for the sound field calculation, and the results of the sound field calculation and the collected sound source data will be used together to calculate the evaluation index of the monitoring point;

[0101] The signal measurement is used to measure the distance space parameters between the circuit breaker surface and the switch cabinet; the circuit breaker surface is divided into a number of equivalent source areas, and the equivalent sound source signal is collected using a microphone; the phase of the circuit breaker equivalent sound source signal is corrected; the equivalent sound source signal is fast Fourier transformed to calculate the frequency domain information of the equivalent sound source;

[0102] The sound field calculation simplifies and constructs a three-dimensional model of the calculation domain based on measured information, and then divides the grid; defines the parameters of the calculation domain and defines the environmental boundary conditions based on the simplified model; uses the discontinuous Galerkin finite element method to calculate the transient change process of the sound field; extracts the time domain signal of the sound pressure of the candidate monitoring point and performs a fast Fourier transform to obtain its corresponding frequency domain information;

[0103] The measuring point analysis calculates the Pearson correlation matrix of the frequency spectrum between all candidate measuring points and the equivalent sound source; calculates the Pearson balanced correlation coefficient based on the calculation result of the Pearson correlation matrix; and selects the candidate monitoring point with the highest Pearson balanced correlation coefficient as the optimal monitoring point.

[0104] It is necessary to obtain the surface acoustic signals of the circuit breaker at all measuring points simultaneously. However, due to the limitation of the number of microphones, only signals from a limited number of measuring points can be collected simultaneously. In order to avoid errors in the sound field calculation caused by complex mechanical parameters and internal propagation processes, in this embodiment, preferably, when performing signal measurement to collect the equivalent sound source, the surface acoustic signals of the circuit breaker at all measuring points need to be obtained simultaneously, and the equivalent sound source signals are corrected using the phase correction method used in the operation deformation vibration mode measurement; a fixed measuring point is used as a reference signal, and the fixed phase angle relationship between the signals of other measuring points and the reference signal is recorded. After all the collection work is completed, all measuring points are phase-corrected based on the anchor phase of the reference measuring point;

[0105] Vibration and sound are essentially mechanical waves. Mechanical waves manifest as vibration waves in the circuit breaker medium. The process of mechanical wave transmission to the air is relatively complex, and it will stimulate additional vibration when transmitting to the circuit breaker. As the final stage of mechanical wave transmission, to avoid errors in the sound field calculation caused by complex mechanical parameters and internal propagation processes, the circuit breaker near-acoustic field analysis is regarded as a transient sound field calculation problem with the circuit breaker surface as the vibration source and the air medium as the calculation domain.

[0106] The vibration characteristics of different positions on the circuit breaker surface are different. The circuit breaker surface cannot be simply treated as a whole. Instead, its spatial differences need to be fully considered. Based on the equivalent source concept, the circuit breaker surface is divided into several sub-sound sources with independent amplitudes and phases. The sound pressure signal at the center point of the sub-sound source area measured experimentally is used as the sound signal on the entire surface of the sub-sound source. The combined effect of all sub-sound sources can equivalently simulate the sound source of the air domain sound field. Theoretically, when the equivalent source is small enough, the sound source state generated by the transformer surface can be completely restored. However, in the actual sound signal acquisition process, only a limited number of point source signals can be sampled. Therefore, it is necessary to simplify the entire circuit breaker surface into a limited number of sub-sound source surfaces according to the actual situation. In this embodiment, preferably, the simplification of the calculation domain is:

[0107] Simplification of acoustic field boundaries: There are boundaries around the circuit breaker, which are simplified as hard acoustic field boundaries and hard acoustic field interfaces with sound-absorbing pores.

[0108] Principle of transient sound field calculation based on DGFEM:

[0109] DGFEM has obvious advantages in transient sound field analysis within a large spatial scale. The spatial discretization form of the sound pressure conduction equation is:

[0110] Mp+Cp+Kp=Q(t),

[0111] Where: M is the fluid mass matrix; C is the fluid damping matrix; K is the fluid stiffness matrix; p is the fluid node pressure matrix; Q(t) is the sound source vector at each node; the time domain discretization of the sound wave propagation equation is: 0<… <t n <… <t N ;

[0112] For the discontinuous time t n , its step function is expressed as:

[0113]

[0114] Where:

[0115]

[0116] For any time step In, the sound pressure distribution function p is calculated using the following cubic Hermit interpolation function:

[0117]

[0118] Where p represents the node sound pressure at time t, and the expressions of A(t) and B(t) are:

[0119]

[0120]

[0121] The derivative v of the sound pressure distribution function is linearly interpolated within the time step:

[0122]

[0123] The sound source Q also uses linear interpolation:

[0124]

[0125] Based on the equilibrium equations and boundary conditions, the weak form of the discontinuous Galerkin finite element method for acoustic wave propagation is obtained as follows:

[0126]

[0127] It is further converted into a decoupled formula, which is the basic solution formula of the time-domain discontinuous Galerkin finite element method:

[0128]

[0129] in,

[0130]

[0131] In order to calculate the magnitude of the audio energy value in the signal to be detected, in this embodiment, preferably, the audio signal detection in S6 includes energy value calculation and the magnitude relationship between the energy value and a preset threshold value;

[0132] Calculate the energy value E,

[0133] The audio energy value of the extracted signal to be detected is calculated; when calculating the energy value E of the collected audio signal, it is shown in the following formula:

[0134]

[0135] Among them, N represents the total number of frames of the time domain audio data of the collected audio signal; k represents the frame sequence number of one frame in the time domain data of the collected signal; M represents the number of time domain sampling point data of each frame in the time domain data information of the collected signal; the mathematical expression x(i) represents the obtained value of the i-th time domain sampling point data of each frame in the time domain data of the collected signal.

[0136] In order to detect whether the device connection is abnormal and generate an alarm signal, in this embodiment, preferably, the relationship between the energy value and the preset threshold is:

[0137] If the calculated energy value E is less than the preset energy threshold Eth, the device connection is in an abnormal state and an alarm signal is generated;

[0138] If the calculated E≥Eth, first convert the N frames of time-domain data of the collected audio signal into frequency-domain data for detection using a corresponding method; the spectral peak frequency of each frame in the N frames of frequency-domain data must be extracted, that is, the amplitude of P frequency-domain sampling points in each frame of the converted frequency-domain information data. When the frequency of the frequency-domain sampling points is 16kHz, P is 1024; at the same time, record the frequency value with the largest amplitude among the P sampling points and record this point as the spectral peak frequency of the N frames of frequency-domain data; determine whether the spectral peak frequency of the frequency-domain audio data remains stable based on the calculation results. The calculation of the first stability value Dif of the spectral peak frequency of the frequency-domain data information is as follows:

[0139]

[0140] The second stability value St of the spectrum peak frequency is as follows:

[0141]

[0142] Wherein, N represents the total number of frames of the collected audio signal; k represents the frame sequence number of a frame of data in the time domain audio of the collected audio signal; index(k) represents the kth frame of frequency domain audio data in the data information of the collected audio signal; index(k+1) represents the spectrum peak frequency of the k+1th frame of data information in the data information of the collected audio signal;

[0143] Compare the calculated Dif with the first stability preset threshold, and the calculated St with the second stability preset threshold in turn; if the calculated Dif value is less than the first stability threshold Difth, and the calculated St value is less than the second stability preset threshold, then the spectrum peak frequency of the collected audio signal is definitely stable, which means that the device connection is abnormal, and an alarm signal is generated at the same time.

[0144] In order to compensate for the loss of high-frequency components; framing is to more conveniently perform local batch processing on the audio data. In this embodiment, preferably, the data collection and preprocessing steps in S6 are as follows:

[0145] The preprocessing process mainly includes four steps: pre-emphasis, framing, windowing and speed matching; among them, pre-emphasis processing is mainly to compensate for the loss of high-frequency components; framing is to facilitate local batch processing of audio data; when framing, each frame signal must contain at least one rotation cycle of the component, and the frame shift cannot exceed half of the frame length; due to the non-periodic truncation of the signal, the spectrum will cause serious tailing in the entire frequency band, resulting in very serious errors, called leakage. In order to ensure that each frame signal after framing still meets the continuity and reduce the impact of truncation effect, each frame data needs to be multiplied by a window function, that is, windowing processing; and the Hamming window is used.

[0146] In the field of audio recognition, audio feature extraction is extremely important. The extracted features are comprehensively analyzed and then the extracted features are reduced in dimension to facilitate screening out effective features. In this embodiment, preferably, the multi-scale feature extraction in S6 includes comprehensive and multi-scale feature extraction of time domain features, frequency domain features, and cepstrum domain features, and then comprehensive analysis is performed and then the extracted features are reduced in dimension to facilitate screening out effective features.

[0147] The calculation formula of time domain characteristics is as follows:

[0148]

[0149] Where: y i (n) The signal amplitude of the nth point of the audio frame i after framing; L is the frame length; n is the point number of each frame; sgn is the sign function;

[0150] Among them, the sgn symbol function is defined as:

[0151]

[0152] Frequency domain features are used to convert time domain signals into frequency domain. They are defined as the signal power within a unit frequency band and intuitively show the functional relationship between signal variance and frequency.

[0153] The cepstral domain feature is the Mel-scale cepstral coefficient feature, which is a perceptual frequency domain cepstral coefficient. Based on the human auditory mechanism, a group of H triangular bandpass filter groups are arranged from low frequency to high frequency according to the size of the critical bandwidth. The number of filter groups and the number of critical bands must be close, generally 22-26.

[0154] After preprocessing the input signal, perform Fourier transform, then pass it through the filter group, calculate the logarithmic energy of each filter group output, and then perform discrete cosine transform to obtain the MFCC coefficients;

[0155] MFCC feature calculation formula:

[0156]

[0157] Where: S represents the Mel filter energy, kg / s 2 ; Subscript i represents the frame number; h represents the Mel filter number; n represents the spectrum line after discrete cosine transform;

[0158] It is used to perform automatic gain control on voiceprint signals, and is mainly composed of two parts: a gain amplifier and a feedback loop. The gain amplifier component uses a certain AGC algorithm to adjust the gain value according to the amplitude and threshold of the feedback loop in the system. That is, the AGC adjusts the automatic control of the signal by the strength of the automatic gain signal in the amplifying circuit. When the signal strength increases, the control of the AGC system feedback loop is reduced accordingly according to a certain relationship. Conversely, when the signal amplitude in the AGC system gain amplifier decreases, the gain of the feedback loop will be increased accordingly according to a certain relationship between the two. Through this method, after the AGC algorithm amplifies and adjusts, it is ensured that the amplitude of the communication system signal output can be basically maintained at a constant state.

[0159] In order to effectively reduce interference during audio signal output and ensure signal stability, in this embodiment, preferably, the steps of the automatic gain control are as follows:

[0160] S601: Determine the dynamic range of the audio signal output: The design of the audio signal processing based on the use of the AGC algorithm must be determined according to the actual situation of the audio signal. If, during the design process, the expected value in the practical AGC system is a fixed value, the amplitude of the audio signal output will approach the expected value of the AGC system. In the process of approaching this expected value, the output signal will be continuously adjusted, thereby causing instability in the amplitude of the signal output. To ensure the stability of the audio signal output, based on the AGC expected value, a dynamic range of stable audio signal output is designed and determined with the expected value as the center. If the amplitude of the audio signal is within this dynamic range during the AGC algorithm adjustment, the output amplitude of the audio signal is considered to be stable.

[0161] S602: Determine the gain adjustment speed: To determine, ensure, and present the normal change trend of the voice signal amplitude, it is necessary to ensure that its gain remains unchanged or varies within a small range, thereby ensuring that the amplitude of the audio signal is not distorted during transmission. The interference encountered during audio signal transmission makes the average amplitude of the audio signal relatively small, so its gain value needs to be increased to ensure that the overall amplitude of the signal is increased, thereby ensuring that the audio signal is not distorted. When applying practical AGC to audio signals, its gain value needs to change slowly and quickly, changing suddenly and quickly with the slow change of the signal amplitude. When the audio signal gain adjustment time is longer than the normal adjustment time of the audio signal, the gain value will not change significantly at this time. The gain adjustment time should be set to 4 seconds based on the characteristics of the audio signal output. If the audio signal output is large, the gain should be reduced quickly at this time. Failure to reduce the gain adjustment will cause damage to the device. When the signal output value exceeds the upper limit of the expected value, the gain adjustment time should be set to 0.5ms.

[0162] The gain calculation formula of the practical AGC algorithm is:

[0163]

[0164] Among them, A(n+1) is the gain value after signal transmission adjustment; A(n) is the gain value before adjustment; A in is the signal amplitude value; A0 is the expected amplitude value; 2 -a is a constant; different values ​​of a will result in different AGC adjustment speeds. According to the actual requirements of the practical AGC algorithm, when the signal output amplitude is less than the upper limit of the expected value, a=16, the gain adjustment will be slower. If a=3, the gain adjustment speed will be faster.

[0165] S603: Determine the dynamic range of the input signal: During the audio signal input process, if the input value range is set too large, the signal will generate greater noise during transmission, affecting the input and output quality of the signal; on the contrary, if the set range of the audio input signal is smaller, the signal will be ignored due to its small size, thereby causing distortion of the audio input signal; based on the input time of the audio signal, a signal strength of 36dB is determined as noise; the input dynamic range of the audio signal is determined using the AGC algorithm, and the noise can be judged whether it occurs in the gap of the audio signal transmission based on the existence time of the noise. If the existence time of the noise is relatively short, it can be regarded as a gap in the audio signal; usually, the noise judgment time is set to 5s for the best; based on the existence time of the noise and the actual situation of the signal input, the range of about 6dB between the minimum value of the audio signal and the noise threshold is determined as the dynamic range of the audio signal input; within this range, the interference intensity of the noise on the audio signal is relatively high, so the gain value needs to be fixed;

[0166] S604: Limit the gain value: To avoid excessive gain and damage to signal transmission devices or equipment, the gain value needs to be controlled within a range. If the value calculated by the AGC is greater than the upper limit of the range, the gain value takes the maximum value, otherwise it takes the minimum value. Based on the actual transmission of the audio signal, the gain range is -3-30dB.

[0167] The working principle and use process of the present invention:

[0168] Step 1: Design the high-sensitivity sensor structure: The high-sensitivity sensor is designed based on the principles of stethoscope and air circulation. It forms a closed environment when in use. The high-sensitivity sensor includes a cone-shaped sound collector, a mounting ring, an energy converter, an upper pressure plate, and an isolator.

[0169] Step 2: Low-power design for highly sensitive sensors: The selected main control microcontroller has a sleep function, and the system is in sleep mode most of the time; low-power integrated circuits are used; unused pins are grounded or connected to a high level; pull-up or pull-down resistors are used as little as possible on IO pins; the power supply of each functional module in the circuit is reasonably divided and managed, and the microcontroller is used for real-time control; and the use of peripheral components is reduced;

[0170] Step 3: Evaluate the installation location of high-sensitivity sensors: First, collect the circuit breaker's acoustic signal to construct the circuit breaker's equivalent sound source. Then, use the discontinuous Galerkin finite element method to reconstruct the circuit breaker's near-sound field to analyze how the sound pressure within the sound field changes over time. Finally, construct the Pearson equilibrium similarity coefficient to calculate the spectral similarity between the soundprint at the alternative measurement point and the equivalent sound source, thereby achieving a quantitative assessment of the soundprint monitoring location.

[0171] Step 4: A highly sensitive sensor collects audio signals: The cone-shaped sound collector is attached to the device, creating a closed measurement environment that prevents external noise from entering. The closed cavity allows all energy to be concentrated and pushed to the PVDF film.

[0172] Step 5: High-sensitivity sensor audio processing: The audio signal sampling and compression processes are combined, and the audio signal is directly sampled at a low speed at a frequency far below the Nyquist frequency at the audio signal acquisition node. This low-speed random sampling of the audio signal can significantly reduce the energy consumption of node data acquisition and transmission.

[0173] Step 6: Audio signal processing: Detect the collected audio signal, then collect and preprocess the audio signal, and finally perform multi-scale feature extraction.

[0174] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A low-power voiceprint sensor design method suitable for high-voltage switchgear circuit breaker mechanical fault detection, characterized in that: The following steps are included: S1. High-sensitivity sensor structure design: The high-sensitivity sensor is designed based on the principles of stethoscope and air circulation. It forms a closed environment when in use. The high-sensitivity sensor includes a cone-shaped sound collector, mounting ring, energy converter, upper pressure plate and isolator. S2. Micro-power design for highly sensitive sensors: The selected master microcontroller has a sleep function, and the system is in sleep mode most of the time; low-power integrated circuits are used; unused pins are grounded or connected to a high level; pull-up or pull-down resistors are used as little as possible on IO pins; the power supply of each functional module in the circuit is reasonably divided and managed, and the microcontroller performs real-time control; and the use of peripheral components is reduced; S3. Evaluation of the installation location of high-sensitivity sensors: First, the circuit breaker acoustic signal is collected to form an equivalent sound source. Then, the discontinuous Galerkin finite element method is used to reconstruct the near-sound field of the circuit breaker to analyze the temporal variation of the sound pressure within the sound field. Finally, the Pearson equilibrium similarity coefficient is constructed to calculate the spectral similarity between the soundprint at the alternative measurement point and the equivalent sound source, thereby achieving a quantitative assessment of the soundprint monitoring location. S4. High-sensitivity sensor collects audio signals: The cone-shaped sound collector fits the device, forming a closed measurement environment that prevents the entry of external noise. The closed cavity allows all energy to be concentrated and pushed to the PVDF film. S5. High-sensitivity sensor audio processing: The audio signal sampling and compression processes are combined, and the audio signal is directly sampled at a low speed at a frequency far below the Nyquist frequency at the audio signal acquisition node. This low-speed random sampling of the audio signal can significantly reduce the energy consumption of node data acquisition and transmission. S6. Audio signal processing: Detect the collected audio signal, then collect and preprocess the audio signal, and finally perform multi-scale feature extraction.

2. The method for designing a low-power voiceprint sensor suitable for detecting mechanical faults in high-voltage switchgear circuit breakers according to claim 1 is characterized in that: The cone-shaped sound collector of the high-sensitivity sensor in S1 uses the principle of gas compression to amplify the original audio signal. The sound collector is made of metal copper material, and the cable is the transmission line. When in use, a closed environment is formed, so that the collected audio has the advantages of stability and accuracy; there is a mounting hole on the top of the sound collector to fix the mounting ring, energy converter and upper pressing plate; the mounting ring and the upper pressing plate are made of polyformaldehyde thermoplastic crystalline polymer, which has a hard texture and good insulation effect, is conducive to wireless signal transmission, and is easy to process and manufacture; the mounting ring is a circular concave structure as a whole, and the concave part is used to install the energy converter, and the diameter of the energy converter is equal to the diameter of the concave part of the mounting ring; the upper pressing plate is a circular convex structure as a whole, and the diameter of the convex part is equal to the diameter of the concave part of the mounting ring; the energy converter is converted into The converter is placed in the concave structure of the mounting ring and is embedded and squeezed in place by the convex part of the upper pressing plate; the connecting seam between the mounting ring and the upper pressing plate is sealed and fixed as a whole in the mounting hole at the top of the cone-shaped sound collector; there are corresponding groove structures in the mounting ring and the upper pressing plate for leading out cables, one end of the two cables is respectively connected to the upper and lower surfaces of the energy converter, and the other end is respectively connected to the positive and negative inputs of the amplifier in the hardware processing circuit; the isolator is made of polyformaldehyde thermoplastic crystalline polymer, with a hollow cylindrical structure at the bottom and a semi-hollow spherical structure at the top, which is connected to the cone-shaped sound collector through threads; the hollow cylindrical structure is used to store the hardware processing circuit, and a vent is provided on the top of the semi-hollow spherical structure to ensure the fluidity of the surrounding air when the energy converter vibrates, and the isolator is connected to the sound collector through threads.

3. The method for designing a low-power voiceprint sensor suitable for detecting mechanical faults in high-voltage switchgear circuit breakers according to claim 1 is characterized in that: The evaluation of the installation position of the high-sensitivity sensor in S3 is generally divided into signal measurement, sound field calculation, and measurement point analysis. The signal measurement part mainly provides the sound field reconstruction data basis for the sound field calculation, and the results of the sound field calculation and the collected sound source data will be used together to calculate the evaluation index of the monitoring point; The signal measurement is used to measure the distance space parameters between the circuit breaker surface and the switch cabinet; the circuit breaker surface is divided into a number of equivalent source areas, and the equivalent sound source signal is collected using a microphone; the phase of the circuit breaker equivalent sound source signal is corrected; the equivalent sound source signal is fast Fourier transformed to calculate the frequency domain information of the equivalent sound source; The sound field calculation simplifies and constructs a three-dimensional model of the calculation domain based on measured information, and then divides the grid; defines the parameters of the calculation domain and defines the environmental boundary conditions based on the simplified model; uses the discontinuous Galerkin finite element method to calculate the transient change process of the sound field; extracts the time domain signal of the sound pressure of the candidate monitoring point and performs a fast Fourier transform to obtain its corresponding frequency domain information; The measuring point analysis calculates the Pearson correlation matrix of the frequency spectrum between all candidate measuring points and the equivalent sound source; calculates the Pearson balanced correlation coefficient based on the calculation result of the Pearson correlation matrix; and selects the candidate monitoring point with the highest Pearson balanced correlation coefficient as the optimal monitoring point.

4. The method for designing a low-power voiceprint sensor suitable for detecting mechanical faults in high-voltage switchgear circuit breakers according to claim 3 is characterized in that: When performing the signal measurement to collect the equivalent sound source, it is necessary to simultaneously obtain the circuit breaker surface acoustic signals at all measuring points, and use the phase correction method used in the operation deformation vibration mode measurement to correct the equivalent sound source signal; fix one measuring point as the reference signal, record the fixed phase angle relationship between the signals of other measuring points and the reference signal, and after all the collection work is completed, perform phase correction on all measuring points based on the anchor phase of the reference measuring point; Vibration and sound are essentially mechanical waves. Mechanical waves manifest as vibration waves in the circuit breaker medium. The process of mechanical wave transmission to the air is relatively complex, and it will stimulate additional vibration when transmitting to the circuit breaker. As the final stage of mechanical wave transmission, to avoid errors in the sound field calculation caused by complex mechanical parameters and internal propagation processes, the circuit breaker near-acoustic field analysis is regarded as a transient sound field calculation problem with the circuit breaker surface as the vibration source and the air medium as the calculation domain.

5. The method for designing a low-power voiceprint sensor suitable for detecting mechanical faults of high-voltage switchgear circuit breakers according to claim 4, characterized in that: The computational domain is simplified as follows: Simplification of acoustic field boundaries: There are boundaries around the circuit breaker, which are simplified as hard acoustic field boundaries and hard acoustic field interfaces with sound-absorbing pores. Principle of transient sound field calculation based on DGFEM: DGFEM has obvious advantages in transient sound field analysis within a large spatial scale. The spatial discretization form of the sound pressure conduction equation is: Mp+Cp+Kp=Q(t), Where: M is the fluid mass matrix; C is the fluid damping matrix; K is the fluid stiffness matrix; p is the fluid node pressure matrix; Q(t) is the sound source vector at each node; the time domain discretization of the sound wave propagation equation is: 0<… <t n <… <t N ; For the discontinuous time t n , its step function is expressed as: Where: For any time step In, the sound pressure distribution function p is calculated using the following cubic Hermit interpolation function: Where p represents the node sound pressure at time t, and the expressions of A(t) and B(t) are: The derivative v of the sound pressure distribution function is linearly interpolated within the time step: The sound source Q also uses linear interpolation: Based on the equilibrium equations and boundary conditions, the weak form of the discontinuous Galerkin finite element method for acoustic wave propagation is obtained as follows: It is further converted into a decoupled formula, which is the basic solution formula of the time-domain discontinuous Galerkin finite element method: in, 6. The method for designing a low-power voiceprint sensor suitable for detecting mechanical faults in high-voltage switchgear circuit breakers according to claim 1, characterized in that: The audio signal detection in S6 includes energy value calculation and the relationship between the energy value and a preset threshold value; Calculate the energy value E, The audio energy value of the extracted signal to be detected is calculated; when calculating the energy value E of the collected audio signal, it is shown in the following formula: Among them, N represents the total number of frames of the time domain audio data of the collected audio signal; k represents the frame sequence number of one frame in the time domain data of the collected signal; M represents the number of time domain sampling point data of each frame in the time domain data information of the collected signal; the mathematical expression x(i) represents the obtained value of the i-th time domain sampling point data of each frame in the time domain data of the collected signal.

7. The method for designing a low-power voiceprint sensor suitable for detecting mechanical faults of high-voltage switchgear circuit breakers according to claim 6, characterized in that: The relationship between the energy value and the preset threshold is: If the calculated energy value E is less than the preset energy threshold Eth, the device connection is in an abnormal state and an alarm signal is generated; If the calculated E≥Eth, first convert the N frames of time-domain data of the collected audio signal into frequency-domain data for detection using a corresponding method; the spectral peak frequency of each frame in the N frames of frequency-domain data must be extracted, that is, the amplitude of P frequency-domain sampling points in each frame of the converted frequency-domain information data. When the frequency of the frequency-domain sampling points is 16kHz, P is 1024; at the same time, record the frequency value with the largest amplitude among the P sampling points and record this point as the spectral peak frequency of the N frames of frequency-domain data; determine whether the spectral peak frequency of the frequency-domain audio data remains stable based on the calculation results. The calculation of the first stability value Dif of the spectral peak frequency of the frequency-domain data information is as follows: The second stability value St of the spectrum peak frequency is as follows: Wherein, N represents the total number of frames of the collected audio signal; k represents the frame sequence number of a frame of data in the time domain audio of the collected audio signal; index(k) represents the kth frame of frequency domain audio data in the data information of the collected audio signal; index(k+1) represents the spectrum peak frequency of the k+1th frame of data information in the data information of the collected audio signal; Compare the calculated Dif with the first stability preset threshold, and the calculated St with the second stability preset threshold in turn; if the calculated Dif value is less than the first stability threshold Difth, and the calculated St value is less than the second stability preset threshold, then the spectrum peak frequency of the collected audio signal is definitely stable, which means that the device connection is abnormal, and an alarm signal is generated at the same time.

8. The method for designing a low-power voiceprint sensor suitable for detecting mechanical faults of high-voltage switchgear circuit breakers according to claim 1, characterized in that: The data collection and preprocessing steps in S6 are as follows: The preprocessing process mainly includes four steps: pre-emphasis, framing, windowing and speed matching; among them, pre-emphasis processing is mainly to compensate for the loss of high-frequency components; framing is to facilitate local batch processing of audio data; when framing, each frame signal must contain at least one rotation cycle of the component, and the frame shift cannot exceed half of the frame length; due to the non-periodic truncation of the signal, the spectrum will cause serious tailing in the entire frequency band, resulting in very serious errors, called leakage. In order to ensure that each frame signal after framing still meets the continuity and reduce the impact of truncation effect, each frame data needs to be multiplied by a window function, that is, windowing processing; and the Hamming window is used.

9. The method for designing a low-power voiceprint sensor suitable for detecting mechanical faults of high-voltage switchgear circuit breakers according to claim 1, characterized in that: The multi-scale feature extraction in S6 includes comprehensive and multi-scale feature extraction from time domain features, frequency domain features, and cepstrum domain features, and then comprehensive analysis is performed on the extracted features, and then dimensionality reduction is performed on the extracted features to facilitate screening out effective features; The calculation formula of time domain characteristics is as follows: Where: y i (n) The signal amplitude of the nth point of the audio frame i after framing; L is the frame length; n is the point number of each frame; sgn is the sign function; Among them, the sgn symbol function is defined as: Frequency domain features are used to convert time domain signals into frequency domain. They are defined as the signal power within a unit frequency band and intuitively show the functional relationship between signal variance and frequency. The cepstral domain feature is the Mel-scale cepstral coefficient feature, which is a perceptual frequency domain cepstral coefficient. Based on the human auditory mechanism, a group of H triangular bandpass filter groups are arranged from low frequency to high frequency according to the size of the critical bandwidth. The number of filter groups and the number of critical bands must be close, generally 22-26. After preprocessing the input signal, perform Fourier transform, then pass it through the filter group, calculate the logarithmic energy of each filter group output, and then perform discrete cosine transform to obtain the MFCC coefficients; MFCC feature calculation formula: Where: S represents the Mel filter energy, kg / s 2 ; The subscript i represents the frame number; h represents the Mel filter number; n represents the spectrum line after discrete cosine transform; It is used to perform automatic gain control on voiceprint signals, and is mainly composed of two parts: a gain amplifier and a feedback loop. The gain amplifier component uses a certain AGC algorithm to adjust the gain value according to the amplitude and threshold of the feedback loop in the system. That is, the AGC adjusts the automatic control of the signal by the strength of the automatic gain signal in the amplifying circuit. When the signal strength increases, the control of the AGC system feedback loop is reduced accordingly according to a certain relationship. Conversely, when the signal amplitude in the AGC system gain amplifier decreases, the gain of the feedback loop will be increased accordingly according to a certain relationship between the two. Through this method, after the AGC algorithm amplifies and adjusts, it is ensured that the amplitude of the communication system signal output can be basically maintained at a constant state.

10. The method for designing a low-power voiceprint sensor suitable for detecting mechanical faults of high-voltage switchgear circuit breakers according to claim 9, characterized in that: The steps of the automatic gain control are as follows: S601: Determine the dynamic range of the audio signal output: The design is based on the audio signal processing using the AGC algorithm and needs to be determined according to the actual situation of the audio signal; If, during the design process, the expected value in the practical AGC system is a fixed value, the amplitude of the audio signal output will approach the expected value of the AGC system; in the process of approaching this expected value, the output signal will be continuously adjusted, which will cause instability in the amplitude of the signal output. To ensure the stability of the audio signal output, based on the AGC expected value, a dynamic range of stable audio signal output is designed and determined with the expected value as the center; when the amplitude of the audio signal is within this dynamic range during the AGC algorithm adjustment, it is determined that the output amplitude of the audio signal is stable; S602: Determine the gain adjustment speed: To determine, ensure, and present the normal change trend of the voice signal amplitude, it is necessary to ensure that its gain remains unchanged or varies within a small range, thereby ensuring that the amplitude of the audio signal is not distorted during transmission. The interference encountered during audio signal transmission makes the average amplitude of the audio signal relatively small, so its gain value needs to be increased to ensure that the overall amplitude of the signal is increased, thereby ensuring that the audio signal is not distorted. When applying practical AGC to audio signals, its gain value needs to change slowly and quickly, changing suddenly and quickly with the slow change of the signal amplitude. When the audio signal gain adjustment time is longer than the normal adjustment time of the audio signal, the gain value will not change significantly at this time. The gain adjustment time should be set to 4 seconds based on the characteristics of the audio signal output. If the audio signal output is large, the gain should be reduced quickly at this time. Failure to reduce the gain adjustment will cause damage to the device. When the signal output value exceeds the upper limit of the expected value, the gain adjustment time should be set to 0.5ms. The gain calculation formula of the practical AGC algorithm is: Among them, A(n+1) is the gain value after signal transmission adjustment; A(n) is the gain value before adjustment; A in is the signal amplitude value; A0 is the expected amplitude value; 2 -a is a constant; different values ​​of a will result in different AGC adjustment speeds. According to the actual requirements of the practical AGC algorithm, when the signal output amplitude is less than the upper limit of the expected value, a=16, the gain adjustment will be slower. If a=3, the gain adjustment speed will be faster. S603: Determine the dynamic range of the input signal: During the audio signal input process, if the input value range is set too large, the signal will generate greater noise during transmission, affecting the input and output quality of the signal; on the contrary, if the set range of the audio input signal is smaller, the signal will be ignored due to its small size, thereby causing distortion of the audio input signal; based on the input time of the audio signal, a signal strength of 36dB is determined as noise; the input dynamic range of the audio signal is determined using the AGC algorithm, and the noise can be judged whether it occurs in the gap of the audio signal transmission based on the existence time of the noise. If the existence time of the noise is relatively short, it can be regarded as a gap in the audio signal; usually, the noise judgment time is set to 5s for the best; based on the existence time of the noise and the actual situation of the signal input, the range of about 6dB between the minimum value of the audio signal and the noise threshold is determined as the dynamic range of the audio signal input; within this range, the interference intensity of the noise on the audio signal is relatively high, so the gain value needs to be fixed; S604: Limit the gain value: To avoid excessive gain and damage to signal transmission devices or equipment, the gain value needs to be controlled within a range. If the value calculated by the AGC is greater than the upper limit of the range, the gain value takes the maximum value, otherwise it takes the minimum value. Based on the actual transmission of the audio signal, the gain range is -3-30dB.

Images