Noise source positioning method, apparatus, device, and medium

Abstract

The application relates to the technical field of noise source positioning, and discloses a noise source positioning method, device, equipment and medium. The method comprises the following steps: acquiring a noise signal generated by a product to be measured and geometric models of a plurality of candidate components; performing audio feature analysis on the noise signal to obtain corresponding noise feature information; performing modal analysis on the geometric models to obtain corresponding modal feature information; performing feature matching on the noise feature information and the modal feature information, and determining a target component generating noise among the candidate components based on a feature matching result. The application can improve the positioning accuracy of a noise source.

Description

Technical Field

[0001] This application relates to the field of noise source localization technology, and in particular to a noise source localization method, apparatus, equipment and medium. Background Technology

[0002] Noise is a common problem in mechanical and electromechanical products. Noise not only affects human comfort and harms physical and mental health, but it is also strongly correlated with product reliability.

[0003] For products, noise problems often indicate reliability issues in their mechanical structure, usually caused by poor structural design. Prolonged use of products with poor structural design may lead to failure modes such as wear and tear.

[0004] In related technologies, noise source localization methods mainly include sound pressure level testing and acoustic array testing. However, both of these testing methods have high environmental requirements, are easily affected by external noise, have low positional resolution, cannot correlate noise with the product's vibration structure, and have insufficient localization accuracy. Summary of the Invention

[0005] The purpose of this application is to provide a noise source localization method, apparatus, device, and medium that integrates noise analysis and modal analysis during noise source localization, thereby improving the accuracy of noise source localization.

[0006] This application provides a noise source localization method, including:

[0007] Acquire the noise signal generated by the product under test and the geometric models of several candidate components; the candidate components are parts in the product under test.

[0008] The noise signal is subjected to audio feature analysis to obtain the corresponding noise feature information;

[0009] Modal analysis is performed on the geometric model to obtain the corresponding modal feature information;

[0010] The noise feature information and the modal feature information are matched for features, and the target component that generates noise is determined from each of the candidate components based on the feature matching results.

[0011] In some embodiments, the audio feature analysis of the noise signal includes:

[0012] Frequency response analysis is performed on the noise signal to extract multiple noise dominance frequencies and the noise peak amplitudes corresponding to the noise dominance frequencies, thereby obtaining the noise characteristic information.

[0013] In some embodiments, performing modal analysis on the geometric model includes:

[0014] Based on the finite element analysis method, multi-modal analysis is performed on the geometric model to obtain the translational participation factor and rotational participation factor of the candidate component under each mode.

[0015] When the deviation between the translational participation factor and the rotational participation factor is minimal and within a preset deviation range, the modal frequency of that mode is selected as the natural frequency of the candidate component, and the mode shape value corresponding to the natural frequency of the component is extracted to obtain the modal feature information.

[0016] In some embodiments, the feature matching of the noise feature information and the modal feature information includes:

[0017] Calculate the frequency deviation between the noise dominant frequency in the noise feature information and the component natural frequency in the modal feature information. When the frequency deviation is not greater than a preset frequency deviation threshold, match the noise dominant frequency and the component natural frequency to obtain the feature matching result.

[0018] In some embodiments, determining the moving parts that generate noise in the product under test based on feature matching results includes:

[0019] Based on the feature matching results, the energy distribution characteristic values ​​corresponding to the noise dominance frequency and the component's natural frequency are determined; the energy distribution characteristic values ​​are positively correlated with the peak amplitude of the dominance frequency and the mode shape value of the component's natural frequency, respectively.

[0020] The target component is determined based on the energy distribution characteristic value and the noise peak amplitude in the noise characteristic information.

[0021] In some embodiments, the formula for calculating the energy distribution characteristic value is:

[0022] ,

[0023] in, For the first Energy distribution characteristic value of each rate pair, For the first The mode shape value corresponding to the natural frequency of the component in a frequency pair. For the first Noise amplitude weights are paired with a single rate.

[0024] In some embodiments, the noise source localization method further includes:

[0025] Display the target component;

[0026] Based on the product under test and the target component, generate corresponding optimized design text data.

[0027] This application embodiment also provides a noise source localization device, including:

[0028] The first module is used to acquire noise signals generated by the product under test and geometric models of several candidate components; the candidate components are parts in the product under test.

[0029] The second module is used to perform audio feature analysis on the noise signal to obtain corresponding noise feature information;

[0030] The third module is used to perform modal analysis on the geometric model to obtain the corresponding modal feature information;

[0031] The fourth module is used to perform feature matching on the noise feature information and the modal feature information, and to determine the target component that generates noise among the candidate components based on the feature matching results.

[0032] This application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described noise source localization method.

[0033] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described noise source localization method.

[0034] The beneficial effects of this application are as follows: by combining fusion noise analysis and modal analysis, feature matching is performed on the noise characteristic information of the noise signal and the modal characteristic information of each candidate component in the product under test, and the target component that generates noise in each candidate component is determined based on the feature matching results. This effectively filters out environmental interference components. For multi-source noise problems at the same frequency, the vibration contribution of different candidate components in the product under test can be accurately distinguished, thereby improving the accuracy of noise source localization. Attached Figure Description

[0035] Figure 1 This is an application environment diagram of the noise source localization method provided in the embodiments of this application.

[0036] Figure 2 This is a flowchart of the noise source localization method provided in the embodiments of this application.

[0037] Figure 3 This is a schematic diagram of modal parameters for modal analysis of a geometric model provided in an embodiment of this application.

[0038] Figure 4 This is a schematic diagram of the noise source localization device provided in the embodiments of this application.

[0039] Figure 5 This is a schematic diagram of the hardware structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0040] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0041] It should be noted that although functional modules are divided in the device schematic diagram and a logical order is shown in the flowchart, in some cases, the steps shown may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first," "second," etc., in the specification, claims, and drawings are used to distinguish similar objects and are not used to describe a specific order or sequence.

[0042] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0043] The noise source localization method provided in this application can be executed by a computer device, which can be a terminal device or a server. Terminal devices include, but are not limited to, mobile phones, computers, smart home appliances, vehicle terminals, and aircraft. The server can be a standalone physical server, a server cluster consisting of multiple physical servers, a distributed system, or a cloud server. Furthermore, the information, data, and signals involved in this application's embodiments are all authorized by the relevant parties or have been fully authorized by all parties, and the collection, use, and processing of related data comply with the relevant laws, regulations, and standards of the relevant countries and regions.

[0044] Figure 1 This diagram illustrates the application environment of the noise source localization method provided in the embodiments of this application. (See also...) Figure 1This noise source localization method is applied to a noise source localization system. The system includes a terminal 110 and a server 120. The terminal 110 and server 120 are connected via a network. The terminal 110 can be a desktop terminal or a mobile terminal; the mobile terminal can be at least one of a mobile phone, tablet, or laptop. The server 120 can be a standalone server or a server cluster consisting of multiple servers. The terminal 110 sends the noise signal generated by the product under test and geometric models of several candidate components to the server 120. The server 120 acquires the noise signal generated by the product under test and the geometric models of several candidate components, performs audio feature analysis on the noise signal to obtain corresponding noise feature information, performs modal analysis on the geometric models to obtain corresponding modal feature information, performs feature matching on the noise feature information and modal feature information, and determines the target component generating the noise among the candidate components based on the feature matching results. The candidate components are parts within the product under test.

[0045] It should be understood that Figure 1 The application scenarios shown are merely examples. In practical applications, the noise source localization method provided in this application embodiment can also be applied to other scenarios. For example, the above-described noise source localization method can be directly applied to terminal 110. Terminal 110 is used to acquire the noise signal generated by the product under test and the geometric model of several candidate components, perform audio feature analysis on the noise signal to obtain the corresponding noise feature information, perform modal analysis on the geometric model to obtain the corresponding modal feature information, perform feature matching on the noise feature information and modal feature information, and determine the target component that generates noise among the candidate components based on the feature matching results.

[0046] To facilitate understanding of the noise source localization method provided in this application embodiment, the following example uses terminal 110 as the execution subject to illustrate the application scenario of the noise source localization method.

[0047] Figure 2 This is a flowchart of the noise source localization method provided in an embodiment of this application. Please refer to [link / reference]. Figure 2 In some embodiments, the method includes, but is not limited to, steps S201 to S204.

[0048] Step S201: Obtain the noise signal generated by the product under test and the geometric model of several candidate components.

[0049] In this context, candidate components are parts of the product under test. In some embodiments, the product under test may be a diaphragm pump, and the components of the product under test may be the pump head, motor, diaphragm, and base of the diaphragm pump, etc.

[0050] The noise signal generated by the product under test can be obtained by acquiring the audio generated by the product under test in real time during operation through an audio sensor. The geometric model of the candidate component can be obtained through virtual simulation design and can include the size information, shape information, material information and assembly information of the candidate component.

[0051] As examples, this could involve placing audio sensors near the product under test (DUT), connected to the execution unit, to upload noise signals generated by the DUT to the execution unit in real time. The geometric model of the candidate component could be pre-stored in the execution unit's memory and retrieved by the execution unit.

[0052] Step S202: Perform audio feature analysis on the noise signal to obtain the corresponding noise feature information.

[0053] Noise characteristic information refers to the noise spectrum characteristics extracted through signal processing techniques. Specifically, it can be obtained by using fast Fourier transform for frequency response analysis to identify the dominant frequency and the corresponding noise peak amplitude in the noise characteristic information, which can reflect the vibration energy distribution characteristics of the noise source.

[0054] As examples, audio feature analysis of noise signals can be performed using the analysis functions in audio analysis tools to analyze the noise signal using logarithmic spectrograms, loudness analysis, frequency response, and A-weighted frequency response to obtain the corresponding noise feature information.

[0055] Step S203: Perform modal analysis on the geometric model to obtain the corresponding modal feature information.

[0056] Modal characteristic information refers to the dynamic response characteristics of a component structure under specific vibration modes. Specifically, modal simulation can be performed using finite element software to extract the natural frequencies of the component and the mode shapes corresponding to the natural frequencies, thus characterizing the vibration mode of the candidate component at a specific frequency under the corresponding mode.

[0057] As examples, modal analysis of a geometric model can be performed by using finite element analysis (FEA) software to analyze the modes of candidate components or by using experimental modes to resolve the modal parameters of the candidate components under multiple modes, so as to obtain the corresponding modal characteristic information.

[0058] Step S204: Perform feature matching on the noise feature information and modal feature information, and determine the target component that generates noise among the candidate components based on the feature matching results.

[0059] When the product under test (DUT) produces abnormal howling, the noise signal generated by the DUT during operation is first collected. Spectral analysis is used to identify the dominant frequencies and their corresponding peak noise amplitudes. For example, identifying the two dominant frequencies of 800Hz and 1200Hz yields the corresponding noise characteristic information. Simultaneously, the geometric models of each candidate component (e.g., gears and bearings) are acquired, and modal simulation is performed to obtain the natural frequencies and corresponding mode shapes of each candidate component within a preset frequency range, yielding the corresponding modal characteristic information. Feature matching is then performed between the noise characteristic information and the modal characteristic information of each candidate component. Upon successful matching, the corresponding feature matching result is obtained. Combining the peak noise amplitude corresponding to the dominant frequency and the mode shape corresponding to the component's natural frequency in the feature matching result, the target component generating the noise—that is, the root cause of the noise—is determined among the candidate components. Therefore, by combining noise analysis and modal analysis, feature matching is performed on the noise characteristics of the noise signal and the modal characteristics of each candidate component in the product under test. Based on the feature matching results, the target component that generates noise in each candidate component is determined, effectively filtering out environmental interference components. For multi-source noise problems at the same frequency, the vibration contribution of different candidate components in the product under test can be accurately distinguished, improving the accuracy of noise source localization.

[0060] In some embodiments, audio feature analysis of the noise signal includes: performing frequency response analysis on the noise signal to extract multiple noise dominance frequencies and the noise peak amplitudes corresponding to the noise dominance frequencies, thereby obtaining noise feature information.

[0061] Frequency response analysis refers to converting time-domain noise signals into frequency-domain signals through mathematical transformations. Specifically, it can be implemented using the Fast Fourier Transform algorithm to decompose the distribution of frequency components in the noise signal. Based on frequency response analysis, A-weighted frequency response analysis can be further used.

[0062] The noise-dominant frequency refers to the frequency band in a noise signal where energy is concentrated. Specifically, it can be determined by the frequency corresponding to the peak point whose amplitude exceeds a preset threshold in the frequency response analysis graph, and is used to characterize the main vibration source of the noise signal. The noise peak amplitude refers to the energy intensity corresponding to the noise-dominant frequency, which can be quantified by the ordinate value of each peak point in the frequency response analysis graph, and is used to reflect the magnitude of the contribution of different frequency components to the noise.

[0063] After acquiring the noise signal of the product under test, the noise signal is first input into a signal processing device, where the time-domain waveform is converted into a frequency-domain spectrum using a Fast Fourier Transform (FFT) algorithm. Frequency components in the spectrum with amplitudes exceeding a preset threshold are selected as noise-dominant frequencies; for example, the threshold can be set to 50% of the maximum amplitude of the spectrum. The peak amplitude corresponding to each noise-dominant frequency is directly extracted from the frequency response analysis plot, forming noise feature information containing frequency-amplitude pairs. This information can characterize the main vibrational characteristics of the noise signal, providing a data basis for subsequent matching of the modal characteristics of candidate components. Therefore, by extracting the noise-dominant frequency and its peak amplitude through frequency response analysis, the key frequency characteristics of the noise signal can be quantified, enabling accurate matching with the modal frequencies of candidate components and avoiding the influence of external noise interference on the positioning results.

[0064] In some embodiments, modal analysis of the geometric model includes: performing multi-mode analysis on the geometric model based on the finite element analysis method to obtain the translational participation factor and rotational participation factor of the candidate component under each mode; when the deviation between the translational participation factor and the rotational participation factor is minimal and within a preset deviation range, selecting the modal frequency under that mode as the component natural frequency of the candidate component, extracting the mode shape value corresponding to the component natural frequency, and obtaining modal characteristic information.

[0065] Finite element analysis (FEM) is a numerical method that discretizes a continuous geometric model into a finite number of elements for mechanical analysis. It can be implemented using software such as ANSYS or ABAQUS, and is used to decompose complex structures and calculate their dynamic characteristics. Multimodal analysis (MMA) involves calculating the natural frequencies and mode shapes of components at multiple vibration orders on a geometric model. It can be implemented using the Lanczos algorithm or subspace iteration method, and is used to determine the response characteristics of candidate components under different vibration modes.

[0066] The translational participation factor refers to the contribution of a candidate component's linear displacement to the overall vibration in a specific mode. It is specifically calculated by projecting the unit's displacement vector onto the global coordinate system, and is used to quantify the impact of translational motion on vibration energy. The rotational participation factor refers to the contribution of a candidate component's rotational displacement to the overall vibration in a specific mode. It is specifically calculated by projecting the unit's rotational angle vector onto the global coordinate system, and is used to quantify the impact of rotational motion on vibration energy.

[0067] The natural frequency of a component refers to the resonant frequency of a candidate component under a specific mode. This can be achieved by extracting eigenvalues ​​from the modal analysis results and is used to characterize the frequency characteristics of the component under free vibration. The mode shape value refers to the distribution of displacement or rotation amplitude at each node of the candidate component at the component's natural frequency. This can be achieved by normalizing the modal shape vector and is used to describe the spatial morphological characteristics of the component during vibration.

[0068] After the geometric model of the candidate component is meshed using finite element software, boundary conditions are applied and the first few modal parameters are calculated. For each mode, the translational participation factor and rotational participation factor are calculated. When the deviation between the two reaches its minimum value and is within a preset range, it indicates that the translational and rotational energy distributions in that mode have reached equilibrium. The corresponding modal frequency is then selected as the component's natural frequency. Subsequently, the mode shape values ​​at this frequency are extracted to form modal feature information containing both frequency and morphological characteristics. See also the following examples. Figure 3 After the geometric model of the candidate component is divided into mesh elements using finite element software, the parameters of the first twenty modes of the mesh elements are calculated, including the modal frequency (FREQUENCY), translational participation factor (PARTIC.FACTOR), and rotational participation factor (RATIO) for each mode, with a preset interval configured to be no greater than 0.5%. Figure 3 As shown, in the eighth mode, the ratio (RATIO) between the translational participation factor and the rotational participation factor is approximately 1, meaning the deviation between them is minimal in this mode. Furthermore, the deviation is within a preset range. Therefore, the modal frequency of the eighth mode is selected as the natural frequency of the candidate component. Thus, by simultaneously calculating both the translational and rotational participation factors and selecting the optimal modal order based on their deviation, the energy distribution of the candidate component in actual vibration can be more accurately reflected, avoiding analytical errors caused by neglecting rotational degrees of freedom.

[0069] In some embodiments, feature matching of noise feature information and modal feature information includes: calculating the frequency deviation between the noise dominant frequency in the noise feature information and the component natural frequency in the modal feature information; when the frequency deviation is not greater than a preset frequency deviation threshold, matching the noise dominant frequency and the component natural frequency to obtain a feature matching result.

[0070] In the noise source localization process, the noise signal of the product under test and the geometric model of the candidate components are first acquired. Spectral analysis is performed on the noise signal to extract multiple dominant noise frequencies and their corresponding peak amplitudes, forming noise characteristic information. Modal analysis is then performed on the geometric model of the candidate components to determine the natural frequencies of the components in each mode and their corresponding mode shapes, forming modal characteristic information. Subsequently, the natural frequency of each candidate component is compared with the dominant noise frequency one by one, and the frequency deviation between the two is calculated. When the deviation does not exceed a preset threshold, the candidate component is determined to be a potential noise source, and a feature matching result is generated. For example, if the natural frequency of a candidate component is 1000Hz, the dominant noise frequency is 980Hz, and the preset threshold is 30Hz, then the deviation is 20Hz, satisfying the matching condition. Thus, by quantitatively calculating the frequency deviation, noise characteristics are matched with structural modal characteristics, avoiding interference from environmental noise on the localization results and improving the correlation accuracy between the noise source and the vibrating component.

[0071] In some embodiments, determining the moving parts that generate noise in the product under test based on feature matching results includes: determining the energy distribution characteristic values ​​corresponding to both the noise dominance frequency and the natural frequency of the part based on the feature matching results; and determining the target part based on the energy distribution characteristic values ​​and the noise peak amplitude in the noise characteristic information. The energy distribution characteristic values ​​are positively correlated with the peak amplitude of the dominance frequency and the mode shape value of the natural frequency of the part, respectively.

[0072] Energy distribution characteristic value refers to the energy correlation index generated when the noise dominant frequency matches the natural frequency of a component. Specifically, it can be calculated by weighting the peak noise amplitude at the noise dominant frequency with the mode shape value of the component's natural frequency. For example, the peak noise amplitude and mode shape value can be multiplied or a weighting coefficient can be introduced for linear combination. This characteristic value is used to quantify the energy distribution intensity of a noise source at a specific frequency, thereby helping to determine whether a candidate component is a noise source.

[0073] After matching the dominant noise frequency with the natural frequency of the component, for each successfully matched frequency pair, the noise peak amplitude and mode shape value are correlated and calculated, for example, by multiplying them to obtain an energy distribution characteristic value. The larger this characteristic value, the stronger the correlation between the vibration energy and noise energy of the candidate component at the corresponding frequency. Subsequently, all candidate components are sorted according to their energy distribution characteristic values, and the sorting results are verified by combining the noise peak amplitude. Finally, the candidate component with the highest energy distribution characteristic value and the noise peak amplitude meeting the preset conditions is determined as the target component. Thus, by introducing the energy distribution characteristic value and combining the noise amplitude characteristics with the component vibration mode characteristics for analysis, it is possible to effectively distinguish the component that actually generates noise among multiple candidate components and avoid misjudgment due to similar frequencies.

[0074] In a specific embodiment, the formula for calculating the energy distribution characteristic value is as follows:

[0075] ,

[0076] in, For the first Energy distribution characteristic value of each rate pair, For the first The mode shape value corresponding to the natural frequency of the component in a frequency pair. For the first Noise amplitude weights are paired with a single rate.

[0077] After determining the modal characteristics of candidate components, an energy distribution characteristic value is generated by calculating the product of the mode shape value and the noise amplitude weight at the noise dominance frequency for each candidate component. For example, the larger the mode shape value and the higher the noise amplitude weight of a candidate component, the larger the energy distribution characteristic value, indicating that the component contributes more to the noise. Therefore, by quantifying the energy distribution characteristic values ​​of different candidate components, the weight of each component in the noise generation process can be directly compared, and the candidate component with the largest energy distribution characteristic value can be selected as the target noise source.

[0078] In some embodiments, the noise source localization method further includes: displaying the target component; and generating corresponding optimized design text data based on the product under test and the target component.

[0079] Optimized design text data refers to text automatically generated based on noise source localization results, which includes suggestions for adjusting structural parameters. This can be achieved by using parameterized templates and associating them with a database, providing data support for subsequent structural optimization.

[0080] After noise source localization, the target component's location information is transmitted to the visualization module, where it is highlighted in the assembly model of the product under test using 3D modeling software. Simultaneously, the structural parameter database is accessed, and a text report is generated based on the correlation between the target component's modal characteristics and noise characteristics. This report includes information such as material thickness adjustment ranges and stiffener placement suggestions. For example, when a gear component is identified as a noise source, the matching data between its natural frequency and the dominant noise frequency is written into the report, recommending increasing the rim thickness or adjusting the tooth profile parameters. Thus, through visual annotation and structured data output, the noise source localization results can be directly mapped to the product's 3D model, providing quantitative parameter basis for design improvements and avoiding the inefficient manual comparison of drawings and test data.

[0081] Please see Figure 4 This application also provides a noise source localization device that can implement the above-described noise source localization method. The device includes:

[0082] The first module 401 is used to acquire the noise signal generated by the product under test and the geometric models of several candidate components; the candidate components are the parts in the product under test.

[0083] The second module 402 is used to perform audio feature analysis on the noise signal to obtain the corresponding noise feature information;

[0084] The third module 403 is used to perform modal analysis on the geometric model and obtain the corresponding modal feature information;

[0085] The fourth module 404 is used to perform feature matching on noise feature information and modal feature information, and to determine the target component that generates noise among the candidate components based on the feature matching results.

[0086] The specific implementation of this noise source localization device is basically the same as the specific embodiment of the noise source localization method described above, and will not be repeated here.

[0087] Figure 5 This is a block diagram illustrating an electronic device according to an exemplary embodiment.

[0088] The following reference Figure 5 To describe an electronic device 500 according to such an embodiment of the present disclosure. Figure 5 The electronic device 500 shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments disclosed herein.

[0089] like Figure 5 As shown, the electronic device 500 is presented in the form of a general-purpose computing device. The components of the electronic device 500 may include, but are not limited to: at least one processing unit 510, at least one storage unit 520, a bus 530 connecting different system components (including storage unit 520 and processing unit 510), a display unit 540, etc.

[0090] The storage unit stores program code, which can be executed by the processing unit 510, causing the processing unit 510 to perform the steps described in the above-described noise source localization method section of this specification according to various exemplary embodiments of this disclosure.

[0091] Storage unit 520 may include a readable medium in the form of a volatile storage unit, such as random access memory (RAM) 5201 and / or cache memory 5202, and may further include a read-only memory (ROM) 5203.

[0092] Storage unit 520 may also include a program / utility 5204 having a set (at least one) program module 5205, such program module 5205 including but not limited to: operating system, one or more application programs, other program modules and program data, each or some combination of these examples may include an implementation of a network environment.

[0093] Bus 530 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the various bus structures.

[0094] Electronic device 500 can also communicate with one or more external devices 500' (e.g., keyboard, pointing device, Bluetooth device, etc.), and with one or more devices that enable a user to interact with electronic device 500, and / or with any device that enables electronic device 500 to communicate with one or more other computing devices (e.g., router, modem, etc.). This communication can be performed via input / output (I / O) interface 550. Furthermore, electronic device 500 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 560. Network adapter 560 can communicate with other modules of electronic device 500 via bus 530. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 500, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0095] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described noise source localization method.

[0096] The noise source localization method, apparatus, device, and medium provided in this application combine fusion noise analysis and modal analysis to perform feature matching on the noise characteristic information of the noise signal and the modal characteristic information of each candidate component in the product under test. Based on the feature matching results, the target component that generates noise in each candidate component is determined, effectively filtering out environmental interference components. For multi-source noise problems at the same frequency, it can accurately distinguish the vibration contribution of different candidate components in the product under test, thereby improving the accuracy of noise source localization.

[0097] From the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, or network device, etc.) to execute the methods described above according to the embodiments of this disclosure.

[0098] The program product may employ any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of readable storage media include: electrical connections having one or more wires, portable disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0099] Computer-readable storage media may include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable storage medium may also be any readable medium other than a readable storage medium that can transmit, propagate, or transfer a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the readable storage medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0100] Those skilled in the art will understand that the above modules can be distributed in the device as described in the embodiments, or they can be modified accordingly and placed in one or more devices that are unique to this embodiment. The modules in the above embodiments can be combined into one module, or they can be further divided into multiple sub-modules.

[0101] Exemplary embodiments of this disclosure have been specifically shown and described above. It should be understood that this disclosure is not limited to the detailed structures, arrangements, or implementations described herein; rather, this disclosure is intended to cover various modifications and equivalent arrangements contained within the spirit and scope of the appended claims.

Claims

1. A method for locating a noise source, characterized in that, include: Acquire the noise signal generated by the product under test and the geometric models of several candidate components; the candidate components are parts in the product under test. The noise signal is subjected to audio feature analysis to obtain the corresponding noise feature information; Modal analysis is performed on the geometric model to obtain the corresponding modal feature information; The noise feature information and the modal feature information are matched for feature matching, and the target component that generates noise is determined from each of the candidate components based on the feature matching results; The modal analysis of the geometric model includes: Based on the finite element analysis method, multi-modal analysis is performed on the geometric model to obtain the translational participation factor and rotational participation factor of the candidate component under each mode. When the deviation between the translational participation factor and the rotational participation factor is minimal and within a preset deviation range, the modal frequency of that mode is selected as the natural frequency of the candidate component, and the mode shape value corresponding to the natural frequency of the component is extracted to obtain the modal feature information.

2. The noise source localization method according to claim 1, characterized in that, The audio feature analysis of the noise signal includes: Frequency response analysis is performed on the noise signal to extract multiple noise dominance frequencies and the noise peak amplitudes corresponding to the noise dominance frequencies, thereby obtaining the noise characteristic information.

3. The noise source localization method according to claim 1, characterized in that, The feature matching of the noise feature information and the modal feature information includes: Calculate the frequency deviation between the noise dominant frequency in the noise feature information and the component natural frequency in the modal feature information. When the frequency deviation is not greater than a preset frequency deviation threshold, match the noise dominant frequency and the component natural frequency to obtain the feature matching result.

4. The noise source localization method according to claim 3, characterized in that, The step of determining the noise-generating moving parts in the product under test based on feature matching results includes: Based on the feature matching results, the energy distribution characteristic values ​​corresponding to the noise dominance frequency and the component's natural frequency are determined; the energy distribution characteristic values ​​are positively correlated with the peak amplitude of the dominance frequency and the mode shape value of the component's natural frequency, respectively. The target component is determined based on the energy distribution characteristic value and the noise peak amplitude in the noise characteristic information.

5. The noise source localization method according to claim 4, characterized in that, The formula for calculating the energy distribution characteristic value is: ， in, For the first Energy distribution characteristic value of each rate pair, For the first The mode shape value corresponding to the natural frequency of the component in a frequency pair. For the first Noise amplitude weights are paired with a single rate.

6. The noise source localization method according to claim 1, characterized in that, Also includes: Display the target component; Based on the product under test and the target component, generate corresponding optimized design text data.

7. A noise source localization device, characterized in that, include: The first module is used to acquire noise signals generated by the product under test and geometric models of several candidate components; the candidate components are parts in the product under test. The second module is used to perform audio feature analysis on the noise signal to obtain corresponding noise feature information; The third module is used to perform modal analysis on the geometric model to obtain the corresponding modal feature information; The fourth module is used to perform feature matching on the noise feature information and the modal feature information, and to determine the target component that generates noise among the candidate components based on the feature matching results; The modal analysis of the geometric model includes: Based on the finite element analysis method, multi-modal analysis is performed on the geometric model to obtain the translational participation factor and rotational participation factor of the candidate component under each mode. When the deviation between the translational participation factor and the rotational participation factor is minimal and within a preset deviation range, the modal frequency of that mode is selected as the natural frequency of the candidate component, and the mode shape value corresponding to the natural frequency of the component is extracted to obtain the modal feature information.

8. An electronic device, characterized in that, The electronic device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the noise source localization method according to any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the noise source localization method according to any one of claims 1 to 6.

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