Method and apparatus for identifying abnormal sound of electric motor, and computer-readable storage medium, electronic device and vehicle

Abstract

The present application relates to the technical field of data processing, and relates to a method and apparatus for identifying an abnormal sound of an electric motor, and a computer-readable storage medium, an electronic device and a vehicle. The method comprises: firstly, acquiring rotational speed information and sound time-domain data of an electric motor; determining a sampling frequency that corresponds to the rotational speed information and is required for identifying an abnormal sound; then performing down-sampling on the sound time-domain data according to the sampling frequency; on the basis of the sound time-domain data which has been subjected to down-sampling, analyzing sound energy corresponding to the rotational speed information; and by means of comparing the sound energy with a preset energy threshold value, identifying whether an abnormal sound has occurred in the electric motor. By means of using the technical solution in the present application, the calculation process for identifying an abnormal sound of an electric motor can be simplified, thereby reducing the amount of calculation and the cost for identifying the abnormal sound of the electric motor, and thus identifying an abnormal sound at a fixed order of the electric motor.

Description

Motor abnormal noise identification method, device, computer-readable storage medium, electronic device and vehicle

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to the Chinese patent application filed with the China Patent Office on June 20, 2023, with application number 202310731674.8 and application name “Method, device, electronic equipment and vehicle for identifying abnormal noise of motors”, the entire contents of which are incorporated by reference into this application. Technical Field

[0003] The present application relates to the field of data processing technology, and is related to, but not limited to, a method and device for identifying abnormal noise of a motor, a computer-readable storage medium, an electronic device, and a vehicle. Background Art

[0004] When the motor in the vehicle is running, it may produce abnormal noises such as howling, which will greatly increase the irritability of passengers. Therefore, it is necessary to identify abnormal motor noises in a timely manner and perform corresponding maintenance in a timely manner.

[0005] Because motor noise 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 motor noise. However, this method requires a large amount of computation, resulting in a waste of computing power and increasing the cost of identifying motor noise.

[0006] Summary of the Invention

[0007] In view of this, the present application provides a method, device, computer-readable storage medium, electronic device and vehicle for identifying abnormal motor noise, which can simplify the calculation process of identifying abnormal motor noise while ensuring accurate identification of whether the motor has abnormal noise, reduce the amount of calculation, and reduce the cost of identifying abnormal motor noise.

[0008] In a first aspect, the present application provides a method for identifying abnormal noise of a motor, comprising:

[0009] Obtain motor speed information and sound time domain data;

[0010] Determine the sampling frequency required to identify abnormal noise corresponding to the speed information;

[0011] Downsample the sound time domain data according to the sampling frequency;

[0012] Analyze the sound energy corresponding to the speed information based on the downsampled sound time domain data;

[0013] By comparing the sound energy with the preset energy threshold, it can identify whether the motor has abnormal noise.

[0014] In an optional embodiment of the present application, determining a sampling frequency required for identifying abnormal noise corresponding to the speed information includes:

[0015] Obtaining the first-order frequency of the motor corresponding to the speed information;

[0016] Analyze the frequency band corresponding to the first-order frequency of the motor based on the empirical value of the order of abnormal noise;

[0017] A Nyquist frequency corresponding to a maximum frequency in the frequency band is determined as the sampling frequency.

[0018] In an optional embodiment of the present application, analyzing the frequency band corresponding to the first-order frequency of the motor based on the empirical value of the order of occurrence of the abnormal noise includes:

[0019] Multiply the first-order frequency of the motor by the empirical value of the order of abnormal noise;

[0020] The frequency band is obtained by increasing the positive and negative range values ​​based on the obtained product value.

[0021] In an optional embodiment of the present application, analyzing the sound energy corresponding to the rotational speed information based on the downsampled sound time domain data includes:

[0022] Perform butterfly parity shift of fast Fourier transform on the downsampled sound time domain data to obtain a shift sequence;

[0023] 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;

[0024] The maximum energy obtained from the spectrum energy corresponding to each frequency is used as the sound energy corresponding to the rotation speed information.

[0025] In an optional embodiment of the present application, 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:

[0026] Obtaining the number K of time domain samples required for the target frequency based on the Nyquist frequency corresponding to the target frequency;

[0027] The target sequence corresponding to the first K sampling points in the permutation sequence is selected 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.

[0028] In an optional embodiment of the present application, obtaining the number K of time domain samples required for the target frequency based on the Nyquist frequency corresponding to the target frequency includes:

[0029] A minimum integer power of 2 greater than the Nyquist frequency corresponding to the target frequency is obtained as the number K of time-domain samples.

[0030] In an optional embodiment of the present application, the step of identifying whether the motor makes an abnormal sound by comparing the sound energy with a preset energy threshold includes:

[0031] Comparing the sound energy corresponding to different rotation speeds with the preset energy threshold, and configuring label information corresponding to different rotation speeds according to the extent of exceeding the preset threshold;

[0032] Probability density calculation is performed based on the label information corresponding to different speeds;

[0033] If the calculated probability density is greater than a preset density threshold, it is determined that the motor has an abnormal noise.

[0034] In a second aspect, the present application provides a device for identifying abnormal motor noise, comprising:

[0035] An acquisition part is configured to acquire the speed information and sound time domain data of the motor;

[0036] a determination portion configured to determine a sampling frequency required for identifying abnormal noise corresponding to the speed information;

[0037] The sampling part is configured to downsample the sound time domain data according to the sampling frequency;

[0038] The analysis part is configured to analyze the sound energy corresponding to the speed information based on the downsampled sound time domain data;

[0039] The identification part is configured to identify whether the motor has abnormal noise by comparing the sound energy with a preset energy threshold.

[0040] In an optional embodiment of the present application, the acquiring part is specifically configured to acquire the first-order frequency of the motor corresponding to the speed information;

[0041] The analysis part is specifically configured to analyze the frequency band corresponding to the first-order frequency of the motor based on the empirical value of the order of the abnormal noise;

[0042] The determining part is specifically configured to determine a Nyquist frequency corresponding to a maximum frequency in the frequency band as the sampling frequency.

[0043] In an optional embodiment of the present application, the determining part is further configured to multiply the first-order frequency of the motor by an empirical value of the order of occurrence of the abnormal noise; and increase the positive and negative range values ​​based on the obtained product value to obtain the frequency band.

[0044] In an optional embodiment of the present application, the analysis part 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, the spectral energy corresponding to each frequency is analyzed; and the maximum energy obtained from the spectral energy corresponding to each frequency is used as the sound energy corresponding to the speed information.

[0045] In an optional embodiment of the present application, the analysis part is further configured to obtain the number K of time domain samples 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 spectral energy corresponding to the target frequency.

[0046] In an optional embodiment of the present application, the analysis part 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.

[0047] In an optional embodiment of the present application, the identification part is specifically configured to compare the sound energy corresponding to different speeds with the preset energy threshold, and configure label information corresponding to different speeds according to the extent of exceeding the preset energy threshold; and perform probability density calculation based on the label information corresponding to different speeds;

[0048] The determination part is specifically configured to determine that the motor has an abnormal noise if the calculated probability density is greater than a preset density threshold.

[0049] In a third aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for identifying abnormal motor noise as described in the first aspect.

[0050] 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 method for identifying abnormal motor noise as described in the first aspect when executing the computer program.

[0051] 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.

[0052] By means of the above technical solution, the present application provides a method, apparatus, computer-readable storage medium, electronic device, and vehicle for identifying abnormal motor noise. The method first obtains motor speed information and sound time-domain data; determines a sampling frequency required for identifying abnormal noise corresponding to the speed information; then downsamples the sound time-domain data based on the sampling frequency; then analyzes the sound energy corresponding to the speed information based on the downsampled sound time-domain data; and identifies whether the motor has abnormal noise by comparing the sound energy with a preset energy threshold. Compared to the current method of analyzing and identifying whether a motor has abnormal noise based on complete sampled data, by applying the technical solution of the present application, the sampling frequency required for identifying abnormal noise corresponding to the current motor speed is determined, and then the sound time-domain data is downsampled based on the sampling frequency, eliminating the need to analyze and calculate the complete sampled 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 to identify whether the motor has abnormal noise. This embodiment can simplify the calculation process for identifying abnormal motor noise while ensuring accurate identification of whether the motor has abnormal noise, reducing the amount of calculation and the cost of identifying abnormal motor noise.

[0053] 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

[0054] 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.

[0055] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0056] FIG1 is a schematic diagram showing a flow chart of a method for identifying abnormal noise of a motor provided in an embodiment of the present application;

[0057] FIG2 is a schematic flow chart of another method for identifying abnormal motor noise provided by an embodiment of the present application;

[0058] FIG3 shows a first schematic diagram of an exemplary butterfly displacement according to an embodiment of the present application;

[0059] FIG4 shows a second schematic diagram of an exemplary butterfly displacement according to an embodiment of the present application;

[0060] FIG5 shows a schematic diagram 1 of a full data waterfall without howling according to an example provided by an embodiment of the present application;

[0061] FIG6 shows a second schematic diagram of a full data waterfall with howling according to an example provided by an embodiment of the present application;

[0062] FIG7 is a schematic diagram showing an exemplary display effect provided by an embodiment of the present application;

[0063] FIG8 shows a schematic structural diagram of a device for identifying abnormal noise of a motor provided in an embodiment of the present application. DETAILED DESCRIPTION

[0064] In order to more clearly understand the above-mentioned objects, features and advantages of the present application, the scheme of the present application will be further described below. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments set forth herein. On the contrary, these embodiments are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are for illustrative purposes only and are not intended to limit the scope of protection of the present disclosure. All other embodiments obtained by those of ordinary skill in the art without making creative work are within the scope of protection of this application.

[0065] It should be noted that, unless there is any conflict, the embodiments of the present application and the features therein may be combined with each other.

[0066] In order to improve the current existing technology for identifying abnormal noises such as howling that may occur when the motor in the vehicle is running, the process involves a large amount of calculation, which in turn wastes computing power and increases the cost of identifying abnormal motor noises. This embodiment provides a method for identifying abnormal motor noises, as shown in Figure 1, the method comprising:

[0067] Step 101: Obtain motor speed information and sound time domain data.

[0068] A motor is an electromagnetic device that converts or transmits electrical 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.

[0069] The motor speed information may include the current motor speed, the speed at different time points in the current time period, or the speed at various time points in a continuous time period, etc. The corresponding sound time domain data may include the current sound time domain data, the current time period sound time domain data, or the sound time domain data in a continuous time period, etc.

[0070] The sounds emitted by the motor at different speeds are different, and the corresponding characteristics of abnormal noises are also different. In this embodiment, when identifying whether the motor has abnormal noises, it is necessary 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.

[0071] It should be noted that the specific time can be the moment when the vehicle speed reaches a preset speed threshold; the specific time can also be the moment when the motor temperature reaches a preset temperature threshold; the specific time can also be the time indicated by the user or other time. The specific specific time can be determined according to actual conditions, and the embodiments of the present application do not limit this.

[0072] Step 102: Determine the sampling frequency required to identify abnormal noise corresponding to the speed information.

[0073] Since the motor makes different sounds at different speeds, the corresponding characteristics of abnormal noises are also different, and therefore the sampling frequencies required to identify the abnormal noises are also different. This embodiment can collect the motor speed information, accurately analyze and determine the sampling frequency required to identify the abnormal noises, and then perform targeted downsampling processing on the sampling frequency, that is, execute the process shown in step 103.

[0074] Step 103: Downsample the sound time domain data according to the sampling frequency.

[0075] 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.

[0076] Step 104 : Analyze the sound energy corresponding to the rotation speed information based on the downsampled sound time domain data.

[0077] Sound energy may represent the sound energy received by the medium due to acoustic vibration when a sound wave propagates in the medium. In this embodiment, the sound energy is the sound spectrum energy that can be perceived by the human ear, which can be specifically obtained by converting the spectrum energy.

[0078] 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 abnormal noise exists based on the sound energy analysis.

[0079] A spectrum is a representation of a time-domain signal in the frequency domain. This can be obtained by performing a Fourier transform on the signal. The result is two graphs with amplitude and phase on the vertical axis and frequency on the horizontal axis. However, phase information is sometimes omitted, leaving only the amplitude data corresponding to different frequencies. The "amplitude spectrum" is sometimes used to show how amplitude varies with frequency, while the "phase spectrum" shows how phase varies with frequency. Simply put, a spectrum shows which frequencies of sine waves make up a signal, and also reveals information such as the magnitude and phase of each sine wave.

[0080] Sound is a mechanical vibration that can travel through matter in all its states. Sound cannot propagate in a vacuum. The sound we hear is also a sound wave with a specific frequency. The human ear's frequency range is approximately 20 to 20,000 Hz; frequencies outside this range are imperceptible. Sound below 20 Hz is considered infrasound, while sounds above 20 kHz are considered ultrasonic. The higher the frequency, the higher the pitch, while the lower the frequency, the lower the pitch.

[0081] Step 105 : Identify whether the motor has abnormal noise by comparing the sound energy with a preset energy threshold.

[0082] The preset energy threshold may be an evaluation threshold set for sound energy, and is used to identify whether the motor has abnormal noise.

[0083] Among them, the preset energy threshold can be a threshold configured in the motor abnormal noise identification device, or a threshold transmitted to the motor abnormal noise identification device by other devices, or a threshold obtained by the motor abnormal noise identification device through other methods. The specific method for the motor abnormal noise identification device to obtain the preset energy threshold can be determined according to actual conditions, and the embodiments of the present application do not limit this.

[0084] 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 current speed of the motor is determined, 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 used to identify 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.

[0085] Furthermore, 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 a specific method as shown in FIG2 , which includes:

[0086] Step 201: Obtain motor speed information and sound time domain data.

[0087] Optionally, obtain the motor speed and sound time domain data corresponding to the current time domain window.

[0088] It should be noted that the time length corresponding to the current time domain window can be 1 second, the time length corresponding to the current time domain window can also be 2 seconds, and the time length corresponding to the current time domain window can also be other time lengths. The specific time length corresponding to the current time domain window can be determined according to actual conditions, and the embodiments of the present application do not limit this.

[0089] For example, taking the current time domain window as 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.

[0090] Step 202: Obtain the first-order frequency of the motor corresponding to the speed information.

[0091] Resonant frequency refers to the two vibrations the motor produces, one up and one down, for each up and down movement of the piston. Therefore, the motor's vibration frequency is related to its speed. First-order vibrations have a frequency equal to the motor's speed, while second-order vibrations have a frequency twice the speed. This continues, with third- and fourth-order vibrations also occurring.

[0092] 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:

[0093] Where, Spd is the motor speed in RPM. Mot is the first-order frequency of the motor.

[0094] For example, 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. No examples are given here one by one.

[0095] 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 noise.

[0096] 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 noise; and increasing the positive and negative range values ​​based on the obtained product value to obtain a frequency band.

[0097] It should be noted that the empirical value of the order of abnormal noise occurrence may be a configured value.

[0098] 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 having abnormal noise frequently, 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 plus or minus 7HZ.

[0099] For example, based on step 202, if the first-order frequency of the current motor is 20 and the order of abnormal noise that often occurs is 2nd order, then the frequency band corresponding to the abnormal noise is determined to be 40. Based on the obtained frequency band value, the positive and negative range is added (i.e., 40+7=47, 40-7=33), and the frequency band for the abnormal noise can be selected as 33 to 47.

[0100] Step 204: Determine the Nyquist frequency corresponding to the maximum frequency in the frequency band as the sampling frequency.

[0101] A frequency band is the portion 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 range of frequencies between the highest and lowest frequencies permitted for transmission (taking into account the fact that attenuation must be within a certain range). If the difference between the two is significant, the frequency band can be considered equal to the highest permitted frequency.

[0102] For a signal, the frequency band is the range of frequencies between the highest and lowest frequencies contained in the signal (of course, the frequency components must be greater than a certain value). If the difference between the two is large, the frequency band can be roughly considered equal to the highest frequency of the signal. If a signal contains only an AC component of a single frequency or a limited number of frequencies, it is called a band signal.

[0103] The Nyquist frequency is the minimum sampling frequency that needs to be defined to prevent signal aliasing. Aliasing refers to the phenomenon in which time-domain periodic extension sequences overlap when the number of sampling points is less than the time-domain sequence length when the discrete Fourier transform is used to sample the signal in the frequency domain.

[0104] For example, if the sampling frequency is set to avoid overlap, a low sampling frequency will result in a sampled result that differs from the original sample. If a sample's spectrum is bandlimited—that is, zeros outside a certain frequency range—the sampling frequency must be greater than twice the current frequency to prevent overlap and distortion. This requirement is known as the Nyquist criterion.

[0105] In the embodiment of the present application, each frequency band value has a corresponding motor frequency. The maximum motor frequency within the frequency band is determined, and the corresponding Nyquist frequency is obtained based on the obtained maximum motor frequency, that is, the sampling frequency. The method for obtaining the Nyquist frequency is shown in the following formula 2: NQST =2f s (Formula 2)

[0106] f in formula 2 NQST is the Nyquist frequency, and fs is the maximum motor frequency.

[0107] Exemplarily, based on step 203 , the frequency band in which the motor makes abnormal noise is selected to be 33 to 47. If the maximum frequency of the motor is 128, then the Nyquist frequency of the motor can be determined to be 256, that is, the sampling frequency of the motor is 256.

[0108] Step 205: Downsample the sound time domain data according to the sampling frequency.

[0109] 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.

[0110] Exemplarily, based on step 204 , the current 0-1s sound time domain data includes 4096 items, and the sampling frequency of the motor is 256, that is, the 4096 time domain data are downsampled to 256 time domain data.

[0111] Step 206: Analyze the sound energy corresponding to the rotation speed information based on the downsampled sound time domain data.

[0112] 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; analyzing the spectral energy corresponding to each frequency based on the transposition sequence and the Nyquist frequency corresponding to each frequency in the frequency band; and obtaining the maximum energy from the spectral energy corresponding to each frequency as the sound energy corresponding to the speed information.

[0113] Optionally, step 206 specifically also includes: obtaining the number K of time domain samples 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] In an embodiment of the present application, taking the selection of a permutation sequence containing 16-bit data 0-15, the number of time domain samples R required to obtain the target frequency as 4, and the minimum number of samples in the sample set required for downsampling at the current target frequency as an example, as shown in Figures 3 and 4, 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 10 11 12 13 14 15] is downsampled to 8, the new permutation sequence is [0 2 4 6 8 10 12 14].

[0118] 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 are not given in detail here.

[0119] The new sequence is permuted, that is, after the original sequence is permuted, the first K numbers are taken for butterfly recursive calculation of the fast Fourier transform to obtain a sequence with the same minimum number of samples in the sample set. The spectral energy corresponding to the down-sampled sequence 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.

[0120] 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: A =M·coe A (Formula 3)

[0121] Among them, coeA is the A weighting coefficient, which represents the human ear's perception of energy in different frequency bands.

[0122] In the embodiment of the present application, the minimum 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: K=min(2 N >f NQST ) (Formula 4)

[0123] Where K is the smallest integer power of 2 greater than the Nyquist frequency.

[0124] For example, 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. Examples are not given one by one here.

[0125] Step 207 : Identify whether the motor has abnormal noise by comparing the sound energy with a preset energy threshold.

[0126] 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 size 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.

[0127] 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, 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.

[0128] 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. The sum of all areas is one. Therefore, analyzing the probability density of a single point alone is meaningless; it must be compared with an interval as a reference. In this embodiment of the application, the interval selected for the probability density can be several sampling points before and after the current speed sampling point, and this is not specifically limited here.

[0129] For example, based on step 206, the motor accelerates from 0 to 100, 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 labeling method can be continuous or discrete. In this embodiment, the labeling method is discrete and is based on the power of the energy.

[0130] 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 abnormal noise has occurred. The corresponding results are shown in Figures 5 and 6, which are comparisons of the full data waterfall charts. Figure 5 is a test without howling, and Figure 6 is a test with howling. It can be seen that the test in Figure 6 has obvious order energy accumulation. The test in Figure 6 has a higher order energy density, so abnormal noise can be identified. The corresponding relationship between the motor speed and the spectrum data can be shown in Figure 7.

[0131] Compared with the current existing technology, this embodiment simplifies the calculation process of identifying abnormal motor noises, reduces the amount of calculation, reduces the cost of identifying abnormal motor noises, and identifies abnormal motor noises of a fixed order, targeting the situation where abnormal noises such as howling may be generated when the motor in the vehicle is running.

[0132] Furthermore, as a specific implementation of the method shown in Figures 1 to 2, this embodiment provides a device for identifying abnormal noise of a motor. As shown in Figure 8, the device includes: an acquisition part 31, a determination part 32, a sampling part 33, an analysis part 34, and an identification part 35.

[0133] An acquisition part 31 is configured to acquire the speed information and sound time domain data of the motor;

[0134] The determination part 32 is configured to determine a sampling frequency required for identifying abnormal noise corresponding to the rotation speed information;

[0135] The sampling part 33 is configured to downsample the sound time domain data according to the sampling frequency;

[0136] an analyzing portion 34 configured to analyze the sound energy corresponding to the rotational speed information based on the downsampled sound time domain data;

[0137] The identification part 35 is configured to identify whether the motor has abnormal sound by comparing the sound energy with a preset energy threshold.

[0138] In a specific application scenario, the acquisition part 31 is specifically configured to obtain the first-order frequency of the motor corresponding to the speed information;

[0139] The analyzing part 34 is specifically configured to analyze the frequency band corresponding to the first-order frequency of the motor based on the empirical value of the order of occurrence of the abnormal sound;

[0140] The determination part 32 is specifically configured to determine a Nyquist frequency corresponding to the maximum frequency in the frequency band as the sampling frequency.

[0141] In a specific application scenario, the determination part 32 is further configured to multiply the first-order frequency of the motor by an empirical value of the order of the abnormal noise; and increase the positive and negative range values ​​based on the obtained product value to obtain the frequency band.

[0142] In a specific application scenario, the analysis part 34 is specifically configured to perform butterfly parity transposition of the 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, the spectral energy corresponding to each frequency is analyzed; the maximum energy obtained from the spectral energy corresponding to each frequency is used as the sound energy corresponding to the speed information.

[0143] In a specific application scenario, the analysis part 34 is further configured to obtain the number K of time domain samples 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.

[0144] In a specific application scenario, the analyzing part 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.

[0145] In a specific application scenario, the recognition part 35 is specifically configured to compare the sound energy corresponding to different speeds with the preset energy threshold, and configure label information corresponding to different speeds according to the extent of exceeding the preset energy threshold; and perform probability density calculation based on the label information corresponding to different speeds;

[0146] The determining part 32 is specifically configured to determine that the motor has an abnormal noise if the calculated probability density is greater than a preset density threshold.

[0147] It should be noted that for other corresponding descriptions of the functional units involved in the device for identifying abnormal noise of a motor provided in this embodiment, reference can be made to the corresponding descriptions in Figures 1 and 2, and no further details will be given here.

[0148] Based on the above method as shown in Figures 1 to 2, this 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 method as shown in Figures 1 to 2 is implemented.

[0149] 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.), and includes 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.

[0150] Based on the above-mentioned method as shown in Figures 1 to 2, and the virtual device embodiment shown in Figure 8, in order to achieve the above-mentioned purpose, the embodiment of the present application also provides an electronic device that can be configured on the vehicle side (such as a new energy vehicle) or the server side, etc., and 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 implement the above-mentioned method as shown in Figures 1 to 2.

[0151] Optionally, the physical device may further include a user interface, a network interface, a camera, a radio frequency (RF) circuit, a sensor, an audio circuit, a Wi-Fi module, and the like. The user interface may include a display, an input unit such as a keyboard, and the like. Optional user interfaces may also include a USB interface, a card reader interface, and the like. The network interface may optionally include a standard wired interface, a wireless interface (such as a Wi-Fi interface), and the like.

[0152] Those skilled in the art will understand 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 component arrangements.

[0153] 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 physical device, supporting the execution of information processing programs and other software and / or programs. The network communication module is used to enable communication between components within the storage medium, as well as with other hardware and software within the physical information processing device.

[0154] Furthermore, based on the above-mentioned method shown in Figures 1 to 2, the virtual device embodiment shown in Figure 8, and the above-mentioned electronic device embodiment, this embodiment also provides a vehicle, which may include the above-mentioned electronic device and further execute the method shown in Figures 1 to 2.

[0155] Through the description of the above implementation methods, those skilled in the art can clearly understand that this application can be implemented by means of software plus the necessary general hardware platform, or by means of hardware. By applying the solution of this embodiment, compared with the current existing technology, this embodiment can simplify the calculation process of identifying abnormal motor noises, reduce the amount of calculation, reduce the cost of identifying abnormal motor noises, and identify abnormal motor noises of a fixed order, in response to the situation where abnormal noises such as howling may be generated during the operation of the motor in the vehicle.

[0156] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any 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 that includes 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 presence of other identical elements in the process, method, article or device that includes the elements.

[0157] The foregoing is merely a list of specific embodiments of the present application, intended to enable those skilled in the art to understand and implement the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application is not limited to the embodiments described herein, but is intended to conform to the broadest scope consistent with the principles and novel features of the present application. Industrial Applicability

[0158] Embodiments of the present application provide a method, apparatus, computer-readable storage medium, electronic device, and vehicle for identifying abnormal motor noise. The method includes: first obtaining motor speed information and acoustic time-domain data; determining the sampling frequency required to identify abnormal noise corresponding to the speed information; then downsampling the acoustic time-domain data based on the sampling frequency; analyzing the sound energy corresponding to the speed information based on the downsampled acoustic time-domain data; and identifying whether the motor is making abnormal noise by comparing the sound energy with a preset energy threshold. Implementing this method simplifies the computational process for identifying abnormal motor noise, reduces the computational effort, lowers the cost of identifying abnormal motor noise, and allows identification of fixed-order abnormal motor noise.

Claims

1. A method for identifying abnormal noise of a motor, comprising: 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, wherein: 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, wherein: 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, wherein: 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, wherein: 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, wherein: 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, wherein: The step of comparing the sound energy with a preset energy threshold to identify whether the motor has an abnormal sound comprises: The sound energy corresponding to different speeds is compared with the preset energy threshold, and the sound energy corresponding to different speeds is compared with the preset energy threshold. The size of the threshold configures the label information corresponding to different speeds; 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, comprising: An acquisition part, configured to acquire the speed information and sound time domain data of the motor; A determination part configured to determine a sampling frequency required for identifying an abnormal sound corresponding to the rotation speed information; A sampling part, configured to downsample the sound time domain data according to the sampling frequency; An analysis part, configured to analyze the sound energy corresponding to the rotation speed information based on the downsampled sound time domain data; The identification part 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, wherein the computer program implements the method according to any one of claims 1 to 7 when executed by a processor.

10. 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 method according to any one of claims 1 to 7 when executing the computer program.

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

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