Motor rotating speed fluctuation measurement method, electronic equipment and computer readable storage medium

Abstract

The embodiment of the invention discloses a motor rotating speed fluctuation measurement method, electronic equipment and a computer readable storage medium, and the method comprises the steps: firstly obtaining a first sound signal of a motor in a constant-speed operation state, carrying out the time-frequency analysis of the first sound signal, obtaining the time-frequency information of the motor, screening the time-frequency information, and obtaining a first sound signal; and extracting a rotating speed fluctuation curve of the motor, and finally calculating the maximum rotating speed fluctuation of the motor according to the maximum value and the minimum value of the rotating speed fluctuation curve. When the motor is at different rotating speeds, sound signals emitted by the motor are different, and if the rotating speed fluctuates, the corresponding sound signals change, so that the rotating speed fluctuation of the motor can be obtained by measuring the fluctuation of the sound signals when the motor runs at a constant speed, and the dynamic process of the rotating speed fluctuation of the motor is monitored and recorded. The operation is more convenient, and the measurement scene is wider.

Description

Technical Field

[0001] This application relates to the field of motor speed measurement technology, and in particular to a method for measuring motor speed fluctuation, an electronic device, and a computer-readable storage medium. Background Technology

[0002] During operation, the speed of an electric motor is not constant; speed fluctuation refers to the change in the motor's speed around a set or desired value. Excessive speed fluctuation can adversely affect the motor's performance and lifespan. Generally, the range of speed fluctuation should be controlled within certain limits to ensure stable operation and optimal performance. Therefore, it is necessary to monitor motor speed fluctuations.

[0003] Current measurement methods generally use stroboscopes or infrared laser reflective paper, but they cannot accurately measure the fluctuation of motor speed. Alternatively, they use the angle sensor built into the motor, but this is not suitable for the overall machine environment or motors without built-in angle sensors, limiting application scenarios and reducing versatility. Summary of the Invention

[0004] In view of this, embodiments of this application provide a method for measuring motor speed fluctuations, an electronic device, and a computer-readable storage medium, which can perform non-contact and accurate measurement of motor speed fluctuations.

[0005] In a first aspect, embodiments of this application provide a method for measuring motor speed fluctuation, the method comprising:

[0006] Acquire a first sound signal of the motor in a constant-speed operation state; perform time-frequency analysis on the first sound signal to obtain the time-frequency information of the motor, wherein the time-frequency information includes carrier frequency, resonant frequency band and noise order; filter the time-frequency information to extract the speed fluctuation curve of the motor; calculate the maximum speed fluctuation of the motor based on the maximum and minimum values ​​of the speed fluctuation curve.

[0007] This measurement method obtains the speed fluctuation of a motor by measuring the fluctuation of the sound signal when the motor is running at a constant speed. Since the sound signal generated by the motor changes with the change of the motor speed, this measurement method can achieve non-contact and accurate measurement of the speed fluctuation of the motor, monitor and record the dynamic process of the speed fluctuation of the motor, and is more convenient to operate and has a wider range of measurement scenarios.

[0008] In some embodiments, performing time-frequency analysis on the first sound signal to obtain the time-frequency information of the motor includes: performing time-frequency analysis on the first sound signal and plotting a first time-frequency diagram; and obtaining the time-frequency information of the motor based on the first time-frequency diagram.

[0009] In the above embodiments, time-frequency information can be intuitively obtained by drawing a time-frequency diagram through time-frequency analysis.

[0010] In some embodiments, before performing time-frequency analysis on the first sound signal to obtain the time-frequency information of the motor, the method further includes: acquiring a second sound signal of the motor in a uniformly variable speed operation state; performing time-frequency analysis on the second sound signal to draw a second time-frequency diagram; the step of performing time-frequency analysis on the first sound signal to obtain the time-frequency information of the motor includes: performing time-frequency analysis on the first sound signal to draw a first time-frequency diagram; and obtaining the time-frequency information of the motor based on the first time-frequency diagram and the second time-frequency diagram.

[0011] When the first time-frequency diagram is unclear and the time-frequency information cannot be accurately extracted, the second time-frequency diagram can display the time-frequency information more clearly. By combining the first and second time-frequency diagrams, the time-frequency information of the motor is obtained, thus improving the accuracy of the time-frequency information.

[0012] In some embodiments, the step of filtering the time-frequency information and extracting the motor speed fluctuation curve includes: filtering noise orders whose frequencies change over time from the time-frequency information to determine multiple characteristic frequencies of the motor, wherein the characteristic frequencies correspond one-to-one with the noise orders; selecting a target characteristic frequency from the multiple characteristic frequencies, wherein the target characteristic frequency satisfies the following conditions: it is continuous in the time domain, and the difference between the intensity of the corresponding motor noise and the intensity of the background noise is greater than a first threshold, the first threshold being related to the intensity of the motor noise corresponding to the target characteristic frequency; and obtaining the motor speed fluctuation curve by fitting the time-frequency distribution of the target characteristic frequency.

[0013] In this embodiment of the application, by selecting a target characteristic frequency that is easy to distinguish and continuous in the time domain to obtain its corresponding fluctuation curve, the speed fluctuation of the motor can be obtained more accurately.

[0014] In some embodiments, calculating the maximum speed fluctuation of the motor based on the maximum and minimum values ​​of the speed fluctuation curve includes: calculating the average value of the maximum and minimum values ​​of the speed fluctuation curve, and the difference between the maximum and minimum values; dividing the difference by the average value to obtain the maximum speed fluctuation of the motor.

[0015] In this embodiment of the application, the motor speed fluctuation range can be obtained by the peak value, trough value and average value on the speed fluctuation curve, thereby reflecting the magnitude of the motor speed fluctuation.

[0016] In some embodiments, the time-frequency analysis method includes short-time Fourier transform or wavelet transform.

[0017] In the embodiments of this application, various transformation methods can be used for time-frequency analysis to obtain a more accurate and complete first time-frequency diagram or second time-frequency diagram.

[0018] In some embodiments, acquiring the first sound signal of the motor in a constant-speed operation state includes: acquiring the first motor sound signal of the motor in a constant-speed operation state collected by an acoustic sensor; and compensating the first motor sound signal based on the amplitude-frequency response of the acoustic sensor to obtain the first sound signal.

[0019] In this embodiment, the amplitude-frequency response of the acoustic sensor is used to compensate for the sound signal of the first motor, thereby reducing distortion and improving the accuracy and fidelity of the sound signal of the first motor, thus improving the accuracy of noise monitoring.

[0020] In some embodiments, acquiring the second sound signal of the motor in a uniformly variable speed operation state includes: acquiring the second motor sound signal of the motor in a uniformly variable speed operation state collected by an acoustic sensor; and compensating the second motor sound signal based on the amplitude-frequency response of the acoustic sensor to obtain the second sound signal.

[0021] In this embodiment of the application, by compensating the second motor sound signal through amplitude-frequency response, distortion can be reduced and background noise signals can be removed.

[0022] Secondly, this application provides an electronic device, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the motor speed fluctuation measurement method as described above.

[0023] Thirdly, this application provides a computer-readable storage medium storing computer-executable instructions for causing a computer device to perform the motor speed fluctuation measurement method described above.

[0024] The beneficial effects of this application's embodiments are as follows: Unlike existing technologies, the method for measuring motor speed fluctuation provided in this application first acquires a first sound signal of the motor operating at a constant speed, and performs time-frequency analysis on the first sound signal to obtain the motor's time-frequency information. This time-frequency information includes the carrier frequency, resonant frequency band, and noise order. Then, the time-frequency information is filtered to extract the motor's speed fluctuation curve. Finally, the maximum speed fluctuation of the motor is calculated based on the maximum and minimum values ​​on the speed fluctuation curve. The sound signal emitted by the motor differs at different speeds, and if the speed fluctuates, the corresponding sound signal also changes. Therefore, this measurement method obtains the motor's speed fluctuation by measuring the fluctuation of the sound signal when the motor is operating at a constant speed. This allows for accurate, non-contact measurement of the motor's speed fluctuation, monitoring and recording the dynamic process of the motor's speed fluctuation, making the measurement more convenient and applicable to a wider range of scenarios. Attached Figure Description

[0025] One or more embodiments are illustrated by way of example with reference numerals in the accompanying drawings. These illustrations do not constitute a limitation on the embodiments. Elements with the same reference numerals in the drawings are denoted as similar elements. Unless otherwise stated, the figures in the drawings are not to be limited by scale.

[0026] Figure 1 This is a flowchart illustrating a method for measuring motor speed fluctuation provided in an embodiment of this application;

[0027] Figure 2 This is a schematic diagram of a first time-frequency diagram provided in an embodiment of this application;

[0028] Figure 3 This is a schematic diagram of a second time-frequency diagram provided in an embodiment of this application;

[0029] Figure 4 yes Figure 1 A flowchart illustrating step S13;

[0030] Figure 5 This is a schematic diagram of a first time-frequency diagram provided in an embodiment of this application;

[0031] Figure 6 This is a schematic diagram of the hardware structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0032] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0033] It should be noted that, unless there is a conflict, the various features in the embodiments of this application can be combined with each other, and all are within the protection scope of this invention. Furthermore, although functional modules are divided in the device schematic diagram and a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than the module division in the device or the order in the flowchart. In addition, the terms "first," "second," and "third" used herein do not limit the data or execution order, but only distinguish identical or similar items with substantially the same function and effect.

[0034] Unless otherwise defined, all technical and scientific terms used in this specification have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. The term "or" as used in this specification includes any and all combinations of one or more of the associated listed items.

[0035] The speed of an electric motor is not constant during operation; speed fluctuation refers to the change in the motor's speed around a set or desired value. Excessive speed fluctuation can adversely affect the motor's performance and lifespan. Generally, the range of speed fluctuation should be controlled within certain limits to ensure stable operation and optimal performance. Therefore, it is necessary to measure the speed fluctuation of the motor.

[0036] Current methods for measuring motor speed fluctuations, such as those using stroboscopes or infrared laser reflective paper, rely on visual inspection and have low instrument update frequencies, making accurate measurement of dynamic speed fluctuations difficult. Another strategy utilizes the motor's own angle sensor, requiring analysis of the sensor's signals to determine the speed fluctuation. However, since the motor interface is typically integrated into the machine, this method is difficult to implement in environments where the motor is already installed. Furthermore, it is unsuitable for motors without angle sensors, limiting its applicability and versatility.

[0037] For the reasons mentioned above, this application provides a method for measuring motor speed fluctuation. This method can accurately monitor motor speed fluctuation and can achieve non-contact measurement, making it more convenient to operate and applicable to a wider range of measurement scenarios.

[0038] Please see Figure 1 , Figure 1 This is a flowchart illustrating the method for measuring motor speed fluctuation provided in an embodiment of this application. Figure 1 As shown, the method S100 may specifically include the following steps:

[0039] S11: Acquire the first sound signal of the motor when it is running at a constant speed.

[0040] S12: Perform time-frequency analysis on the first sound signal to obtain the time-frequency information of the motor, wherein the time-frequency information includes the carrier frequency, resonant frequency band and noise order.

[0041] S13: Filter the time and frequency information and extract the motor speed fluctuation curve.

[0042] S14: Calculate the maximum speed fluctuation of the motor based on the maximum and minimum values ​​of the speed fluctuation curve.

[0043] In this embodiment, an acoustic sensor can be used to collect the first sound signal of the motor under constant speed conditions, and then time-frequency analysis can be performed on the first sound signal. Time-frequency analysis is used to analyze time-varying non-stationary signals. It can provide joint distribution information of the first sound signal in the time and frequency domains, clearly describing the relationship between the frequency of the first sound signal and time. Therefore, by performing time-frequency analysis on the first sound signal, the time-frequency information of the motor can be obtained. The time-frequency information includes the carrier frequency, resonant frequency band, and noise order. By filtering the time-frequency information, the speed fluctuation curve of the motor can be extracted. Finally, based on the maximum and minimum values ​​of the speed fluctuation curve, the maximum speed fluctuation of the motor can be calculated.

[0044] The carrier frequency refers to the switching frequency of the motor driver. The switching of the motor driver causes harmonic components of the switching frequency in the current, resulting in noise emitted by the motor at that frequency. The resonant frequency band is the modal frequency of the motor or its structural components. When the motor's excitation frequency is close to or coincides with this band, it will amplify the amplitude of the motor or its components, thus radiating greater noise. The noise order refers to the number of times a certain noise occurs per revolution of the motor. It characterizes the frequency of the noise. If it is order k, the noise frequency is k times the motor's rotational frequency. k can be an integer or a fraction, and it is a value that does not change with the rotational speed.

[0045] The sound signals emitted by a motor vary depending on its speed. Therefore, this measurement method obtains the fluctuation of the motor's speed by measuring the fluctuation of the sound signal when the motor is running at a constant speed. Moreover, the sound signal generated by the motor changes with the change of the motor speed, which can accurately monitor the dynamic process of the motor speed fluctuation. At the same time, this measurement method can achieve non-contact measurement, making the measurement more convenient and applicable to a wider range of scenarios.

[0046] In some embodiments, step S11 specifically includes:

[0047] S111: Acquire the first motor sound signal collected by the acoustic sensor when the motor is running at a constant speed.

[0048] S112: The first sound signal is obtained by compensating the first motor sound signal based on the amplitude-frequency response of the acoustic sensor.

[0049] A set control signal is sent to the motor so that the motor reaches and maintains a set speed V1. When the motor is running at the set speed V1, an acoustic sensor is used to collect the first motor sound signal generated when the motor is running, and the first motor sound signal is compensated based on the amplitude-frequency response of the acoustic sensor to obtain the first sound signal.

[0050] The amplitude-frequency response (ARF) of an acoustic sensor refers to the variation in the amplitude of the output signal generated by the sensor in response to sound waves of different frequencies. It is a crucial performance indicator of acoustic sensors, reflecting their sensitivity and responsiveness at different frequencies. ARF characterizes the relationship between the amplitude of the output signal and the frequency of the input sound wave. Acoustic sensors typically have an operating frequency band within which their response is relatively stable. When the input sound wave frequency exceeds this band, the sensor's sensitivity decreases significantly, resulting in a smaller output signal amplitude, which may cause distortion. The purpose of compensating for sound signals based on the ARF of acoustic sensors is to correct the differences in sensor sensitivity at different frequencies, ensuring that the output sound signal accurately reflects the original acoustic environment. Therefore, compensating for and correcting the sound signal of the first motor based on the ARF of acoustic sensors can improve the accuracy and fidelity of the first sound signal and enhance the accuracy of noise monitoring.

[0051] In some embodiments, the sampling frequency of the acoustic sensor is at least twice the highest frequency component of the first motor sound signal. The sampling frequency can be set as needed; to obtain better sound quality and less distortion, a higher sampling frequency is typically chosen to obtain a more complete first motor sound signal. For example, in one embodiment, the sampling frequency is 192 kHz.

[0052] In some embodiments, the first motor sound signal includes a first motor noise signal and a first background noise signal, wherein the intensity of the first background noise signal is less than the intensity of the first motor noise signal. In some embodiments, the difference between the intensity of the first motor noise signal and the intensity of the first background noise signal is greater than or equal to a first intensity threshold. The first intensity threshold can be set according to the intensity of the first motor noise signal. Specifically, the acquisition operation is performed in a scene with low background noise; for example, the first intensity threshold is 10 dB, meaning the intensity of the first background noise signal is less than the intensity of the first motor noise signal by 10 dB.

[0053] In some embodiments, step S11 further includes: denoising the first motor sound signal to obtain a first motor noise signal. For example, high-pass filtering, low-pass filtering, or band-pass filtering are used to filter out the first background noise signal in the collected first motor sound signal. S112 includes: compensating the first motor noise signal based on the amplitude-frequency response of the acoustic sensor to obtain a first sound signal.

[0054] In some embodiments, step S12 includes:

[0055] S121: Perform time-frequency analysis on the first sound signal and draw the first time-frequency diagram.

[0056] S122: Based on the first time-frequency diagram, obtain the time-frequency information of the motor.

[0057] In some embodiments, when performing time-frequency analysis on the first audio signal, the time-frequency analysis method used includes short-time Fourier transform or wavelet transform.

[0058] The Short-Time Fourier Transform (STFT) divides a long signal into shorter time segments and performs a Fourier transform on each segment, providing frequency information about how the signal changes over time. The Wavelet Transform decomposes a signal into a superposition of wavelet functions. Wavelet functions possess locality and multi-scale properties, enabling precise decomposition of the signal into components of different frequencies. These time-frequency analysis methods can analyze signals whose frequency changes over time, generating clear time-frequency plots, and are suitable for processing non-stationary signals.

[0059] The following example uses short-time Fourier transform to perform time-frequency analysis on the first sound signal to describe the process of time-domain analysis.

[0060] S201: Select a smooth window function to divide the first sound signal into multiple short-time signal slices.

[0061] Each signal slice represents a local region of the first audio signal, and the length of the signal slice determines the resolution of the time-frequency analysis. Specifically, shorter signal slices can better capture rapid changes in time; longer signal slices can better distinguish subtle changes in frequency. That is, shorter signal slices have better time resolution but poorer frequency resolution; longer slices provide better frequency resolution but reduce time resolution. The specific length can be set as needed. The sampling interval of the signal slices depends on the desired time resolution. The sampling interval refers to the time interval between two adjacent signal slices. If the time interval is too long, rapid changes in the signal may be missed; if the time interval is too short, the computational load increases while the information gain is small.

[0062] In some embodiments, the window function can be a Hamming Window, a Hanning Window, a Blackman Window, a Gaussian Window, etc., with smooth edge transitions.

[0063] In some embodiments, adjacent signal slices at least partially overlap. This partial overlap increases the number of data points, thereby improving the accuracy of signal feature estimation. Overlapping reduces signal distortion caused by window boundaries and ensures a smooth transition between adjacent signal slices, maintaining better time-frequency continuity. In some embodiments, the overlapping portion between adjacent signal slices accounts for less than or equal to 50% of each signal slice.

[0064] In some embodiments, S201 further includes: weighting the first audio signal using a window function. When performing frequency domain analysis (such as STFT), it is typically necessary to divide a long signal into multiple short segments for processing. However, directly performing Fourier transforms on these short segments can produce boundary effects, also known as spectral leakage, leading to distortion of frequency components. Weighting the signal using a window function can smooth the start and end points of the signal, reduce spectral leakage caused by signal truncation, and improve the accuracy of spectral analysis. Specifically, the value of each signal slice is multiplied by the value of the corresponding window function.

[0065] S202: Perform a Fourier transform on each signal slice to obtain the first time-frequency matrix.

[0066] The Fourier transform converts a signal slice from a time-domain signal to a frequency-domain signal. Each signal slice undergoes a Discrete Fourier Transform (DFT), yielding a Fourier transform result. These results are stored in a two-dimensional matrix, resulting in the first time-frequency matrix. Each column of this matrix corresponds to the Fourier transform result of a signal slice, and each element represents the amplitude or power at a specific time and frequency point.

[0067] In some embodiments, the Fourier transform is implemented using the Fast Fourier Transform (FFT) algorithm to achieve higher computational efficiency.

[0068] S203: Draw the first time-frequency diagram based on the first time-frequency matrix.

[0069] In some embodiments, in order to more intuitively observe the frequency change trend of the first sound signal over time, a spectrum diagram of the first sound signal, i.e., a first time-frequency diagram, can be plotted based on a first time-frequency matrix. The horizontal axis of the first time-frequency diagram represents time, the vertical axis represents frequency, and the color or grayscale value represents the signal strength (amplitude or power) of a certain frequency at that moment, thereby allowing for a direct observation of the frequency components of the first sound signal at different times.

[0070] STFT obtains the time-frequency distribution of a signal by segmenting and windowing the signal, and then performing a Fourier transform on each segment. It is suitable for analyzing signals with different frequency characteristics in different time periods.

[0071] In some embodiments, the first time-frequency diagram is as follows: Figure 2 As shown, the first time-frequency plot contains multiple curves, each corresponding to a noise order. The fluctuation of each curve represents the frequency variation trend of the noise order corresponding to that curve. Figure 2 In the first time-frequency diagram shown, the noise order includes the conventional order and the umbrella-shaped order. The starting point of the wave curve corresponding to the umbrella-shaped order is not zero, and it scatters in an umbrella shape on both sides of the carrier frequency. The frequency corresponding to the umbrella-shaped order is the modulation wave frequency and its harmonics. The modulation wave refers to the fundamental wave and its harmonics generated under the action of the carrier signal.

[0072] In such Figure 2 In the first time-frequency diagram shown, box S1 displays the frequency fluctuation curves corresponding to the umbrella-shaped order. The carrier frequency line corresponding to the carrier frequency is L1. Centered on carrier frequency line L1, the symmetrical frequency lines appearing on both sides of L1 correspond to the fundamental frequency f1. Since the fundamental frequency f1 increases with increasing rotational speed, the symmetrical frequency lines appearing on both sides of carrier frequency line L1 exhibit umbrella-shaped scattering. Box S2 displays the resonant frequency band, which contains multiple resonant frequencies. When the motor's excitation frequency is close to or coincides with this resonant frequency, it will amplify the amplitude of the motor or its auxiliary structural components, thus radiating greater noise.

[0073] Based on the first time-frequency diagram, time-frequency information such as carrier frequency, noise order, and resonant frequency band can be obtained. The frequency fluctuation curves corresponding to each noise order can also be obtained, and thus the speed fluctuation curve of the motor can be obtained.

[0074] In some embodiments, if the first time-frequency diagram corresponding to the first sound signal is not clear enough and cannot clearly display time-frequency information such as umbrella-shaped order and resonant frequency band, then the second sound signal corresponding to the uniform speed change of the motor can be obtained, the second sound signal can be analyzed in terms of time and frequency to obtain the second time-frequency diagram, and the time-frequency information can be obtained by combining the first time-frequency diagram and the second time-frequency diagram.

[0075] Specifically, before obtaining the time-frequency information of the motor based on the first time-frequency diagram, the measurement method further includes:

[0076] S15: Acquire the second sound signal of the motor when it is running at a constant speed.

[0077] S16: Perform time-frequency analysis on the second sound signal and plot the second time-frequency diagram.

[0078] The second time-frequency diagram obtained when the motor is in a uniform speed change state can more clearly and explicitly display the time-frequency information, making it easier to distinguish the resonance band and the carrier frequency.

[0079] In some embodiments, step S15 specifically includes:

[0080] S151: Acquire the second motor sound signal collected by the acoustic sensor when the motor is running at a constant speed.

[0081] S152: The second motor sound signal is compensated based on the amplitude-frequency response of the acoustic sensor to obtain the second sound signal.

[0082] The motor is controlled to accelerate uniformly to V2, or to decelerate uniformly to V3. During this operation, an acoustic sensor is used to collect the second motor sound signal generated when the motor is operating under uniform speed change. The second motor sound signal is then compensated based on the amplitude-frequency response of the acoustic sensor to obtain the second sound signal. This embodiment improves the accuracy and fidelity of the second sound signal and enhances the accuracy of noise monitoring.

[0083] In some embodiments, the second motor sound signal includes a second motor noise signal and a second background noise signal, wherein the intensity of the second background noise signal is less than the intensity of the second motor noise signal. In some embodiments, the difference between the intensity of the second motor noise signal and the intensity of the second background noise signal is greater than or equal to a second intensity threshold. The second intensity threshold can be set according to the intensity of the second motor noise signal. Specifically, the acquisition operation is performed in a scenario with low background noise; for example, the second intensity threshold is 10 dB, meaning the intensity of the second background noise signal is 10 dB less than the intensity of the second motor noise signal.

[0084] In some embodiments, when performing time-frequency analysis on the second audio signal, the time-frequency analysis method is the same as that for the first audio signal, such as short-time Fourier transform or wavelet transform.

[0085] In some embodiments, step S12 further includes:

[0086] S123: Perform time-frequency analysis on the first sound signal and plot the first time-frequency diagram;

[0087] S124: Based on the first and second time-frequency diagrams, obtain the time-frequency information of the motor.

[0088] As can be seen from the above embodiments, a first time-frequency diagram is obtained by performing time-frequency analysis on the first sound signal using short-time Fourier transform or wavelet transform. The first time-frequency diagram can be as follows: Figure 2 As shown, time-frequency analysis is performed on the second sound signal to obtain the second time-frequency diagram, as shown in the figure. Figure 3 As shown.

[0089] Figure 3 The box S3 shows the umbrella-shaped order, and the box S4 shows the resonant frequency band. In one embodiment, Figure 3 In the second time-frequency diagram shown, the carrier frequency f of the motor is 7806Hz, and the center frequency of the resonant frequency band is 1430Hz. The second time-frequency diagram can more clearly display time-frequency information such as noise order, carrier frequency, and resonant frequency band. Combining the first and second time-frequency diagrams helps to obtain more accurate time-frequency information.

[0090] In some embodiments, such as Figure 4 As shown, step S13 includes:

[0091] S131: Filter noise orders whose frequencies change over time from the time-frequency information to determine multiple characteristic frequencies of the motor, wherein each characteristic frequency corresponds one-to-one with a noise order.

[0092] The value of the characteristic frequency changes with the motor's speed, but the noise order corresponding to the characteristic frequency remains constant. Therefore, the fluctuation of the characteristic frequency over time can be used to characterize the fluctuation of the motor's speed. For example, Figure 5 The three characteristic frequencies shown are f4, f5, and f6.

[0093] S132: Select a target characteristic frequency from multiple characteristic frequencies, wherein the target characteristic frequency is continuous in the time domain, and the difference between the intensity of the corresponding motor noise and the intensity of the background noise is greater than a first threshold, and the first threshold is related to the intensity of the motor noise corresponding to the target characteristic frequency.

[0094] Among the characteristic frequencies excluding the resonant frequency band and the carrier frequency, a characteristic frequency that is continuous in the time domain and whose noise order is easily distinguishable is selected as the target characteristic frequency. In some embodiments, the difference between the intensity of the sound signal corresponding to the selected target characteristic frequency and the intensity of the background noise signal is greater than a first threshold. The first threshold can be set as needed; in this embodiment, it can be 3 dB. For example, Figure 5 Based on the selection principles described above, the characteristic frequency f5 can be selected as the target characteristic frequency from the three characteristic frequencies shown.

[0095] S133: By fitting the time-frequency distribution of the target characteristic frequency, the speed fluctuation curve of the motor is obtained.

[0096] After selecting the target characteristic frequency, the fluctuation curve of the target characteristic frequency, i.e. the speed fluctuation curve of the motor, is obtained by fitting the set of points contained in the target characteristic frequency.

[0097] In some embodiments, step S14 specifically includes:

[0098] S141: Calculate the average of the maximum and minimum values ​​of the speed fluctuation curve, and the difference between the maximum and minimum values;

[0099] S142: Divide the difference by the average value to obtain the maximum speed fluctuation of the motor.

[0100] The speed fluctuation curve represents the speed fluctuation trend of the motor. The difference between the peak value and the trough value also represents the difference between the maximum and minimum speed values ​​of the motor. Therefore, the maximum speed fluctuation of the motor can be obtained from this speed fluctuation curve.

[0101] Specifically, the maximum value on the speed fluctuation curve is the peak value, and the minimum value is the trough value. First, calculate the difference between the maximum and minimum values. Then, calculate the average of the maximum and minimum values. Finally, divide the difference by the average value to obtain the maximum speed fluctuation. The maximum speed fluctuation is a percentage value; a larger value indicates greater fluctuation.

[0102] For example, if the maximum characteristic frequency corresponding to a certain speed order of the motor is 3155Hz and the minimum characteristic frequency corresponding to that speed order is 2760Hz, then the maximum speed fluctuation of the motor is: (3155-2760) / (3155+2760) / 2=3.34%.

[0103] In summary, this measurement method obtains the motor speed fluctuation by measuring the fluctuation of the sound signal when the motor is running at a constant speed. It can accurately monitor the motor speed fluctuation in a non-contact manner, making the measurement more convenient and applicable to a wider range of scenarios.

[0104] The method for measuring motor speed fluctuations provided in this application can be executed by a processor or by other devices with computing capabilities (such as electronic devices). This computer program can be integrated into an application or run as a standalone utility application.

[0105] It should be noted that if the method for measuring motor speed fluctuation provided in this application embodiment is executed by an electronic device, the electronic device can be hardware or software. When the electronic device is hardware, it can be implemented as a distributed cluster composed of multiple servers or terminal devices, or as a single server or a single terminal device. When the electronic device is software, it can be installed in the hardware devices listed above. It can be implemented as, for example, multiple software or software modules used to provide distributed services, or as a single software or software module. It should be understood that the number of electronic devices is merely illustrative; any number of electronic devices can be used according to implementation needs.

[0106] This application also provides an electronic device; please refer to [link / reference]. Figure 6 , Figure 6 This is a schematic diagram of the hardware structure of an electronic device provided in an embodiment of this application. For example... Figure 6 As shown, the electronic device 300 includes at least one processor 301 and a memory 302 connected in communication. Figure 6 (Taking a bus connection and a single processor as an example).

[0107] The processor 301 provides computing and control capabilities to control the electronic device 300 to execute the motor speed fluctuation measurement method in any of the above method embodiments. In some embodiments, the processor 301 may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), a hardware chip, or any combination thereof; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The PLD may be a complex programmable logic device (CPLD), a field-programmable gate array (FPGA), a generic array logic (GAL), or any combination thereof.

[0108] The memory 302, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs, non-transitory computer-executable programs, and modules, such as the program instructions / modules corresponding to the motor speed fluctuation measurement method in the embodiments of this application. The processor 301 can implement the motor speed fluctuation measurement method in any of the above-described method embodiments by running the non-transitory software programs, instructions, and modules stored in the memory 302. To avoid repetition, it will not be described again here.

[0109] In some embodiments, memory 302 may include volatile memory (VM), such as random access memory (RAM); memory 302 may also include non-volatile memory (NVM), such as read-only memory (ROM), flash memory, hard disk drive (HDD), solid-state drive (SSD), or other non-transitory solid-state storage devices; memory 302 may also include combinations of the above types of memory. In some embodiments, memory 302 may also include memory remotely located relative to the processor, and this remote memory may be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0110] In the embodiments of this application, the lidar 300 may also include other components for realizing the device functions, which will not be described in detail here.

[0111] This application also provides a computer-readable storage medium storing computer-executable instructions that can be executed by a processor to complete the method for measuring motor speed fluctuations in the above embodiments. In some embodiments, the computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a compact disc read-only memory (CDROM), magnetic tape, floppy disk, or optical data storage device, etc.

[0112] This application also provides a computer program product comprising one or more lines of program code stored in a computer-readable storage medium. A processor of an electronic device reads the program code from the computer-readable storage medium and executes the program code to complete the method steps of the motor speed fluctuation measurement method provided in the above embodiments.

[0113] It should be noted that the device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0114] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented using software and a general-purpose hardware platform, or of course, using hardware. Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0115] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; under the concept of the present invention, the technical features of the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations of different aspects of the present invention as described above, which are not provided in detail for the sake of brevity; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for measuring motor speed fluctuation, characterized in that, The method includes: Acquire the first sound signal of the motor when it is running at a constant speed; Time-frequency analysis is performed on the first sound signal to obtain the time-frequency information of the motor, wherein the time-frequency information includes carrier frequency, resonant frequency band and noise order; The time-frequency information is filtered to extract the motor speed fluctuation curve; The maximum speed fluctuation of the motor is calculated based on the maximum and minimum values ​​of the speed fluctuation curve.

2. The method according to claim 1, characterized in that, The step of performing time-frequency analysis on the first sound signal to obtain the time-frequency information of the motor includes: Perform time-frequency analysis on the first sound signal and plot the first time-frequency diagram; Based on the first time-frequency diagram, the time-frequency information of the motor is obtained.

3. The method according to claim 1, characterized in that, Before performing time-frequency analysis on the first sound signal to obtain the time-frequency information of the motor, the method further includes: Acquire the second sound signal of the motor in a uniformly variable speed operation state; Perform time-frequency analysis on the second sound signal and plot the second time-frequency diagram; The step of performing time-frequency analysis on the first sound signal to obtain the time-frequency information of the motor includes: Perform time-frequency analysis on the first sound signal and plot the first time-frequency diagram; Based on the first time-frequency diagram and the second time-frequency diagram, the time-frequency information of the motor is obtained.

4. The method according to claim 1, characterized in that, The step of filtering the time-frequency information and extracting the motor speed fluctuation curve includes: The noise order whose frequency changes over time is filtered from the time-frequency information to determine multiple characteristic frequencies of the motor, wherein each characteristic frequency corresponds one-to-one with the noise order. A target feature frequency is selected from a plurality of the aforementioned feature frequencies, wherein the target feature frequency satisfies the following conditions: it is continuous in the time domain, and the difference between the intensity of the corresponding motor noise and the intensity of the background noise is greater than a first threshold, wherein the first threshold is related to the intensity of the motor noise corresponding to the target feature frequency; The speed fluctuation curve of the motor is obtained by fitting the time-frequency distribution of the target characteristic frequency.

5. The method according to claim 4, characterized in that, The step of calculating the maximum speed fluctuation of the motor based on the maximum and minimum values ​​of the speed fluctuation curve includes: Calculate the average of the maximum and minimum values ​​of the speed fluctuation curve, and the difference between the maximum and minimum values; The maximum speed fluctuation of the motor is obtained by dividing the difference by the average value.

6. The method according to claim 1, characterized in that, The time-frequency analysis methods include short-time Fourier transform or wavelet transform.

7. The method according to claim 1, characterized in that, The acquisition of the first sound signal of the motor in a constant-speed operation state includes: Acquire the first motor sound signal collected by the acoustic sensor when the motor is running at a constant speed; The first sound signal is obtained by compensating the first motor sound signal based on the amplitude-frequency response of the acoustic sensor.

8. The method according to claim 3, characterized in that, The acquisition of the second sound signal of the motor in a uniformly variable speed operation state includes: Acquire the second motor sound signal collected by the acoustic sensor when the motor is in a uniform speed change operation state; The second motor sound signal is obtained by compensating for the amplitude-frequency response of the acoustic sensor.

9. An electronic device, characterized in that, include: A processor; A memory communicatively connected to the processor; wherein the memory stores instructions executable by the processor, the instructions being executed by the processor to enable the processor to perform the method for measuring motor speed fluctuation as described in any one of claims 1-8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions for causing a computer device to perform the method for measuring motor speed fluctuation as described in any one of claims 1-8.

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