Motor abnormal sound identification method and device, electronic equipment and vehicle

Abstract

The invention relates to a motor abnormal sound identification method and device, electronic equipment and a vehicle, and relates to the technical field of data processing, and the method comprises the steps: firstly obtaining the rotating speed information and sound time domain data of a motor; determining a sampling frequency required for identifying abnormal sound corresponding to the rotating speed information; carrying out downsampling on the sound time domain data according to the sampling frequency; analyzing sound energy corresponding to the rotating speed information based on the downsampled sound time domain data; and identifying whether the motor has abnormal sound or not by comparing the sound energy with a preset energy threshold value. According to the technical scheme, the calculation process of motor abnormal sound recognition can be simplified, the calculation amount is reduced, the recognition cost of the motor abnormal sound is reduced, and the motor abnormal sound of a fixed order is recognized.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a method, device, electronic equipment and vehicle for identifying abnormal noise of a motor. Background Art

[0002] The motor in the vehicle may produce abnormal noises such as howling when it is running, which will greatly increase the irritability of the passengers. Therefore, it is necessary to identify the abnormal noise of the motor in time and perform corresponding maintenance in time.

[0003] Since the abnormal noise of the motor is generally of a high order, a large amount of data needs to be sampled. Currently, analysis and identification can be performed based on the complete sampled data to identify the abnormal noise of the motor. However, this method will involve a large amount of calculation, which will waste computing power and increase the cost of identifying abnormal noise of the motor. Summary of the invention

[0004] In view of this, the present application provides a method, device, electronic device and vehicle for identifying abnormal noise of a motor. The main purpose is to improve the current existing technology for identifying abnormal noises such as howling that may be generated when the motor in the vehicle is running. There is a technical problem that there is a large amount of calculation, which will result in a waste of computing power and increase the cost of identifying abnormal noise of the motor.

[0005] In a first aspect, the present application provides a method for identifying abnormal noise of a motor, comprising:

[0006] Obtain the motor's speed information and sound time domain data;

[0007] Determine the sampling frequency required to identify abnormal noise corresponding to the speed information;

[0008] Downsample the sound time domain data according to the sampling frequency;

[0009] Based on the downsampled sound time domain data, the sound energy corresponding to the speed information is analyzed;

[0010] By comparing the sound energy with the preset energy threshold, it is possible to identify whether the motor has abnormal noise.

[0011] In a second aspect, the present application provides a device for identifying abnormal noise of a motor, comprising:

[0012] An acquisition module is configured to acquire the speed information and sound time domain data of the motor;

[0013] A determination module is configured to determine a sampling frequency required to identify an abnormal sound corresponding to the rotation speed information;

[0014] A sampling module is configured to downsample the sound time domain data according to the sampling frequency;

[0015] An analysis module configured to analyze sound energy corresponding to the rotation speed information based on the downsampled sound time domain data;

[0016] The identification module is configured to identify whether the motor has abnormal noise by comparing the sound energy with a preset energy threshold.

[0017] In a third aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the method for controlling the vehicle function described in the first aspect is implemented.

[0018] In a fourth aspect, the present application provides an electronic device comprising a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, wherein the processor implements the vehicle function control method described in the first aspect when executing the computer program.

[0019] In a fifth aspect, the present application provides a vehicle, comprising the device as described in the second aspect, or the electronic device as described in the fourth aspect.

[0020] By means of the above technical scheme, the present application provides a method, device, electronic device and vehicle for identifying abnormal noise of a motor, firstly obtaining the speed information and sound time domain data of the motor; determining the sampling frequency required for identifying abnormal noise corresponding to the speed information; then downsampling the sound time domain data according to the sampling frequency; then analyzing the sound energy corresponding to the speed information based on the downsampled sound time domain data; and identifying whether the motor has abnormal noise by comparing the sound energy with the preset energy threshold. Compared with the current method of analyzing and identifying whether the motor has abnormal noise based on complete sampling data, by applying the technical scheme of the present application, the sampling frequency required for identifying abnormal noise corresponding to the speed is determined according to the current speed of the motor, and then downsampling the sound time domain data according to the sampling frequency, without analyzing and calculating the complete sampling data, the sound energy corresponding to the current speed can be analyzed based on the downsampled sound time domain data, and combined with the preset energy threshold, identifying whether the motor has abnormal noise. While ensuring accurate identification of whether the motor has abnormal noise, this embodiment can simplify the calculation process of identifying abnormal noise of the motor, reduce the amount of calculation, and reduce the recognition cost of abnormal noise of the motor.

[0021] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0023] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0024] Figure 1 A schematic diagram of a flow chart of a method for identifying abnormal noise of a motor provided in an embodiment of the present application is shown;

[0025] Figure 2 A schematic flow chart of another method for identifying abnormal noise of a motor provided in an embodiment of the present application is shown;

[0026] Figure 3 A schematic diagram showing an example provided by an embodiment of the present application;

[0027] Figure 4 A schematic diagram showing an example provided by an embodiment of the present application;

[0028] Figure 5 A schematic diagram showing an example provided by an embodiment of the present application;

[0029] Figure 6 A schematic diagram showing an example provided by an embodiment of the present application;

[0030] Figure 7 A schematic diagram showing an example display effect provided by an embodiment of the present application is shown;

[0031] Figure 8 A schematic diagram of the structure of a device for identifying abnormal noise of a motor provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0032] In order to more clearly understand the above-mentioned purposes, features and advantages of the present application, the scheme of the present application will be further described below. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0033] In order to improve the current existing technology for identifying abnormal noises such as whistling that may be generated when the motor in the vehicle is running, there is a large amount of calculation, which will cause a waste of computing power and increase the technical problem of the cost of identifying abnormal noises of the motor. This embodiment provides a method for identifying abnormal noises of the motor, such as Figure 1 As shown, the method includes:

[0034] Step 101: Obtain the speed information and sound time domain data of the motor.

[0035] The motor refers to an electromagnetic device that realizes the conversion or transmission of electric energy according to the law of electromagnetic induction, and is divided into an electric motor and a generator. In this embodiment, it can be a motor device in a vehicle.

[0036] The motor speed information may include the motor speed at the current moment, or the speed at different time points in the current time period, or the speed at each time point in a continuous time, etc. The corresponding sound time domain data may include the sound time domain data at the current moment, or the sound time domain data in the current time period, or the sound time domain data in a continuous time, etc.

[0037] The sounds emitted by the motor at different speeds are different, and the corresponding characteristics of abnormal noises are also different. When identifying whether the motor has abnormal noises, this embodiment needs to obtain the motor speed information and sound time domain data at a specific time, and then perform targeted analysis to accurately analyze whether the motor has abnormal noises at this speed.

[0038] Step 102: Determine the sampling frequency required for identifying abnormal noise corresponding to the rotation speed information.

[0039] Since the sounds emitted by the motor at different speeds are different, the corresponding characteristics of the abnormal noise are also different, and therefore the sampling frequencies required to identify the abnormal noise are also different. This embodiment can collect the motor speed information, accurately analyze and determine the sampling frequency required to identify the abnormal noise, and then perform targeted downsampling processing on the sampling frequency, that is, execute the process shown in step 103.

[0040] Step 103: downsample the sound time domain data according to the sampling frequency.

[0041] The purpose of downsampling the sound time domain data in this embodiment is to save computing power. Compared with the existing method of analyzing and identifying whether the motor has abnormal noise based on complete sampling data, this embodiment can downsample the sound time domain data, and then perform analysis and calculation based on the downsampled sound time domain data to further identify whether the motor has abnormal noise. While ensuring accurate identification of whether the motor has abnormal noise, this embodiment can simplify the calculation process of motor abnormal noise identification, reduce the amount of calculation, and reduce the cost of identifying motor abnormal noise.

[0042] Step 104: Analyze the sound energy corresponding to the rotation speed information based on the downsampled sound time domain data.

[0043] Sound energy may represent the sound energy obtained by the medium due to sound vibration when the sound wave propagates in the medium. In this embodiment, the sound energy is the sound spectrum energy that can be felt by the human ear, which can be obtained by converting the spectrum energy.

[0044] For example, this embodiment can analyze whether the motor at the current speed has abnormal noise, calculate the sound energy of the current speed based on the sound time domain data after downsampling in steps 101 to 103, and then determine whether there is abnormal noise based on the sound energy analysis.

[0045] The spectrum refers to the representation of a time domain signal in the frequency domain, which can be obtained by Fourier transforming the signal. The result will be two graphs with amplitude and phase as the vertical axis and frequency as the horizontal axis. However, sometimes the phase information is omitted, and only the corresponding amplitude data at different frequencies are available. Sometimes the "amplitude spectrum" is used to represent the change of amplitude with frequency, and the "phase spectrum" is used to represent the change of phase with frequency. In simple terms, the spectrum can indicate which frequencies of sine waves a signal is composed of, and can also show information such as the size and phase of the sine waves of each frequency.

[0046] Sound is a mechanical vibration that can pass through matter in all states of matter. Sound cannot propagate in a vacuum. The sound that people hear is also a sound wave with a certain frequency. The frequency range of human hearing is about 20 to 20,000 Hz. Anything beyond this range is not perceptible to our ears. Below 20 Hz is infrasound, and above 20 kHz is ultrasound. The higher the frequency of the sound, the higher the pitch of the sound, and the lower the frequency of the sound, the lower the pitch of the sound.

[0047] Step 105 , by comparing the sound energy with a preset energy threshold, identifying whether the motor has abnormal noise.

[0048] The preset energy threshold may be an evaluation threshold set for sound energy, and is used to identify whether the motor has abnormal noise.

[0049] Compared with the current method of analyzing and identifying whether the motor has abnormal noise based on complete sampling data, by applying the technical solution of this embodiment, the sampling frequency required for identifying abnormal noise corresponding to the speed is determined according to the current speed of the motor, and then the sound time domain data is downsampled according to the sampling frequency. There is no need to analyze and calculate the complete sampling data. Subsequently, the sound energy corresponding to the current speed can be analyzed based on the downsampled sound time domain data, and combined with the preset energy threshold, it can be identified whether the motor has abnormal noise. This embodiment can simplify the calculation process of motor abnormal noise identification while ensuring accurate identification of whether the motor has abnormal noise, reduce the amount of calculation, and reduce the cost of identifying motor abnormal noise.

[0050] Further, as a refinement and extension of the above embodiment, in order to fully illustrate the specific implementation process of the method of this embodiment, this embodiment provides the following Figure 2 The specific method shown includes:

[0051] Step 201: Obtain the speed information and sound time domain data of the motor.

[0052] Optionally, obtain the motor speed and sound time domain data corresponding to the current time domain window.

[0053] Exemplarily, taking the current time domain window of 0-1s as an example, the motor speed corresponding to the current 0-1s is obtained to be 10r / s, and the sound time domain data obtained for the current 0-1s includes 4096 items.

[0054] Step 202: Obtain the first-order frequency of the motor corresponding to the speed information.

[0055] The resonance frequency means that when the piston moves up and down once, the motor will vibrate twice, so the vibration frequency of the motor is related to the motor speed. The vibration frequency that is the same as the motor speed is called first-order vibration, and the frequency that is twice the motor speed is called second-order vibration. By analogy, there are also third-order and fourth-order vibrations.

[0056] In the embodiment of the present application, the first-order frequency of the motor is determined by the motor speed corresponding to the current time domain window, which can be specifically shown in the following formula 1:

[0057]

[0058] Where, Spd is the motor speed in RPM. fmot is the first-order frequency of the motor.

[0059] Exemplarily, based on step 201, when the motor speed corresponding to the current 0-1s is 10, the corresponding first-order frequency of the motor is 1 / 6. Similarly, if the current motor speed is 1200, the corresponding first-order frequency of the motor is 20, and so on. Examples are not given one by one here.

[0060] Step 203: Analyze the frequency band corresponding to the first-order frequency of the motor based on the empirical value of the order of the abnormal sound.

[0061] Optionally, step 203 may specifically include: multiplying the first-order frequency of the motor by an empirical value of the order of occurrence of the abnormal sound; and increasing the positive and negative range values ​​based on the obtained product value to obtain a frequency band.

[0062] In the embodiment of the present application, the empirical value of the order of abnormal noise is the order in which abnormal noise often occurs for the current motor. The frequency band corresponding to the first-order frequency of the motor can be determined by calculating the product of the first-order frequency of the motor and the order in which abnormal noise often occurs. Since the frequency band in which the motor has abnormal noise cannot be accurately determined based on the empirical value, it is necessary to further expand the frequency band determined based on the empirical value of the motor's frequent abnormal noise, that is, to increase the positive and negative range values ​​based on the obtained product value to obtain the frequency band. For example, the increased positive and negative range can be selected as positive and negative 7HZ.

[0063] Exemplarily, based on step 202, if the first-order frequency of the current motor is 20, and the order at which abnormal noise often occurs is the 2nd order, then the frequency band corresponding to the abnormal noise is determined to be 40, and based on the obtained frequency band value, a positive and negative range is added, and the frequency band at which the abnormal noise occurs can be selected as 33 to 47.

[0064] Step 204: Determine the Nyquist frequency corresponding to the maximum frequency in the frequency band as the sampling frequency.

[0065] Frequency band refers to the part of the radio spectrum between two specific frequency limits. The unit of frequency band is Hertz (Hz). In layman's terms, for a channel, the frequency band is the frequency range between the highest frequency of the signal allowed to be transmitted and the lowest frequency of the signal allowed to be transmitted (of course, considering that attenuation must be within a certain range). If the difference between the two is large, the frequency band can be considered to be equal to the highest frequency of the signal allowed to be transmitted.

[0066] For a signal, the frequency band is the frequency range between the highest frequency and the lowest frequency contained in the signal (of course, the frequency component must be greater than a certain value). If the difference between the two is very large, we can roughly think that the frequency band is equal to the highest frequency of the signal. If a signal contains only one frequency AC component or a limited number of frequencies of AC components, we call this signal a band signal.

[0067] Nyquist frequency is the minimum sampling frequency that needs to be defined to prevent signal aliasing. Aliasing refers to the phenomenon that when the discrete Fourier transform is used to sample the signal domain in the frequency domain, the number of sampling points is less than the length of the time domain sequence, causing the time domain period extension sequence to overlap with each other.

[0068] If the sampling frequency is too low to avoid overlap, the sampling result will be different from the original sample. If the spectrum of a sample is a band-limited spectrum, that is, a spectrum that is 0 outside a certain frequency, then the sampling frequency must be greater than twice the current frequency to avoid overlap and thus distortion. The sampling frequency must be greater than twice the current frequency, which is the Nyquist criterion.

[0069] In the embodiment of the present application, each frequency band value has a corresponding motor frequency, and the maximum motor frequency within the frequency band is determined. The corresponding Nyquist frequency, that is, the sampling frequency, is obtained based on the obtained maximum motor frequency. The method for obtaining the Nyquist frequency is shown in the following formula 2:

[0070] f NQST =2f s (Formula 2)

[0071] In formula 2, f NQST is the Nyquist frequency and fs is the maximum motor frequency.

[0072] Exemplarily, based on step 203 , when the frequency band in which the motor makes abnormal noise is selected to be 33 to 47 and the maximum frequency of the motor is 128, it can be determined that the Nyquist frequency of the motor is 256, that is, the sampling frequency of the motor is 256.

[0073] Step 205: downsample the sound time domain data according to the sampling frequency.

[0074] In an embodiment of the present application, the sound time domain data is downsampled according to the sampling frequency. Specifically, the sound time domain data is downsampled to a number corresponding to the sampling frequency.

[0075] Exemplarily, based on step 204, the current 0-1s sound time domain data includes 4096, and the sampling frequency of the motor is 256, that is, 4096 time domain data are downsampled to 256 time domain data.

[0076] Step 206: Analyze the sound energy corresponding to the rotation speed information based on the downsampled sound time domain data.

[0077] Optionally, step 206 may specifically include: performing butterfly parity transposition of fast Fourier transform on the downsampled sound time domain data to obtain a transposition sequence; based on the transposition sequence and the Nyquist frequency corresponding to each frequency in the frequency band, analyzing the spectral energy corresponding to each frequency; and using the maximum energy obtained from the spectral energy corresponding to each frequency as the sound energy corresponding to the rotational speed information.

[0078] Optionally, step 206 specifically also includes: obtaining the number of time domain samples K required for the target frequency based on the Nyquist frequency corresponding to the target frequency; selecting the target sequence corresponding to the first K sampling points in the displacement sequence to perform butterfly recursive calculation of the fast Fourier transform, and then multiplying the result by the A weighting coefficient to obtain the spectrum energy corresponding to the target frequency.

[0079] Optionally, step 206 specifically further includes: obtaining a minimum integer power of 2 that is greater than the Nyquist frequency corresponding to the target frequency as the number K of time domain samples.

[0080] Fast Fourier transform is a fast algorithm for discrete Fourier transform. It is obtained by improving the algorithm of discrete Fourier transform based on the odd, even, imaginary and real characteristics of discrete Fourier transform.

[0081] The butterfly parity shift of the fast Fourier transform refers to the use of point-valued polynomial representation and the use of the unit root property to perform recursive calculations on the parity decomposition of the polynomial to accelerate the algorithm. The premise of the recursion is to rearrange the original sequence according to the butterfly parity distribution.

[0082] In the embodiment of the present application, the selected bit shift sequence includes 16 data 0-15, the number of time domain samples R required to obtain the target frequency is 4, and the minimum number of samples in the sample set required for downsampling at the current target frequency is 8 as an example, Figure 3 and Figure 4 As shown, an odd-even butterfly permutation is performed, and the first K numbers after the permutation are the permutation sequence of downsampling the original sequence of N numbers to a sequence of K numbers. Specifically, if the permutation sequence [0 1 2 3 4 5 6 7 8 9 101112 13 14 15] is downsampled to 8, the new permutation sequence is [0 2 4 6 8 10 12 14].

[0083] For example, in the permutation sequence [0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15], the target sequence corresponding to the first four sampling points is [0 4 8 12][2 6 10 14][1 5 9 13][3 7 11 15]. Other cases can be deduced by analogy and no specific examples are given here.

[0084] The new sequence is permuted, that is, after the original sequence is permuted, the first K numbers are taken for butterfly recursive calculation of fast Fourier transform to obtain a sequence with the same minimum number of samples in the sample set, and the spectral energy corresponding to the sequence after downsampling can be obtained. Since the rotation speed changes in the time window, the maximum energy in the window is taken as the window energy, which can tighten the abnormal sound recognition.

[0085] Optionally, the spectrum energy after downsampling needs to be converted into spectrum energy perceived by the human ear, as shown in the following formula 3:

[0086] M A =M·coe A (Formula 3)

[0087] Among them, coeA is the A weighting coefficient, which represents the human ear's perception of energy in different frequency bands.

[0088] In the embodiment of the present application, the smallest integer power of 2 greater than the Nyquist frequency corresponding to the target frequency is obtained as the number of time domain samples K, as shown in the following formula 4:

[0089] K=min(2 N >f NQST ) (Formula 4)

[0090] Wherein K is the smallest integer power of 2 that is greater than the Nyquist frequency.

[0091] Exemplarily, based on step 205, the sampling frequency of the motor is 256, that is, when the Nyquist frequency of the motor is 256, K is 8; if the sampling frequency of the motor is 200, K is 8, and so on, which are not given one by one here.

[0092] Step 207: Identify whether the motor has abnormal sound by comparing the sound energy with a preset energy threshold.

[0093] Optionally, step 207 may specifically include: comparing the sound energy corresponding to different speeds with a preset energy threshold, and configuring label information corresponding to different speeds according to the magnitude exceeding the preset energy threshold; performing probability density calculation based on the label information corresponding to different speeds; if the calculated probability density is greater than the preset density threshold, it is determined that the motor has an abnormal sound.

[0094] The formula for probability density is probability density = probability / group interval. Probability refers to the chance of an event occurring randomly. For a uniform distribution function, the probability density is equal to the probability of a certain interval (the range of values ​​of the event) divided by the length of the interval.

[0095] The integral of the probability density over the interval is the area, and this area is the probability of the event occurring in this interval, and the sum of all areas is one. Therefore, it is meaningless to analyze the probability density of a point alone, and it must have an interval as a reference and comparison. In the embodiment of the present application, the interval selected by the probability density can be several sampling points before and after the current speed sampling point, which is not specifically limited here.

[0096] Exemplarily, based on step 206, the motor performs 0-100 acceleration, determines the sound energy corresponding to different speeds, and labels the energy corresponding to the current speed according to the magnitude exceeding the threshold. The method can be selected as continuous or discrete. In the embodiment of the present application, the labeling method selects the discrete method according to the power of energy.

[0097] For example, the tag information can be divided into three levels: 1 for exceeding 0-3dB, 2 for 3-6dB, 3 for 3-9dB, and 0 for not exceeding the threshold. When the density exceeds 2.5, it is considered that an abnormal sound occurs, and the corresponding result is: Figure 5 and Figure 6 They are comparisons made by the full data waterfall chart. Figure 5 For the test without howling, Figure 6 For the howling test, it can be seen that Figure 6 The test has obvious order energy accumulation. Figure 6 The test has a high order energy density, so abnormal noise can be identified. The corresponding relationship between motor speed and spectrum data can be shown as follows: Figure 7 shown.

[0098] Compared with the current existing technology, this embodiment simplifies the calculation process of identifying abnormal motor noise, reduces the amount of calculation, reduces the cost of identifying abnormal motor noise, and identifies abnormal motor noise of a fixed order, targeting abnormal noises such as howling that may be generated when the motor in the vehicle is running.

[0099] Further, as Figure 1 to Figure 2 The specific implementation of the method shown in the figure, this embodiment provides a device for identifying abnormal noise of a motor, such as Figure 8 As shown, the device includes: an acquisition module 31, a determination module 32, a sampling module 33, an analysis module 34, and an identification module 35.

[0100] An acquisition module 31 is configured to acquire the speed information and sound time domain data of the motor;

[0101] A determination module 32 is configured to determine a sampling frequency required for identifying an abnormal sound corresponding to the rotation speed information;

[0102] The sampling module 33 is configured to downsample the sound time domain data according to the sampling frequency;

[0103] An analysis module 34 is configured to analyze the sound energy corresponding to the rotation speed information based on the downsampled sound time domain data;

[0104] The identification module 35 is configured to identify whether the motor has abnormal sound by comparing the sound energy with a preset energy threshold.

[0105] In a specific application scenario, the determination module 32 is specifically configured to obtain the first-order frequency of the motor corresponding to the speed information; analyze the frequency band corresponding to the first-order frequency of the motor based on the empirical value of the order of abnormal sound; and determine the Nyquist frequency corresponding to the maximum frequency in the frequency band as the sampling frequency.

[0106] In a specific application scenario, the determination module 32 is further configured to multiply the first-order frequency of the motor by an empirical value of the order of occurrence of the abnormal sound; and increase the positive and negative range values ​​based on the obtained product value to obtain the frequency band.

[0107] In a specific application scenario, the analysis module 34 is specifically configured to perform butterfly parity transposition of fast Fourier transform on the downsampled sound time domain data to obtain a transposition sequence; based on the transposition sequence and the Nyquist frequency corresponding to each frequency in the frequency band, analyze the spectral energy corresponding to each frequency; and use the maximum energy obtained from the spectral energy corresponding to each frequency as the sound energy corresponding to the rotational speed information.

[0108] In a specific application scenario, the analysis module 34 is further configured to obtain the number of time domain samples K required for the target frequency based on the Nyquist frequency corresponding to the target frequency; select the target sequence corresponding to the first K sampling points in the displacement sequence to perform butterfly recursive calculation of the fast Fourier transform, and then multiply the result by the A weighting coefficient to obtain the spectrum energy corresponding to the target frequency.

[0109] In a specific application scenario, the analysis module 34 is further configured to obtain a minimum integer power of 2 greater than the Nyquist frequency corresponding to the target frequency as the number K of time domain samples.

[0110] In a specific application scenario, the identification module 35 is specifically configured to compare the sound energy corresponding to different speeds with the preset energy threshold, and configure the label information corresponding to different speeds according to the size exceeding the preset energy threshold; perform probability density calculation based on the label information corresponding to different speeds; if the calculated probability density is greater than the preset density threshold, it is determined that the motor has an abnormal sound.

[0111] It should be noted that for other corresponding descriptions of the functional units involved in the motor abnormal noise identification device provided in this embodiment, reference can be made to Figure 1 to Figure 2 The corresponding description in will not be repeated here.

[0112] Based on the above Figure 1 to Figure 2 The method shown in the embodiment also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the above-mentioned Figure 1 to Figure 2 The method shown.

[0113] Based on this understanding, the technical solution of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, USB flash drive, mobile hard disk, etc.), including a number of instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods of various implementation scenarios of the present application.

[0114] Based on the above Figure 1 to Figure 2 The method shown, and Figure 8 In order to achieve the above-mentioned purpose, the embodiment of the present application further provides an electronic device, which can be configured on a vehicle (such as a new energy vehicle) or a server, etc. The device includes a storage medium and a processor; the storage medium is used to store a computer program; the processor is used to execute the computer program to achieve the above-mentioned Figure 1 to Figure 2 The method shown.

[0115] Optionally, the above-mentioned physical device may also include a user interface, a network interface, a camera, a radio frequency (RF) circuit, a sensor, an audio circuit, a WI-FI module, etc. The user interface may include a display, an input unit such as a keyboard, etc., and the optional user interface may also include a USB interface, a card reader interface, etc. The network interface may optionally include a standard wired interface, a wireless interface (such as a WI-FI interface), etc.

[0116] Those skilled in the art will appreciate that the above-mentioned physical device structure provided in this embodiment does not constitute a limitation on the physical device, and may include more or fewer components, or a combination of certain components, or different arrangements of components.

[0117] The storage medium may also include an operating system and a network communication module. The operating system is a program that manages the hardware and software resources of the above-mentioned physical device, and supports the operation of the information processing program and other software and / or programs. The network communication module is used to realize the communication between the components inside the storage medium, and the communication with other hardware and software in the information processing physical device.

[0118] Furthermore, based on the above Figure 1 to Figure 2 The method shown, and Figure 8 The virtual device embodiment shown in the figure, as well as the electronic device embodiment described above, this embodiment also provides a vehicle, which may include the electronic device described above, and may further perform the following Figure 1 to Figure 2 The method shown.

[0119] Through the description of the above implementation methods, those skilled in the art can clearly understand that the present application can be implemented by means of software plus a necessary general hardware platform, or by hardware. By applying the solution of this embodiment, compared with the current prior art, this embodiment can simplify the calculation process of motor abnormal noise identification, reduce the amount of calculation, reduce the cost of motor abnormal noise identification, and identify fixed-order motor abnormal noise, targeting abnormal noises such as whistling that may be generated when the motor in the vehicle is running.

[0120] It should be noted that, in this article, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the term "comprises" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0121] The above is only a specific implementation of the present application, so that those skilled in the art can understand or implement the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments described herein, but will conform to the widest scope consistent with the principles and novel features applied for herein.

Claims

1. A method for identifying abnormal noise of a motor, characterized in that: include: Obtain the motor's speed information and sound time domain data; Determine a sampling frequency required to identify abnormal noise corresponding to the speed information; Downsampling the sound time domain data according to the sampling frequency; Analyzing the sound energy corresponding to the rotation speed information based on the downsampled sound time domain data; By comparing the sound energy with a preset energy threshold, it is identified whether the motor has abnormal noise.

2. The method according to claim 1, characterized in that The determining of the sampling frequency required for identifying the abnormal sound corresponding to the rotation speed information includes: Acquire the first-order frequency of the motor corresponding to the speed information; Analyze the frequency band corresponding to the first-order frequency of the motor based on the empirical value of the order of abnormal noise; A Nyquist frequency corresponding to a maximum frequency in the frequency band is determined as the sampling frequency.

3. The method according to claim 2, characterized in that The analyzing the frequency band corresponding to the first-order frequency of the motor according to the empirical value of the abnormal sound occurrence order includes: Multiply the first-order frequency of the motor by the empirical value of the order of abnormal noise; The frequency band is obtained by increasing the positive and negative range values ​​based on the obtained product value.

4. The method according to claim 2, characterized in that: The analyzing the sound energy corresponding to the rotation speed information based on the downsampled sound time domain data includes: Perform butterfly parity shift of fast Fourier transform on the downsampled sound time domain data to obtain a shift sequence; Analyzing the spectrum energy corresponding to each frequency based on the permutation sequence and the Nyquist frequency corresponding to each frequency in the frequency band; The maximum energy obtained from the spectrum energy corresponding to each frequency is used as the sound energy corresponding to the rotation speed information.

5. The method according to claim 4, characterized in that The analyzing the spectrum energy corresponding to each frequency based on the permutation sequence and the Nyquist frequency corresponding to each frequency in the frequency band includes: According to the Nyquist frequency corresponding to the target frequency, the number of time domain samples K required for the target frequency is obtained; The target sequence corresponding to the first K sampling points is selected in the permutation sequence to perform butterfly recursive calculation of fast Fourier transform, and the result is multiplied by the A weighting coefficient to obtain the spectrum energy corresponding to the target frequency.

6. The method according to claim 5, characterized in that The step of obtaining the number of time domain samples K required for the target frequency according to the Nyquist frequency corresponding to the target frequency includes: A minimum integer power of 2 that is greater than the Nyquist frequency corresponding to the target frequency is obtained as the number K of time-domain samples.

7. The method according to any one of claims 1 to 6, characterized in that The step of comparing the sound energy with a preset energy threshold to identify whether the motor has an abnormal sound comprises: Compare the sound energy corresponding to different rotation speeds with the preset energy threshold, and configure label information corresponding to different rotation speeds according to the magnitude exceeding the preset threshold; Probability density calculation is performed based on the label information corresponding to different speeds; If the calculated probability density is greater than a preset density threshold, it is determined that the motor has an abnormal noise.

8. A device for identifying abnormal noise of a motor, characterized in that: include: An acquisition module is configured to acquire the speed information and sound time domain data of the motor; A determination module configured to determine a sampling frequency required for identifying an abnormal sound corresponding to the rotation speed information; A sampling module, configured to downsample the sound time domain data according to the sampling frequency; an analysis module configured to analyze the sound energy corresponding to the rotation speed information based on the downsampled sound time domain data; The identification module is configured to identify whether the motor has abnormal sound by comparing the sound energy with a preset energy threshold.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

10. An electronic device comprising a storage medium, a processor, and a computer program stored in the storage medium and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 7 is implemented.

11. A vehicle, characterized in that: include: The device as claimed in claim 8, or the electronic device as claimed in claim 10.

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