Device sound source positioning method and apparatus, electronic device, and machine-readable storage medium

Abstract

This invention provides a method, apparatus, electronic device, and machine-readable storage medium for locating a sound source in a device, belonging to the field of acoustic monitoring technology. The device includes a device wall and internal components, and at least two acoustic sensors are installed on the device to collect acoustic signals. The method includes: acquiring acoustic signals over a period of time during device operation; extracting modal features of the acoustic signals; and determining the component generating the acoustic signals based on the modal features, wherein the generating component includes the device wall and internal components. This invention, by extracting the modal features of the acoustic signals, achieves the location of the acoustic signal source, accurately identifying the component generating the acoustic signals, and providing a basis for subsequent fault analysis.

Description

Technical Field

[0001] This invention relates to the field of acoustic monitoring technology, specifically to a method for locating a sound source in a device, a device for locating a sound source in a device, an electronic device, and a machine-readable storage medium. Background Technology

[0002] The operational status of equipment directly affects the operational safety and economic benefits of an enterprise. Equipment includes both dynamic and static equipment. For static equipment, fault diagnosis primarily involves process flow simulation and online measurement of process parameters. Process flow simulation enables optimized control and operation of chemical processes, but its successful application presupposes that the equipment structure is "normal." When equipment experiences unpredictable mechanical failures due to scaling, corrosion, vibration, overload, or other reasons, or when operating conditions exceed the model's prediction range, it becomes difficult to accurately determine whether the equipment's operational status is normal. Online testing technologies for process parameters such as temperature, pressure, flow rate, and composition during equipment operation can only provide superficial information about the equipment's operational status. Once mechanical or operational faults occur, these conventional detection methods are insufficient to pinpoint the root cause.

[0003] Therefore, existing technologies have proposed using acoustic signals for equipment fault detection. However, since the equipment walls and internal components are interconnected or close to each other, when any component of the wall or internal component generates an acoustic signal, the acoustic sensors installed on the wall or internal component may collect the corresponding acoustic signal. Therefore, when using acoustic sensors to collect acoustic signals, there is a situation of signal mixing. Existing technologies have proposed a method based on a combination of signal strength and acoustic signal acquisition time to locate the signal source in order to locate the acoustic signal generating component. However, the existing technology has the problems of complex processing methods and poor positioning accuracy. Summary of the Invention

[0004] The purpose of this invention is to provide a method, apparatus, electronic device, and machine-readable storage medium for locating a sound source in a device, so as to at least solve the problems of complex processing methods and poor positioning accuracy in the prior art.

[0005] To achieve the above objectives, a first aspect of the present invention provides a method for locating a sound source in a device, the device comprising a device wall and internal components, wherein at least two acoustic sensors are disposed on the device, the acoustic sensors being used to collect acoustic signals, and the method comprising:

[0006] Acquire acoustic signals from the device over a period of time.

[0007] Extract modal features of acoustic signals;

[0008] Based on the modal characteristics, the acoustic signal generating component is determined, and the generating component includes the device wall and internal components.

[0009] Optionally, modal features of the acoustic signal can be extracted, including:

[0010] The acoustic signal is processed using synchronous compressed wavelets to obtain its modal characteristics.

[0011] Optionally, the equipment includes petrochemical equipment;

[0012] The internal components include at least one of a wing valve, a double-acting slide valve, a tray, a float valve, and a tube bundle.

[0013] Optionally, based on the modal characteristics, the acoustic signal generating component is determined, including:

[0014] The first preset mode is reconstructed from the modal features to obtain the time-domain signal that first arrives at the preset mode;

[0015] Based on the time-domain signal that arrives first in the preset mode, the arrival time of the acoustic signal within that operating time period is determined.

[0016] The component that generates the acoustic signal is determined based on the arrival time of the acoustic signal.

[0017] Optionally, inverse wavelet transform can be used to reconstruct the preset mode that arrives first in the modal features.

[0018] Optionally, based on the time-domain signal that first arrives at the preset mode, the arrival time of the acoustic signal within this operating time period is determined, including:

[0019] The signal envelope is obtained by processing the time-domain signal that first arrives at the preset mode using the Hilbert transform.

[0020] Based on the signal envelope, determine the global maximum point;

[0021] The time window is determined based on the global maximum value and the preset time delay;

[0022] Within the time window, the first global minimum point is calculated using the AIC function;

[0023] Based on the first global minimum point, the range of the neighborhood is determined, the midpoint of the neighborhood is the first global minimum point, and the length of the neighborhood is a preset time delay.

[0024] Within the specified range, the second global minimum point is calculated using the AIC function and used as the arrival time of the acoustic signal.

[0025] Optionally, the acoustic signal generating component is determined based on the acoustic signal arrival time, including:

[0026] At least one time difference value is obtained by subtracting the arrival times of the acoustic signals from each acoustic sensor.

[0027] Based on the time difference values, a signal time difference matrix is ​​constructed;

[0028] Based on the aforementioned signal time difference matrix, multiple spatial distances are obtained;

[0029] From the spatial distance, a predetermined number of minimum spatial distances that can form a predetermined shape are determined as the acoustic signal generation area, so as to determine the acoustic signal generation component.

[0030] A second aspect of the present invention provides a device for locating a sound source in a device. The device includes a device wall and internal components. At least two acoustic sensors are disposed on the device for collecting acoustic signals. The device includes:

[0031] The data acquisition module is used to acquire acoustic signals from the device over a period of time.

[0032] The feature extraction module is used to extract the modal features of acoustic signals;

[0033] A component determination module is used to determine the component that generates the acoustic signal based on the modal characteristics. The component that generates the signal includes the device wall and internal components.

[0034] Optionally, the feature extraction module is specifically used for:

[0035] The acoustic signal is processed using synchronous compressed wavelets to obtain its modal characteristics.

[0036] Optionally, the equipment includes petrochemical equipment;

[0037] The internal components include at least one of a wing valve, a double-acting slide valve, a tray, a float valve, and a tube bundle.

[0038] A third aspect of the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-described device sound source localization method.

[0039] On the other hand, the present invention provides a machine-readable storage medium storing instructions for causing a machine to perform the above-described device sound source localization method.

[0040] This technical solution extracts the modal features of acoustic signals to locate the source of the acoustic signals, accurately identifying the components that generate the acoustic signals and providing a basis for subsequent fault analysis to accurately determine the faulty components.

[0041] Other features and advantages of the embodiments of the present invention will be described in detail in the following detailed description section. Attached Figure Description

[0042] The accompanying drawings are provided to further illustrate embodiments of the present invention and form part of the specification. They are used together with the following detailed description to explain the embodiments of the present invention, but do not constitute a limitation thereof. In the drawings:

[0043] Figure 1 This is a flowchart of the device sound source localization method provided by the present invention;

[0044] Figure 2 This is a flowchart of the process for determining the acoustic signal generation component based on modal characteristics, provided by the present invention.

[0045] Figure 3 This is a flowchart for determining the arrival time of an acoustic signal provided by the present invention;

[0046] Figure 4 This is a flowchart of the acoustic signal generation component based on the arrival time of the acoustic signal, provided by the present invention.

[0047] Figure 5 This is a comparative diagram of the arrival times of acoustic signals calculated using different methods, provided by the present invention.

[0048] Figure 6 This is a schematic diagram of the structure of the sound source localization device provided by the present invention.

[0049] Explanation of reference numerals in the attached figures

[0050] 10 - Data acquisition module; 20 - Feature extraction module; 30 - Component determination module. Detailed Implementation

[0051] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0052] Figure 1 This is a flowchart of the device sound source localization method provided by the present invention; Figure 2 This is a flowchart of the process for determining the acoustic signal generation component based on modal characteristics, provided by the present invention. Figure 3 This is a flowchart for determining the arrival time of an acoustic signal provided by the present invention; Figure 4 This is a flowchart of the acoustic signal generation component based on the arrival time of the acoustic signal, provided by the present invention. Figure 5 This is a schematic diagram of the structure of the sound source localization device provided by the present invention.

[0053] Example 1

[0054] like Figure 1 As shown, an embodiment of the present invention provides a method for locating a sound source in a device, the device comprising a device wall and internal components, the method comprising:

[0055] Step S1: Acquire acoustic signals from the device during a certain operating time;

[0056] Step S2: Extract the modal features of the acoustic signal;

[0057] Step S3: Based on the modal characteristics, determine the acoustic signal generating component, which includes the device wall and internal components.

[0058] Specifically, in this embodiment, the equipment applicable to this solution is petrochemical equipment, that is, it can be used in the chemical industry. Petrochemical equipment may include components with the same damage and failure mechanism, such as at least one of reaction equipment, concentration and crystallization equipment, filtration and separation equipment, drying equipment, molding equipment, mixing, homogenizing and emulsifying equipment, pulverizing and grinding equipment, feeding and conveying equipment, hydrogen production equipment, flue gas energy recovery equipment, fluid mechanics experimental equipment, heat exchange equipment and refrigeration equipment. Each of the above-mentioned equipment includes at least one internal component; the internal component may include at least one of the following: wing valve, double-acting slide valve, tray, float valve, tube bundle, and components with the same working principle as wing valve, double-acting slide valve, tray, float valve or tube bundle. For internal components where acoustic sensors can be installed, acoustic signals are collected by setting at least one acoustic sensor on both the device wall and the internal component. For internal components where acoustic sensors cannot be installed, at least two acoustic sensors are set on the device wall, with at least one of them located within a predetermined distance (e.g., within 30cm of the connection point) between the internal component and the device wall. This sensor arrangement ensures that acoustic signals can be collected even for internal components where acoustic sensors cannot be installed, while also ensuring that the component generating the acoustic signal can be accurately identified.

[0059] More specifically, in this embodiment, modal features include at least one of frequency features, amplitude features, time-domain features, and phase features. Frequency features include center frequency, frequency bandwidth, and frequency resolution. Center frequency is an important parameter describing the frequency distribution of an acoustic signal. It roughly represents the frequency location where the energy of the acoustic signal is concentrated. For example, for a narrowband noise signal, its center frequency can well reflect the main frequency component of the noise. In speech signals, the center frequency of vowels can help distinguish different vowels; for example, the center frequencies of / a / and / i / are significantly different, with / a / having a relatively low center frequency and / i / having a higher center frequency. Frequency bandwidth refers to the frequency range contained in an acoustic signal. Wideband signals contain richer frequency components and can convey more detailed information. For example, symphonic signals in music have a wide frequency bandwidth, covering a variety of frequency components from low-frequency drumbeats to high-frequency string sounds. Narrowband signals, on the other hand, have a narrow frequency range; for example, simple monotone signals, such as pure tone signals in pure tone audiometry, have a narrow frequency bandwidth and typically only have one main frequency component. This refers to the fineness with which an acoustic signal can be distinguished along the frequency axis. High frequency resolution allows for more accurate analysis of the characteristics of different frequency components in an acoustic signal. In spectrum analysis, frequency resolution can be improved by increasing the data length or employing more advanced algorithms (such as high-resolution FFT algorithms). For example, when analyzing complex machine vibration acoustic signals to detect faults, high frequency resolution helps distinguish subtle frequency differences between normal operation and fault conditions, as different fault modes may produce subtle frequency variations within specific frequency ranges.

[0060] Amplitude characteristics include peak amplitude, root mean square (RMS) amplitude, and amplitude dynamic range. Peak amplitude is the maximum amplitude value reached by an audio signal over a period of time. It is crucial for assessing the upper limit of an audio signal's intensity. For example, peak amplitude is a key indicator when evaluating the performance of audio equipment. If the peak amplitude of an audio signal exceeds the processing range of the equipment, it will lead to clipping distortion. In acoustic measurements, such as measuring the intensity of an explosion, peak amplitude can be used to measure the maximum sound pressure level generated at the moment of the explosion. RMS amplitude is a statistical description of the amplitude of an audio signal, reflecting its average power. In audio processing, it reflects the actual loudness of the sound better than peak amplitude. For example, for a periodic audio signal, RMS amplitude can be obtained by averaging the square of the signal amplitude over one period and then taking the square root. In audio broadcasting, RMS amplitude is often used to control the broadcast level of the audio signal to ensure that the loudness heard by the listener is relatively stable. Amplitude dynamic range refers to the difference between the maximum and minimum amplitude of an audio signal. It reflects the degree of variation in the amplitude of the audio signal. In musical performances, symphonic music typically has a wider dynamic range, ranging from very soft (ppp) to very loud (fff), while some electronic music may have a relatively smaller dynamic range. A good audio system should be able to reproduce the dynamic range of the sound signal well, thus providing listeners with a more realistic auditory experience.

[0061] Temporal characteristics include waveform shape, rise time and fall time, and duration. The waveform shape of an acoustic signal can intuitively reflect the characteristics of the sound. For example, a sine wave is the simplest waveform, representing a pure tone of a single frequency. Complex speech or music signals, on the other hand, have waveforms composed of multiple frequency components. Different waveforms, such as square waves and triangle waves, also have their own unique acoustic characteristics. Square waves contain abundant odd harmonics, and their spectral energy distribution differs significantly from that of sine waves; this difference in waveform shape leads to different timbre effects. Rise time refers to the time it takes for an acoustic signal to rise from its initial amplitude to its peak amplitude, while fall time is the time it takes for the peak amplitude to fall to a lower amplitude. In pulsed acoustic signals, rise time and fall time are crucial for describing the characteristics of the pulse. For example, in acoustic imaging technology, the rise time and fall time of short pulsed acoustic signals (such as ultrasonic pulses) affect the imaging resolution. Short rise and fall times can make the pulse sharper, thereby improving imaging accuracy. The duration of an acoustic signal is also important for identifying the type of sound. For example, short-duration sounds might be transient sounds like key presses or clicks, while long-duration sounds might be continuous background music or spoken words. Audio editing software allows you to create different audio effects by trimming the duration of sound signals, such as editing a long piece of background music into a short clip suitable as a ringtone.

[0062] Phase characteristics include initial phase and phase difference. Initial phase is the phase value of the acoustic signal at the start of the signal. When multiple acoustic signals of the same frequency are superimposed, different initial phases will cause changes in the amplitude and waveform shape of the synthesized signal. For example, in audio spatial positioning technology, by adjusting the initial phase of sounds of the same frequency emitted by different speakers, the perceived spatial location of the sound can be altered. Phase difference refers to the phase difference between two or more acoustic signals. In a stereo system, the phase difference between the left and right channel sounds is crucial for creating a sense of spatiality. When the phase difference between the left and right channel sounds is appropriate, the listener can perceive that the sound is coming from a specific direction. Furthermore, in acoustic signal processing, such as beamforming technology, directional reception of sound sources can be achieved by controlling the phase difference between the acoustic signals received by different array elements.

[0063] Example 2

[0064] This embodiment also provides a method for extracting modal features of acoustic signals, and a method for determining the acoustic signal generating component based on the modal features, such as... Figure 2 As shown, it specifically includes:

[0065] First, synchronous compressed wavelet transform (wavelet decomposition, frequency redistribution based on wall / internal component modal features, and synchronous compression) is used to extract different modal features of the signals received by each sensor. A suitable wavelet basis is selected based on the wall / internal component modal features; instantaneous frequency is calculated, and differentiation and phase analysis are performed: the time derivative of each scale component in the wavelet coefficient matrix is ​​calculated to determine its phase change rate. The instantaneous frequency is calculated using the phase change rate, reflecting the change of the acoustic signal frequency over time. Time-frequency rearrangement (synchronous compression): a time-frequency plane is constructed: the time-scale plane is converted into a time-frequency plane to more intuitively observe the time-frequency characteristics of the acoustic signal. The time-frequency spectrum is rearranged: based on the calculated instantaneous frequency, the wavelet coefficients are rearranged, i.e., the wavelet coefficients near the instantaneous frequency are squeezed, making the energy more concentrated near the instantaneous frequency. This step improves the time-frequency resolution, making the time-frequency representation of the acoustic signal clearer and more accurate. The synchronous compressed wavelet transform result is obtained: the time-frequency representation after synchronous compression is the result of the synchronous compressed wavelet transform. This result has higher resolution in the time-frequency domain and can more accurately reflect the time-frequency characteristics of the acoustic signal.

[0066] Among them, such as Figure 2 As shown, step S3, which determines the acoustic signal generating component based on modal features, specifically includes:

[0067] S31. Reconstruct the first preset mode from the modal features to obtain the time domain signal that first arrives at the preset mode;

[0068] Data reconstruction involves transforming preset modal components from one geometric form to another, and from one format to another. This includes structural conversion, format conversion, and type replacement (data splicing, data trimming, data compression, etc.) to achieve uniformity in the structure, format, and type of preset modal component data, and to connect and fuse multi-source and heterogeneous data. Specifically, in this embodiment, based on the time-frequency ridge trend of different modal signals, the mode of the acoustic signal is determined from the time-frequency ridge of the high-resolution time-frequency analysis results. Acoustic components of different frequencies are identified, and the variation law of acoustic modes over time is extracted to determine the fastest main characteristic mode (which may be S0, A0, S1, A1, etc., with different preset modes corresponding to the container wall and different internal components), which is the first preset mode to arrive. Furthermore, inverse wavelet transform is used to reconstruct the first preset mode from the modal features to obtain the time-domain signal that arrives first.

[0069] S32. Based on the time-domain signal that arrives first in the preset mode, determine the arrival time of the acoustic signal of each acoustic sensor during the operating time.

[0070] Specifically, such as Figure 3 As shown, the arrival time of the acoustic signal for each acoustic sensor is determined in the following manner:

[0071] S321. Based on the acoustic signal, the time-domain signal that first arrives at the preset mode is processed using Hilbert transform to obtain the signal envelope;

[0072] Specifically, in this embodiment, the signal envelope is calculated using the following formulas (3) and (4):

[0073]

[0074] S322. Based on the signal envelope, determine the global maximum value point;

[0075] S323. Determine the time window based on the global maximum value point and the preset time delay;

[0076] More specifically, the global maximum point is the time point corresponding to the maximum value on the ordinate, and the time window N is set between the starting point t0 and t1. MAX +t AM Between, where t MAX The global maximum point, t AM The set time delay is defined as one cycle of the fastest mode of the signal in the structure, thereby ensuring that at least one preset mode arrives first within the entire time window.

[0077] S324. Within the time window, the first global minimum point is calculated using the AIC function;

[0078] The function definition of AIC is as follows:

[0079] AIC(t w )=t w ·log(var(R w (t w ,1))) +(T w -t w -1)·log(var(R w (1+t w ,T w )))(1)

[0080] S325. Determine the range of the domain based on the first global minimum point;

[0081] Wherein, the midpoint value of the domain range is the first global minimum point, and the length of the domain range is a preset time delay;

[0082] S326. Within the stated area, the second global minimum point is calculated using the AIC function and used as the arrival time of the acoustic signal.

[0083] Determining the start time of transient fault signals is crucial for improving source localization accuracy, and accurately acquiring the arrival time of acoustic emission signals at the sensor is key. Traditionally, a first-crossing-threshold method is used, and the choice of threshold is very important. A low threshold will cause premature triggering, while a high threshold will reduce localization accuracy. For signals with very small amplitude or high noise, this method will lead to localization errors. The AIC algorithm can accurately determine the arrival time of acoustic emission signals, and experimental verification shows that the AIC function outperforms the Hinkley standard in a range of signal-to-noise ratios.

[0084] Based on the traditional AIC algorithm, a two-step AIC algorithm based on dispersion curves is proposed, which can extract arrival times more accurately. The AIC information criterion is a standard for measuring the goodness of fit of a statistical model. It is based on the concept of entropy and can balance the complexity of the estimated model with its goodness of fit to the data. A time series can be divided into two locally stable periods, corresponding to the non-informative part (noise) and the informative part (signal) of the time series containing the first arrival of the acoustic emission event. Each period can be fitted by an autoregressive model. Assuming that the experimental data time series contains the start time of the acoustic emission event, the sequence is divided into two periods at the moment the acoustic emission wave begins: the pre-arrival time and the post-arrival time. The AIC function returns a minimum value, which occurs at the moment the signal begins. The starting position of the signal can be easily determined through image analysis.

[0085] The time series R is divided into two parts, with w as the dividing point, w∈[1,N], N is the length of the signal, var is the variance function, and T W For the last sample in the time series, t W For any sample in the time series, R W (t W ,1) indicates that the variance function is calculated from the starting point to the current value t. W R W (1+t W ,T W The variance function is calculated over a range of 1 + t. W To T W All sample values. The variance function var is defined as:

[0086]

[0087] However, when using AIC to select the arrival time, the time of the absolute minimum may differ significantly from the signal arrival time. This can happen, for example, when multiple modes arrive at different times, and a later-arriving mode is stronger than the first-arriving mode. This can severely impact positioning accuracy. Furthermore, the performance of AIC largely depends on the choice of the time window N. At low signal-to-noise ratios, the effect of AIC is also not significant. In addition, different modes propagate at different speeds and exhibit dispersion characteristics. Moreover, different modes have different amplitudes, and large differences in amplitude can cause serious problems in conventional positioning, thus affecting positioning accuracy.

[0088] To accurately identify arrival times, the time window N is configured such that it begins within the noise, starts at the beginning of the original signal, and ends after the signal amplitude reaches its maximum value. After this point, only the final portion of the acoustic emission event, its reflection, and the noise remain, affecting the AIC function result. Arrival time can be indicated by variations in either frequency or amplitude within the time series; the characteristic function should enhance this variation, improving the resolution between noise and acoustic emission signals. The original shape of the signal is characterized by the easily computed and widely used absolute value function CF(i) = |R(i)|, and the signal envelope calculated using the Hilbert transform.

[0089] To verify the applicability of the optimized AIC algorithm and accurately extract the arrival time of the acoustic emission wave, a numerical simulation of ultrasonic guided wave propagation was performed using multiphysics simulation software. A sinusoidal excitation was applied to simulate a single-frequency acoustic emission excitation signal, with its center frequency fc set to 150 kHz. In the numerical simulation, the finite element boundary conditions were free to ensure comparability between the simulation and subsequent experiments. Tetrahedral mesh elements were generated in all finite element models. To ensure computational accuracy and convergence, at least 20 meshes were required for the wavelength of the highest frequency wave. The time step should not exceed 1 / 20 of the highest frequency; since the studied frequency was around 150 kHz, the time step and the maximum element mesh size could be 0.3 μs and 0.8 mm, respectively. Receiver points were designed at distances of 100 mm, 100 mm, and 141 mm from the excitation point in three different directions to comprehensively verify the time extraction capability of the optimized AIC.

[0090] like Figure 5 As shown in the diagram, this invention provides a comparison between the arrival time extracted by the optimized AIC (The second AIC) and the arrival time extracted by the traditional AIC (The first AIC). In the diagram, the horizontal axis represents time, and the vertical axis represents the normalized amplitude. The arrival time is extracted using both the optimized AIC and the traditional AIC, and then compared with the theoretically calculated time. The comparison between the arrival time extracted by the two algorithms and the theoretical arrival time shows that the optimized AIC is superior to the traditional AIC, and the determined arrival time is very close to the theoretical arrival time.

[0091] The mean absolute error of arrival time extraction by the two algorithms can be calculated using formula (6), and the accuracy of arrival time extraction by both algorithms can be evaluated.

[0092]

[0093] Among them, t calc,i t represents the arrival time calculated and extracted by the algorithm. method,i The arrival time is the theoretically calculated value, and N is the number of sensors. Detailed results are shown in Table 1. Compared to the traditional AIC, the mean absolute error of the arrival time using the optimized AIC decreased from 6.88 μs to 2.68 μs, demonstrating that the optimized AIC can improve the accuracy of arrival time extraction and reduce errors. The standard deviation of the arrival time extracted by the two algorithms can be calculated using formula (7) to evaluate their accuracy.

[0094] As shown in Table 1, the standard deviation decreased from 6.91 μs to 2.92 μs, which proves that optimizing AIC can reduce dispersion, make the positioning system more stable, and improve the accuracy of time of arrival extraction.

[0095] Table 1. MAE and Standard Deviation of First Arrival Time

[0096]

[0097] In this scheme, the first step of the algorithm is applicable to most acoustic emission signals, but the accuracy of the first step decreases when the amplitude of the first incident mode is very small compared to that of subsequent incident modes. In such signals, changing the time window setting eliminates this limitation, and the second step improves the estimation of the first step to enhance accuracy and precision.

[0098] S33. Based on the arrival time of the acoustic signals from each acoustic sensor, determine the component that generates the acoustic signals.

[0099] Specifically, such as Figure 4 As shown, based on the arrival time of the acoustic signals from each acoustic sensor, the components that generate the acoustic signals are determined, specifically including:

[0100] S331. Subtract the arrival times of the acoustic signals from each acoustic sensor to obtain at least one time difference value;

[0101] S332. Based on the time difference value, construct the signal time difference matrix.

[0102] S333. Based on the signal time difference matrix, multiple spatial distances are obtained;

[0103] Specifically, based on the signal time difference matrix, multiple spatial distances are obtained, including:

[0104] The spatial distance is calculated using the following formula:

[0105]

[0106] Among them, D k For spatial distance; TR(i,j) is the signal time difference matrix; R k (i,j) is the preset time difference matrix.

[0107] S334. Determine the region corresponding to a preset number of minimum spatial distances that can form a preset shape from the spatial distance, and use it as the acoustic signal generation region to determine the acoustic signal generation component.

[0108] Specifically, the preset shape and preset quantity correspond to each other and are both related to the arrangement of the acoustic sensors. If the acoustic sensors are arranged in a rectangular structure on the device wall, the regions corresponding to the four minimum spatial distances that can form a rectangle are determined from the spatial distances and used as the acoustic signal generation regions.

[0109] Specifically, in this embodiment, to locate the acoustic signal generation area, a novel positioning method based on a time difference matrix is ​​proposed. This method is an improvement on the traditional Delta T technique. The traditional Delta T technique uses a threshold crossover method to determine the wave arrival time. However, simple threshold crossover can lead to incorrect positioning because the signal initially falls below the threshold level. The preprocessing of the provided positioning method is the same as that of the traditional Delta T. First, an artificial source needs to be created in advance. By constructing signals of wall cracks and internal component damage through the artificial source, a preset time difference matrix can be obtained.

[0110] A rectangular node grid is constructed on the device wall, with four nodes forming one grid area. Signals are acquired, and after optimization using the AIC algorithm, the arrival time of each node relative to each sensor is obtained to construct the time difference value. When there are N sensors, for each node, there exists... Time difference. This Group time difference constructs a {1×C}N 2 A one-dimensional matrix.

[0111] These one-dimensional time difference matrices are all contained in the time difference matrix library. The time difference of each sensor pair is calculated based on the actual acoustic emission event of the fault, resulting in a one-dimensional matrix composed of a set of time difference values. This matrix is ​​compared with k matrices in the time difference matrix library to calculate the spatial distance, obtaining k distinct spatial distances. By iterating through these k values, the nodes corresponding to the four smallest spatial distances that can form a complete rectangle are found, thus determining the region of the fault source and ultimately identifying the acoustic signal generating component.

[0112] The smaller the calculated spatial distance, the more matched the two time difference matrices are; otherwise, the greater the difference between them.

[0113] Example 3

[0114] To verify the effectiveness of the acoustic signal generating component identification method, 50 acoustic signals were triggered on both the vessel wall and the internal components to locate the acoustic signal generating component. These signals were then collected using acoustic sensors on both the vessel wall and the internal components. The actual number of collected signals was 86 on the vessel wall and 93 on the internal components, indicating some signal duplication. However, analysis using the above method confirmed that 50 signals were emitted from both the vessel wall and the internal components. Therefore, the proposed method for locating the acoustic signal generating component can pinpoint the specific component that generates the acoustic signal, thereby enabling fault diagnosis of that specific component.

[0115] Example 4

[0116] This embodiment provides a device for locating a sound source in a device. The device includes a device wall and internal components. At least two acoustic sensors are installed on the device. These acoustic sensors are used to collect acoustic signals, such as… Figure 6 As shown, the device includes:

[0117] Data acquisition module 10 is used to acquire acoustic signals of the device during a certain operating time;

[0118] Feature extraction module 20 is used to extract modal features of acoustic signals;

[0119] The component determination module 30 is used to determine the component that generates the acoustic signal based on the modal characteristics. The component that generates the signal includes the device wall and internal components.

[0120] Furthermore, the feature extraction module 20 is specifically used for:

[0121] The acoustic signal is processed using synchronous compressed wavelets to obtain its modal characteristics.

[0122] Furthermore, the equipment includes petrochemical equipment;

[0123] The internal components include at least one of a wing valve, a double-acting slide valve, a tray, a float valve, and a tube bundle.

[0124] Furthermore, the component determination module 30 specifically includes:

[0125] The data reconstruction module is used to reconstruct the earliest arriving preset mode from the modal features to obtain the time domain signal that arrives earliest at the preset mode;

[0126] The arrival time determination module is used to determine the arrival time of the acoustic signal within the operating time based on the time-domain signal that arrives first in the preset mode.

[0127] The component determination module is used to determine the component that generates the acoustic signal based on the arrival time of the acoustic signal.

[0128] Furthermore, inverse wavelet transform is used to reconstruct the preset mode that arrives first in the modal features.

[0129] Furthermore, the arrival time determination module specifically includes:

[0130] The signal transformation module is used to process the time-domain signal that first arrives at the preset mode using Hilbert transform to obtain the signal envelope;

[0131] The maximum value point determination module is used to determine the global maximum value point based on the signal envelope;

[0132] The time window determination module is used to determine the time window based on the global maximum value and a preset time delay;

[0133] The first minimum point determination module is used to calculate the first global minimum point using the AIC function within the time window.

[0134] The domain range determination module is used to determine the domain range based on the first global minimum point, wherein the midpoint value of the domain range is the first global minimum point, and the length of the domain range is a preset time delay;

[0135] The second minimum point determination module is used to calculate the second global minimum point within the said domain using the AIC function, and use it as the arrival time of the acoustic signal.

[0136] Furthermore, the component determination module specifically includes:

[0137] The time difference determination module is used to obtain at least one time difference value by subtracting the arrival times of the acoustic signals from each acoustic sensor.

[0138] A time difference matrix determination module is used to construct a signal time difference matrix based on the time difference values;

[0139] A spatial distance determination module is used to obtain multiple spatial distances based on the signal time difference matrix;

[0140] The generation area determination module is used to determine the area corresponding to a preset number of minimum spatial distances that can form a preset shape from the spatial distance, as the acoustic signal generation area, so as to determine the acoustic signal generation component.

[0141] Example 5

[0142] This embodiment provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the device sound source localization method described above.

[0143] Example 6

[0144] This embodiment provides a machine-readable storage medium storing instructions that cause a machine to perform the above-described device sound source localization method.

[0145] Those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing related hardware. This program is stored in a storage medium and includes several instructions to cause a microcontroller, chip, or processor to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0146] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy distinction and are not intended to limit the scope of protection of this invention.

[0147] The optional embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the embodiments of the present invention are not limited to the specific details described above. Within the scope of the technical concept of the embodiments of the present invention, various simple modifications can be made to the technical solutions of the embodiments of the present invention, and these simple modifications all fall within the protection scope of the embodiments of the present invention. It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. To avoid unnecessary repetition, the embodiments of the present invention will not further describe the various possible combinations.

[0148] Furthermore, various different embodiments of the present invention can be combined in any way, as long as they do not violate the spirit of the embodiments of the present invention, they should also be regarded as the content disclosed by the embodiments of the present invention.

Claims

1. A method for locating a sound source in a device, characterized in that, The device includes a device wall and internal components, and at least two acoustic sensors are provided on the device for collecting acoustic signals. The method includes: Acquire acoustic signals from the device over a period of time. Extract modal features of acoustic signals; Based on the modal characteristics, the acoustic signal generating component is determined, and the generating component includes the device wall and internal components.

2. The device sound source localization method according to claim 1, characterized in that, Extracting modal features of acoustic signals, including: The acoustic signal is processed using synchronous compressed wavelets to obtain its modal characteristics.

3. The device sound source localization method according to claim 1, characterized in that, The equipment includes petrochemical equipment; The internal components include at least one of a wing valve, a double-acting slide valve, a tray, a float valve, and a tube bundle.

4. The device sound source localization method according to claim 1, characterized in that, Based on the modal characteristics, the acoustic signal generating components are determined, including: The first preset mode is reconstructed from the modal features to obtain the time-domain signal that first arrives at the preset mode; Based on the time-domain signal that arrives first in the preset mode, the arrival time of the acoustic signal within that operating time period is determined. The component that generates the acoustic signal is determined based on the arrival time of the acoustic signal.

5. The device sound source localization method according to claim 4, characterized in that, The inverse wavelet transform is used to reconstruct the first preset mode from the modal features.

6. The device sound source localization method according to claim 4, characterized in that, Based on the time-domain signal that first arrives at the preset mode, the arrival time of the acoustic signal within this operating time is determined, including: The signal envelope is obtained by processing the time-domain signal that first arrives at the preset mode using the Hilbert transform. Based on the signal envelope, determine the global maximum point; The time window is determined based on the global maximum value and the preset time delay; Within the time window, the first global minimum point is calculated using the AIC function; Based on the first global minimum point, the range of the neighborhood is determined, the midpoint of the neighborhood is the first global minimum point, and the length of the neighborhood is a preset time delay. Within the specified range, the second global minimum point is calculated using the AIC function and used as the arrival time of the acoustic signal.

7. The device sound source localization method according to claim 4, characterized in that, Based on the arrival time of the acoustic signal, the components that generate the acoustic signal are determined, including: At least one time difference value is obtained by subtracting the arrival times of the acoustic signals from each acoustic sensor. Based on the time difference values, a signal time difference matrix is ​​constructed; Based on the aforementioned signal time difference matrix, multiple spatial distances are obtained; From the spatial distance, a predetermined number of minimum spatial distances that can form a predetermined shape are determined as the acoustic signal generation area, so as to determine the acoustic signal generation component.

8. A device for locating the sound source of an equipment, characterized in that, The device includes a device wall and internal components. At least two acoustic sensors are installed on the device for collecting acoustic signals. The apparatus includes: The data acquisition module is used to acquire acoustic signals from the device over a period of time. The feature extraction module is used to extract the modal features of acoustic signals; A component determination module is used to determine the component that generates the acoustic signal based on the modal characteristics. The component that generates the signal includes the device wall and internal components.

9. The device for locating the sound source of an equipment according to claim 8, characterized in that, The feature extraction module is specifically used for: The acoustic signal is processed using synchronous compressed wavelets to obtain its modal characteristics.

10. The device sound source localization device according to claim 8, characterized in that, The equipment includes petrochemical equipment; The internal components include at least one of a wing valve, a double-acting slide valve, a tray, a float valve, and a tube bundle.

11. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the device sound source localization method according to any one of claims 1-7.

12. A machine-readable storage medium, characterized in that, The machine-readable storage medium stores instructions for causing the machine to perform the device sound source localization method according to any one of claims 1-7.

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