Motor wind friction loss measurement method and device, electronic equipment and storage medium

Abstract

The invention provides a motor wind friction loss measuring method and device, electronic equipment and a storage medium, the motor wind friction loss measuring method comprises the following steps: detecting first torque data and second torque data of a tested motor, the first torque data being used for indicating the output torque of the tested motor when the tested motor is in a no-load state, and the second torque data being used for indicating the output torque of the tested motor when the tested motor is in a no-load state; the second torque data is used for indicating the output torque of the tested motor when the fan is loaded; determining fan loss measurement data according to the first torque data and the second torque data; performing simulation analysis on the tested motor to obtain fan loss simulation data; calibrating the fan loss measurement data according to the fan loss simulation data to obtain calibrated data; and determining the wind friction loss of the tested motor according to the calibrated data. According to the scheme, the accuracy of the obtained wind friction loss can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of electrical engineering, and particularly to a method, device, electronic device and storage medium for measuring the windage and friction loss of a motor. Background Art

[0002] Windage and friction loss refers to the energy loss generated due to air resistance during the operation of a motor, which is mainly caused by the friction between the surface of the fan and the surrounding air. Accurately obtaining the windage and friction loss can provide a reference for the design of the rotor structure and the heat dissipation structure. The absolute value of the windage and friction loss is small and it is difficult to measure directly.

[0003] Currently, by abstracting the rotor as a cylinder, the windage and friction loss is calculated based on the surface roughness coefficient, friction coefficient, air density, and the rotational angular velocity, length, and radius of the cylinder, etc.

[0004] However, since the value of the friction coefficient is based on an empirical formula, there is a difference between the friction coefficient used to calculate the windage and friction loss in different test environments and the actual friction coefficient, resulting in poor accuracy of the calculated windage and friction loss. Summary of the Invention

[0005] In view of this, the method, device, electronic device and storage medium for measuring the windage and friction loss of a motor provided by the present invention can improve the accuracy of the measured windage and friction loss.

[0006] According to the first aspect of the embodiments of the present invention, a method for measuring the windage and friction loss of a motor is provided, including: detecting first torque data and second torque data of the motor to be measured, where the first torque data is used to indicate the output torque of the motor to be measured when it is unloaded, and the second torque data is used to indicate the output torque of the motor to be measured when a fan is loaded; determining fan loss measurement data according to the first torque data and the second torque data; performing simulation analysis on the motor to be measured to obtain fan loss simulation data; calibrating the fan loss measurement data according to the fan loss simulation data to obtain calibrated data; and determining the windage and friction loss of the motor to be measured according to the calibrated data.

[0007] In a first possible implementation manner, in combination with the first aspect described above, detecting the first torque data and the second torque data of the motor under test includes: fixing the motor under test on a first support platform; adjusting the height of the auxiliary centering platform, wherein a V-block is arranged on the auxiliary centering platform; adjusting the spatial positions of the first support platform and the second support platform based on the V-block to make the axis of the output shaft of the motor under test coincide with the axis of the torque sensor, wherein a driving motor and the torque sensor are fixed on the second support platform, and the output shaft of the driving motor is connected to the torque sensor; after lowering the auxiliary centering platform to separate the output shaft of the motor under test from the V-block, connecting the output shaft of the motor under test to the torque sensor through a coupling; controlling the driving motor to drive the motor under test to rotate through the torque sensor; obtaining the torque data detected by the torque sensor, wherein the first torque data is obtained when the motor under test is unloaded, and the second torque data is obtained when a fan is loaded on the motor under test.

[0008] In a second possible implementation manner, in combination with the first aspect described above, determining the fan loss measurement data according to the first torque data and the second torque data includes: calculating the difference between the data points corresponding to the same rotational speed in the first torque data and the second torque data, and determining the absolute value of the calculated difference as the fan loss measurement data.

[0009] In a third possible implementation manner, in combination with the first aspect described above, calibrating the fan loss measurement data according to the fan loss simulation data to obtain calibrated data includes: preprocessing the fan loss measurement data to obtain preprocessed data, wherein the preprocessing includes at least one of deleting duplicate data, supplementing missing data, deleting discrete outliers, deleting noise data, and normalizing the data; analyzing the preprocessed data to obtain a feature analysis result; if the feature analysis result indicates that the preprocessed data does not include repetitive abnormal features, determining the preprocessed data as the data to be calibrated; if the feature analysis result indicates that the preprocessed data includes repetitive abnormal features, obtaining the data to be calibrated after removing the abnormal feature data included in the preprocessed data; calibrating the data to be calibrated according to the fan loss simulation data to obtain calibrated data.

[0010] In a fourth possible implementation manner, in combination with the third possible implementation manner described above, removing the abnormal feature data in the preprocessed data to obtain the data to be calibrated includes: inputting the repetitive abnormal features included in the preprocessed data into a data filtering model, and removing the abnormal feature data included in the preprocessed data through the data filtering model to obtain the data to be calibrated.

[0011] In the fifth possible implementation manner, in combination with the fourth possible implementation manner described above, the method further includes: inputting the repetitive anomaly feature into an anomaly analysis model, and performing anomaly analysis on the motor under test through the anomaly analysis model to determine the anomaly event that causes the repetitive anomaly feature.

[0012] In the sixth possible implementation manner, in combination with any one of the third possible implementation manner to the fifth possible implementation manner described above, calibrating the data to be calibrated according to the fan loss simulation data to obtain calibrated data includes: calculating a Kalman gain according to the fan loss simulation data and the data to be calibrated; weighting the fan loss simulation data and the data to be calibrated according to the Kalman gain to obtain the calibrated data.

[0013] In the seventh possible implementation manner, in combination with the sixth possible implementation manner described above, calculating the Kalman gain according to the fan loss simulation data and the data to be calibrated includes: calculating a predicted value of the fan loss torque at the k-th moment according to the fan loss simulation data through the following formula: which is used to represent the predicted value of the fan loss torque at the k-th moment, which is used to represent the optimal estimate of the fan loss torque at the (k - 1)-th moment, u [k-1] which is used to represent the control input amount at the (k - 1)-th moment during the process of detecting the first torque data and the second torque data, A is used to represent a preset state transition matrix, and B is used to represent a preset control matrix; calculating a priori error covariance matrix of the true value and the predicted value of the fan loss torque through the following formula: which is used to represent the a priori error covariance matrix of the true value and the predicted value of the fan loss torque at the k-th moment, P [k-1] which is used to represent the posterior error covariance matrix of the true value and the optimal estimate of the fan loss torque at the (k - 1)-th moment determined based on the data to be calibrated, Q is used to represent the covariance of the process noise determined based on the data to be calibrated; calculating the Kalman gain at the k-th moment through the following formula: K [k] which is used to represent the Kalman gain at the k-th moment, H is used to represent an observation transition matrix determined based on the data to be calibrated, and R is used to represent the covariance of the measurement noise determined based on the data to be calibrated.

[0014] In the eighth possible implementation manner, in combination with the seventh possible implementation manner described above, weighting the fan loss simulation data and the data to be calibrated according to the Kalman gain to obtain the calibrated data includes: calculating the optimal estimate of the fan loss torque at the k-th moment through the following formula: For characterizing the optimal estimate of the fan loss torque at the k-th moment, z [k] For characterizing the true value of the fan loss torque corresponding to the k-th moment in the data to be calibrated; determining the optimal estimate of the fan loss torque at each moment as the calibrated data.

[0015] In the ninth possible implementation manner, in combination with the seventh possible implementation manner described above, the method further includes:

[0016] After obtaining the Kalman gain at the k-th moment, calculate the true value of the fan loss torque at the k-th moment and the posterior error covariance matrix of the optimal estimate through the following formula: P [k] For characterizing the posterior error covariance matrix of the true value and the optimal estimate of the fan loss torque at the k-th moment, and I is used to characterize the identity matrix.

[0017] According to the second aspect of the embodiments of the present invention, there is provided a device for measuring the windage and friction loss of a motor, including: a detection unit for detecting first torque data and second torque data of the motor to be measured, wherein the first torque data is used to indicate the output torque of the motor to be measured when it is unloaded, and the second torque data is used to indicate the output torque of the motor to be measured when a fan is loaded; a calculation unit for determining fan loss measurement data according to the first torque data and the second torque data; a simulation unit for performing simulation analysis on the motor to be measured to obtain fan loss simulation data; a calibration unit for calibrating the fan loss measurement data according to the fan loss simulation data to obtain calibrated data; and a mapping unit for determining the windage and friction loss of the motor to be measured according to the calibrated data.

[0018] In a third aspect, the embodiments of the present invention further provide an electronic device, including: a processor, a memory, a communication interface, and a communication bus, and the processor, the memory, and the communication interface complete communication with each other through the communication bus; the memory is used to store at least one executable instruction, and the executable instruction causes the processor to execute the operations corresponding to the method for measuring the windage and friction loss of the motor fan provided in the first aspect or any possible implementation manner of the first aspect.

[0019] In a fourth aspect, the embodiments of the present invention further provide a computer-readable storage medium, on which computer instructions are stored, and when the computer instructions are executed by a processor, the processor is caused to execute the method for measuring the windage and friction loss of the motor fan provided in the first aspect or any possible implementation manner of the first aspect.

[0020] Fifth aspect, an embodiment of the present invention further provides a computer program product, which is tangibly stored on a computer-readable medium and includes computer-executable instructions. When the computer-executable instructions are executed, at least one processor is caused to execute the method for measuring the wind friction loss of the motor fan provided in the above first aspect or any possible implementation manner of the first aspect.

[0021] As can be seen from the above technical solutions, after detecting the first torque data of the motor under test when it is unloaded and the second torque data when the fan is loaded, the fan loss measurement data is determined according to the first torque data and the second torque data. The fan loss measurement data can indicate the torque change caused by the fan. Therefore, the fan loss measurement data is linearly related to the wind friction loss of the motor under test. The calibrated data is obtained by calibrating the fan loss measurement data with the fan loss simulation data. The calibrated data is linearly related to the wind friction loss of the motor under test. Therefore, the wind friction loss of the motor under test can be determined according to the calibrated data. By measuring the fan loss measurement data caused by the wind friction loss and calibrating the fan loss measurement data based on the fan loss simulation data, the noise and constant data included in the fan loss measurement data are reduced. Furthermore, the wind friction loss of the motor under test is determined based on the calibrated data obtained by calibrating the fan loss measurement data. Since no parameters with uncertain errors are introduced during the measurement process of the wind friction loss, the accuracy of the obtained wind friction loss can be improved. Description of the Drawings

[0022] Figure 1 is a flowchart of the method for measuring the wind friction loss of the motor in an embodiment of the present invention;

[0023] Figure 2 is a schematic diagram of the wind friction loss test platform in an embodiment of the present invention;

[0024] Figure 3 is a flowchart of the torque data detection method in an embodiment of the present invention;

[0025] Figure 4 is a flowchart of the data calibration method in an embodiment of the present invention;

[0026] Figure 5 is a flowchart of the Kalman gain calculation method in an embodiment of the present invention;

[0027] Figure 6 is a schematic diagram of the device for measuring the wind friction loss of the motor in an embodiment of the present invention;

[0028] Figure 7 is a schematic diagram of the electronic device in an embodiment of the present invention.

[0029] List of Reference Numerals:

[0030] 100: Motor wind friction loss measurement method; 101-105: Method steps; 200: Wind friction loss test platform; 201: First support platform; 202: Second support platform; 203: Auxiliary centering platform; 204: Tested motor; 205: Driving motor; 206: Torque sensor; 207: First coupling; 208: V-shaped block; 209: Second coupling; 300: Torque data detection method; 301-306: Method steps; 400 : Data calibration method; 401~405: Method steps; 500: Kalman gain calculation method; 501~503: Method steps; 600: Motor wind friction loss measurement device; 601: Detection unit; 602: Calculation unit; 603: Simulation unit; 604: Calibration unit; 605: Mapping unit; 700: Electronic device; 702: Processor; 704: Communication interface; 706: Memory; 708: Communication bus; 710: Program. DETAILED DESCRIPTION

[0031] As mentioned above, since the absolute value of the wind friction loss is small and difficult to measure directly, the rotor is currently abstracted as a cylinder and the wind friction loss generated when the rotor rotates is calculated using the following formula (1):

[0032]

[0033] Among them, P fw It is used to characterize wind friction loss, k is used to characterize surface roughness coefficient, C f It is used to represent the friction coefficient, π is used to represent the circumference, ρ air Used to characterize air density, ω m It is used to characterize the angular velocity of the cylinder, l is used to characterize the axial length of the cylinder, and r is used to characterize the radius of the cylinder.

[0034] Friction coefficient C f It needs to be determined based on an empirical formula. However, according to the references, there are many methods for calculating the friction coefficient C. f The friction coefficient C calculated by different empirical formulas f Different, it is difficult to determine which empirical formula to use to calculate the friction coefficient C f More accurate and different test environments to calculate the friction coefficient C used for wind friction loss f There is a difference between the actual friction coefficient and the actual friction coefficient, which leads to the poor accuracy of the obtained wind friction loss.

[0035] In the embodiments of the present invention, the output torque of the motor is measured respectively under the test conditions of the motor being no - load and the motor being loaded with a fan. The absolute value of the difference between the output torques measured under the above two test conditions is the torque loss caused by wind - friction loss. Based on the torque loss simulation data of the motor, the measured value of the torque loss can be calibrated to reduce the influence of noise, and then the wind - friction loss can be calculated according to the calibrated measured value of the torque loss. By measuring the torque loss caused by wind - friction loss and correcting the measured value of the torque loss based on the simulation data, the noise in the measured value of the torque loss is reduced. Furthermore, based on the calibrated measured value of the torque loss, the wind - friction calibration is determined. The process of measuring the wind - friction loss does not introduce data such as friction coefficients with errors, so the accuracy of the obtained wind - friction loss can be improved.

[0036] The method, device, and electronic device for measuring the wind - friction loss of a motor provided by the embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0037] Figure 1 It is a flowchart of a method 100 for measuring the wind - friction loss of a motor according to an embodiment of the present invention. The method 100 for measuring the wind - friction loss of a motor includes the following steps:

[0038] Step 101, detect the first torque data and the second torque data of the motor to be measured.

[0039] When the motor to be measured is no - load, detect the output torque of the motor to be measured to obtain the first torque data. When the motor to be measured is loaded with a fan, detect the output torque of the motor to be measured to obtain the second torque data. The motor to be measured being no - load means the state where the motor to be measured is not installed with a fan, and the motor to be measured being loaded with a fan means the state where the motor to be measured is installed with a fan.

[0040] It should be noted that when detecting the first torque data and the second torque data, the motor to be measured can be driven to rotate by a driving motor. The output shaft of the driving motor and the output shaft of the motor to be measured are connected through a torque sensor, and the first torque data and the second torque data are detected through the torque sensor. It should be understood that when the driving motor drives the motor to be measured to rotate, the output shafts of the driving motor and the motor to be measured generate torques that interact with each other. The driving motor outputs a torque to drive the motor to be measured to rotate, and the motor to be measured outputs a torque to hinder the rotation of the driving motor. Therefore, in the embodiments of the present invention, the output torque of the motor to be measured does not refer to the torque output when the motor to be measured rotates under electric drive, but refers to the torque acting on the output shaft of the motor to be measured when the motor to be measured is dragged by the driving motor to rotate.

[0041] Step 102, determine the fan loss measurement data according to the first torque data and the second torque data.

[0042] The first torque data can indicate the output torque of the motor under test when it is unloaded. The fan is not installed on the motor under test when it is unloaded, and at this time, the windage and friction loss of the motor is approximately zero. The second torque data can indicate the output torque of the motor under test when the fan is loaded. The fan is installed on the motor under test when the fan is loaded, and at this time, the fan will generate windage and friction loss when the motor under test rotates.

[0043] When the rotational speed of the motor under test is the same, the components of the first torque data and the second torque data other than the windage and friction loss are the same. Therefore, based on the first torque data and the second torque data corresponding to relevant rotational speeds, the torque change caused by loading the fan on the motor under test can be determined, and this torque change is determined as the fan loss measurement data at this rotational speed.

[0044] Step 103: Conduct a simulation analysis on the motor under test to obtain fan loss simulation data.

[0045] Based on the performance parameters and control parameters of the motor under test, a simulation analysis can be conducted on the motor under test to obtain the simulation value of the torque change caused by loading the fan on the motor under test, and then this simulation value of the torque change is determined as the fan loss simulation data. In one example, the performance parameters of the motor under test may include some or all of power, rated voltage, rated current, rated speed, temperature rise, insulation class, and protection class, and the control parameters include some or all of output power, input voltage, input current, ambient temperature, ambient humidity, fan material, and actual speed.

[0046] It should be noted that since the rotational speed of the motor under test will affect the torque loss caused by the fan and the windage and friction loss is different at different rotational speeds, when conducting a simulation analysis on the motor under test, the rotational speed of the motor under test should match the rotational speed of the motor under test during the measurement of the first torque data and the second torque data. For example, if the first torque data and the second torque data are measured when the motor under test rotates at full speed, then the motor under test is simulated at its full speed to obtain the fan loss simulation data at the full speed of the motor under test.

[0047] The first torque data and the second torque data can be detected at one or more rotational speeds of the motor under test, or can be detected at one or more rotational speed ranges of the motor under test. The embodiments of the present invention do not limit this.

[0048] Step 104: Calibrate the fan loss measurement data according to the fan loss simulation data to obtain the calibrated data.

[0049] When detecting the first torque data and the second torque data, due to electrical noise in the inverter-driven motor signal (such as the variable frequency switching frequency, PID / PWM control strategy, etc.), the material structure and machining errors of the motor under test itself, component resonance deformation that may occur during acceleration and deceleration tests, etc., the first torque data and / or the second torque data may fluctuate abnormally, which in turn causes the fan loss measurement data determined based on the first torque data and the second torque data to include noise and abnormal data.

[0050] The fan loss simulation data is obtained through simulation analysis of the motor under test, which does not include the noise and abnormal data caused by the above reasons. Therefore, the fan loss measurement data can be calibrated according to the fan loss simulation data to reduce or eliminate the noise and abnormal data included in the fan loss measurement data and obtain the calibrated data.

[0051] Step 105: Determine the windage and friction loss of the motor under test according to the calibrated data.

[0052] The calibrated data is obtained by calibrating the fan loss measurement data. The fan loss measurement data indicates the torque change value caused by the fan. There is a linear relationship between the torque change value introduced by the fan and the windage and friction loss of the motor under test. Therefore, there is a linear relationship between the calibrated data and the windage and friction loss of the motor under test. Therefore, after obtaining the calibrated data, the windage and friction loss of the motor under test can be determined according to this linear relationship.

[0053] In the embodiment of the present invention, after detecting the first torque data of the motor under test when it is no-load and the second torque data when the fan is loaded, the fan loss measurement data is determined according to the first torque data and the second torque data. The fan loss measurement data can indicate the torque change caused by the fan. Therefore, the fan loss measurement data is linearly related to the windage and friction loss of the motor under test. The calibrated data is obtained by calibrating the fan loss measurement data with the fan loss simulation data. The calibrated data is linearly related to the windage and friction loss of the motor under test. Therefore, the windage and friction loss of the motor under test can be determined according to the calibrated data. By measuring the fan loss measurement data caused by the windage and friction loss and calibrating the fan loss measurement data based on the fan loss simulation data, the noise and constant data included in the fan loss measurement data are reduced. Furthermore, the windage and friction loss of the motor under test is determined based on the calibrated data obtained by calibrating the fan loss measurement data. Since no parameters with uncertain errors are introduced during the measurement process of the windage and friction loss, the accuracy of the obtained windage and friction loss can be improved.

[0054] In a possible implementation manner, to ensure the accuracy of the first torque data and the second torque data, the motor under test can be tested through a windage and friction loss test platform to obtain the first torque data and the second torque data.

[0055] Figure 2The schematic diagram of the wind friction loss test platform 200 according to an embodiment of the present invention is shown. As Figure 2 shown, the wind friction loss test platform 200 includes a first support platform 201, a second support platform 202, and an auxiliary centering platform 203.

[0056] The motor under test 204 is fixed on the first support platform 201, and the driving motor 205 and the torque sensor 206 are fixed on the second support platform 202. The output shaft of the driving motor 205 and the first connection end of the torque sensor 206 are connected by a first coupling 207. Both the first support platform 201 and the second support platform 202 can move in three directions of the mutually perpendicular X-axis, Y-axis, and Z-axis. That is, the first support platform 201 can drive the motor under test 204 to move in three-dimensional space, and the second support platform 202 can drive the driving motor 205 and the torque sensor 206 to move in three-dimensional space.

[0057] A V-shaped block 208 for auxiliary centering alignment is fixed on the auxiliary centering platform 203. The auxiliary centering platform 203 can drive the V-shaped block 208 to move up and down along the Z-axis direction.

[0058] The torque sensor 206 has an appropriate bandwidth and sampling frequency to ensure that the obtained first torque data and second torque data will not have signal distortion, and at the same time avoid excessive cost and complexity in subsequent processing of the first torque data and second torque data.

[0059] The torque sensor 206 can be a torque sensor supporting multiple measurement ranges, or a torque sensor with a fixed measurement range. When the torque sensor 206 is a torque sensor supporting multiple measurement ranges, the first torque data and second torque data of different types of motors can be tested through this torque sensor 206, making the wind friction loss test platform 200 have strong applicability. When the torque sensor 206 is a torque sensor with a fixed measurement range, a corresponding torque sensor can be selected as the torque sensor 206 according to the range of the output torque of the motor under test 204. Different motors under test can select torque sensors with different measurement ranges to be connected to the driving motor 205.

[0060] The following takes the test of the motor under test 204 by the Figure 2 shown wind friction loss test platform 200 as an example to elaborate in detail the process of detecting the first torque data and second torque data.

[0061] Figure 3 The flowchart of the torque data detection method 300 according to an embodiment of the present invention is shown, which is used to detect the first torque data and second torque data of the motor under test 204. As Figure 3 shown, the torque data detection method 300 includes the following steps:

[0062] Step 301: Fix the motor under test 204 on the first support platform 201.

[0063] Step 302: Adjust the height of the auxiliary centering platform 203.

[0064] Adjust the height of the auxiliary centering platform 203 in the Z-axis direction so that the V-shaped block 208 is at an appropriate height.

[0065] Step 303: Based on the V-shaped block 208, adjust the spatial positions of the first support platform 201 and the second support platform 202 so that the output shaft of the motor under test 204 coincides with the axis of the torque sensor 206.

[0066] Based on the position of the V-shaped block 208, adjust the spatial position of the first support platform 201 so that the output shaft of the motor under test 204 contacts the two side walls of the V-shaped groove on the V-shaped block 208, and adjust the spatial position of the second support platform 202 so that the second connection end of the torque sensor 206 contacts the two side walls of the V-shaped groove on the V-shaped block 208, that is, make the axis of the output shaft of the motor under test 204 coincide with the axis of the second connection end of the torque sensor 206, and make the end of the output shaft of the motor under test 204 abut against the end of the second connection end of the torque sensor 206.

[0067] Step 304: After lowering the auxiliary centering platform 203 to separate the output shaft of the motor under test 204 from the V-shaped block 208, connect the motor under test 204 and the torque sensor 206 through the second coupling 209.

[0068] After the axis of the output shaft of the motor under test 204 coincides with the axis of the second connection end of the torque sensor 206, lower the auxiliary centering platform 203 to separate the V-shaped block 208 from the output shaft of the motor under test 204 and the second connection end of the torque sensor 206, and then connect the output shaft of the motor under test 204 and the second connection end of the torque sensor 206 through the second coupling 209.

[0069] Step 305: Control the driving motor 205 to drive the motor under test 204 to rotate through the torque sensor 206.

[0070] The output shaft of the motor under test 204 is connected to the second connection end of the torque sensor 206 through the second coupling 209, and the first connection end of the torque sensor 206 is connected to the output shaft of the driving motor 205 through the first coupling 207. When the driving motor 205 is controlled to rotate according to the preset control parameters, the driving motor 205 drives the motor under test 204 to rotate through the torque sensor 206.

[0071] Step 306: Obtain the torque data detected by the torque sensor 206.

[0072] During the process of driving the motor under test 204 to rotate by the driving motor 205 through the torque sensor 206, the torque sensor 206 can detect the torque acting on the output shaft of the motor under test 204, and this torque is the output torque of the motor under test 204. When the motor under test 204 is unloaded, the first torque data is detected by the torque sensor 206. When the fan is loaded on the motor under test 204, the second torque data is detected by the torque sensor 206.

[0073] It should be noted that since the output torque of the motor under test 204 is affected not only by windage and friction losses, but also by many other factors, such as ambient temperature, temperature, fan material, and manufacturing process, etc. In order to ensure that the windage and friction loss can be accurately reflected according to the fan loss measurement data, during the process of testing the first torque data and the second torque data, it is necessary to control the ambient temperature, temperature, fan material, and manufacturing process, etc. to be as stable as possible.

[0074] In the embodiment of the present invention, the first torque data and the second torque data of the motor under test 204 are tested by the windage and friction loss test platform 200, and it is ensured that the output shaft of the motor under test 204 coincides with the axis of the torque sensor 206 during the test, so that the fan loss measurement data determined based on the first torque data and the second torque data can accurately reflect the windage and friction loss introduced by the fan, and further ensure the accuracy of the windage and friction loss determined based on the fan loss measurement data.

[0075] In a possible implementation manner, when determining the fan loss measurement data according to the first torque data and the second torque data, the difference between the data points corresponding to the same rotational speed in the first torque data and the second torque data can be calculated, and the absolute value of the calculated difference is determined as the fan loss measurement data.

[0076] The first torque data and the second torque data include the output torque of the motor under test 204 at at least one rotational speed. During the test process of the first torque data and the second torque data, the motor under test 204 rotates according to the same time-rotational speed curve, that is, when respectively testing the first torque data and the second torque data, the rotational speed of the motor under test 204 at the k-th moment after the start of measurement is the same.

[0077] When the rotational speed of the motor under test 204 is the same, the change in the output torque of the motor under test 204 when the motor under test 204 is unloaded and the fan is loaded is caused by the fan, that is, caused by the windage and friction loss of the motor under test 204. Therefore, the absolute value of the difference between the data points corresponding to the same rotational speed of the motor under test 204 in the first torque data and the second torque data can be used as the fan loss measurement data at this rotational speed.

[0078] In one example, when the motor 204 under test rotates according to the same time - speed curve during the testing of the first torque data and the second torque data, the absolute value of the difference between the data points corresponding to the k - th moment in the first torque data and the second torque data is used as the fan loss measurement data corresponding to the k - th moment, where k is an integer from 0 and less than or equal to N, and N is the number of data points included in the first torque data / the second torque data.

[0079] In the embodiments of the present invention, the difference between the data points corresponding to the same rotational speed in the first torque data and the second torque data is introduced by the fan, that is, caused by windage loss. Therefore, the absolute value of the difference between the data points corresponding to the same rotational speed in the first torque data and the second torque data can be used as the fan loss measurement data, ensuring that the determined fan loss measurement data can accurately reflect the windage loss, and further ensuring the accuracy of the windage loss determined based on the fan loss measurement data.

[0080] In a possible implementation manner, Figure 4 FIG. shows a flowchart of a data calibration method 400 according to an embodiment of the present invention, which is used to calibrate the fan loss measurement data according to the fan loss simulation data, as Figure 4 shown, the data calibration method 400 includes the following steps:

[0081] Step 401: Pre - process the fan loss measurement data to obtain pre - processed data.

[0082] Pre - processing the fan loss measurement data includes deleting duplicate data, supplementing missing data, deleting discrete outliers, deleting noise data, and normalizing the data, etc., to improve the quality and usability of the fan loss measurement data.

[0083] During the pre - processing of the fan loss measurement data, check the integrity of the data to ensure that all necessary data points are collected, then check the accuracy of the data, identify and correct errors and missing values in the data, while deleting duplicate data records and excluding discrete outliers and noise data. It is also necessary to normalize the fan loss measurement data to eliminate differences between different measurement devices. To remove noise and reduce data fluctuations, it is necessary to smooth the fan loss measurement data. Filters can be used to remove noise in a specific frequency range from the fan loss measurement data, or the moving average method can be used to reduce random fluctuations. It is also possible to perform operations such as standardization and normalization on the fan loss measurement data to make the fan loss measurement data more comparable.

[0084] Step 402: Analyze the pre - processed data to obtain a feature analysis result.

[0085] After preprocessing the fan loss measurement data to obtain the preprocessed data, the preprocessed data is time-series data. To further process the preprocessed data, the continuous data can be converted into discrete categories or distinctions to facilitate subsequent analysis and processing. Extract meaningful features from the preprocessed data to describe the preprocessed data through the extracted features. For example, features such as the average value, variance, peak value, and frequency can be extracted from the preprocessed data. The extracted features can indicate the statistical characteristics and periodic changes of the preprocessed data.

[0086] To check whether the preprocessed data has periodic characteristics, spectral analysis (such as Fourier transform or wavelet transform) can be used to analyze the frequency components of the preprocessed data to determine whether there are periodic patterns. Based on the periodic patterns of the preprocessed data, the performance changes of the motor fan can be checked and diagnosed.

[0087] By analyzing the preprocessed data, a feature analysis result can be obtained, and the feature analysis result can indicate whether the preprocessed data includes repetitive abnormal features.

[0088] Step 403: If the feature analysis result indicates that the preprocessed data does not include repetitive abnormal features, the preprocessed data is determined as the data to be calibrated.

[0089] If the feature analysis result indicates that the preprocessed data does not include repetitive abnormal features, there is no need to further process the preprocessed data. The preprocessed data can be directly calibrated according to the fan loss simulation data, and thus the preprocessed data is determined as the data to be calibrated.

[0090] Step 404: If the feature analysis result indicates that the preprocessed data includes repetitive abnormal features, after removing the abnormal feature data included in the preprocessed data, the data to be calibrated is obtained.

[0091] If the preprocessed data includes repetitive abnormal features, that is, there are obvious periodic fluctuation outliers in the preprocessed data, which may be caused by resonance or deformation of the fan or shaft, structural or assembly problems. At this time, the preprocessed data needs to be further processed to remove these abnormal feature data, and then the preprocessed data after removing the abnormal feature data is determined as the data to be calibrated.

[0092] Step 405: Calibrate the data to be calibrated according to the fan loss simulation data to obtain the calibrated data.

[0093] In an embodiment of the present invention, by preprocessing the fan loss measurement data, at least part of the noise included in the fan loss measurement data is removed, and the random fluctuation of the data is reduced to obtain the preprocessed data. By analyzing the preprocessed data, if the preprocessed data includes repetitive abnormal features, the abnormal feature data caused by the resonance or deformation of the fan or shaft in the motor under test, or structural or assembly problems is removed to obtain the data to be calibrated, so that the data to be calibrated includes less noise and / or interference. Furthermore, the data to be calibrated is calibrated by the fan loss simulation data to obtain the calibrated data, ensuring the accuracy of the wind friction loss determined based on the calibrated data.

[0094] In a possible implementation manner, the repetitive abnormal features included in the preprocessed data can be input into a pre-determined data filtering model, and the abnormal feature data included in the preprocessed data is removed by the data filtering model to obtain the data to be calibrated.

[0095] The data filtering model can be a neural network model. Through the neural network model, the abnormal values with periodic fluctuations in the preprocessed data can be identified, and after filtering out these abnormal values, the data to be calibrated is output.

[0096] In an embodiment of the present invention, the abnormal feature data in the preprocessed data is filtered out by the data filtering model, ensuring that the abnormal values with periodic fluctuations in the preprocessed data can be filtered out, ensuring that the data to be calibrated includes less noise and interference, and further ensuring the accuracy of the obtained wind friction loss.

[0097] In a possible implementation manner, after determining that the preprocessed data includes repetitive abnormal features, the repetitive abnormal features included in the preprocessed data can be input into a pre-determined abnormal analysis model, and the abnormal analysis model is used to perform abnormal analysis on the motor under test to determine the abnormal event that causes the repetitive abnormal features.

[0098] The abnormal analysis model can be implemented based on a threshold method, a statistical method, a machine learning method, or a surrogate model method.

[0099] When using machine learning or a surrogate model to detect abnormal events, the acquisition of the model involves steps such as data preprocessing, simulation technology, model selection (training), and result evaluation. The accuracy of data instructions and model training is ensured through data preprocessing and data analysis. Performance data of the motor under different conditions is obtained through simulation and used to establish a surrogate model. The surrogate model is an approximate model that can replace a complex and detailed model. The surrogate model can be implemented based on a polynomial response surface method, a Kriging interpolation method, a radial basis function interpolation method, a neural network method, a support vector machine regression method, etc.

[0100] After selecting a suitable surrogate model, methods such as genetic algorithms or multi-objective optimization algorithms can be used to find the optimal solution under the premise of meeting the constraint conditions. The simulation results are used as verification data to verify the trained surrogate model and evaluate its accuracy and reliability. By defining the criteria for judging outliers, the wind power and wind friction losses under different working conditions can be analyzed.

[0101] If a machine learning model is used for anomaly event detection, statistical methods, distance-based methods, ensemble methods, etc. can be adopted to implement the anomaly analysis model. Ensemble methods such as Isolation Forest identify outliers by building multiple decision trees. Machine learning models can also use neural network methods, Bayesian networks, and support vector machines (SVM), etc.

[0102] In the embodiment of the present invention, the repetitive anomaly features included in the preprocessed data are input into the anomaly analysis model. Through the anomaly analysis model, the anomaly events that cause the repetitive anomaly features can be determined. According to the anomaly events, the key influencing factors and interference sources in the motor fan design can be screened, improving the design efficiency of the motor fan.

[0103] In a possible implementation manner, when calibrating the data to be calibrated with the fan loss simulation data, the Kalman gain can be calculated according to the fan loss simulation data and the data to be calibrated, and then the fan loss simulation data and the data to be calibrated are weighted according to the Kalman gain to obtain the calibrated data.

[0104] After obtaining the data to be calibrated, the fan loss simulation data can be integrated, the data to be calibrated can be calibrated using a filter, and then the correlation between the torque data and other variables can be determined. For example, the relationships between torque and parameters such as fan structure, motor speed, and temperature can be analyzed to determine the influence of different factors on torque.

[0105] Calibrating the data to be calibrated can be achieved through a pre-determined model, which is based on the Kalman filtering method. This model can predict torque changes based on the existing torque data and can also determine in advance the possible problems of the motor for maintenance and repair.

[0106] Kalman filtering is a recursive Bayesian estimation method used to estimate the unknown system state from an observation sequence containing noise, for environments with uncertainty, measurement errors, or incomplete information. In the case of comparing simulation results with test data, Kalman filtering can be used to integrate these two types of data to obtain a more accurate and reliable system state estimate. Commonly used evaluation metrics include root mean square error (RMSE), mean absolute error (MAE), etc.

[0107] In the process of calibrating the data to be calibrated through Kalman filtering, the data to be calibrated and the fan loss simulation data serve as two different observation sources and are integrated through a Kalman filter. The filter determines the respective weights according to the reliability of the data to be calibrated and the fan loss simulation data and their correlation with the system state, so as to obtain the optimal state estimate value. For example, for process noise, especially when there are time-varying characteristics and unknown characteristics, an adaptive noise enhanced Kalman filter (A-AKF) can be used to estimate the optimal process noise covariance matrix, so as to adapt to the changes in the system state and environmental conditions. This adaptive ability helps the filter better adapt to the fluctuations of measurement noise, timely adjust the weight distribution between model prediction and data correction, and thus reduce the adverse impact of noise on state estimation.

[0108] In the embodiments of the present invention, by calculating the Kalman gain, intelligent weighting can be performed on the data to be calibrated and the fan loss simulation data, and the gain value represents the credibility of the data to be calibrated and the fan loss simulation data at a specific moment. When the measurement noise is large, the gain value is small, and the filter will rely more on the fan loss simulation data. When the measurement noise is small, the gain value is large, and the filter will rely more on the data to be calibrated for state update. Based on the Kalman gain, by dynamically adjusting the weights, the filter can adapt to measurement data with different noise levels, reduce the negative impact of noise on state estimation, and ensure that the calibrated data obtained can accurately reflect the wind friction loss.

[0109] In a possible implementation manner, Figure 5 shows a flowchart of a Kalman gain calculation method 500 according to an embodiment of the present invention, as Figure 5 shown, the Kalman gain calculation method 500 includes the following steps:

[0110] Step 501, according to the fan loss simulation data, calculate the predicted value of the fan loss torque at the k-th moment through formula (2).

[0111]

[0112] In the above formula (2), is used to represent the predicted value of the fan loss torque at the k-th moment, is used to represent the optimal estimate of the fan loss torque at the (k - 1)-th moment, u [k-1] is used to represent the control input quantity at the (k - 1)-th moment during the detection of the first torque data and the second torque data, A is used to represent the preset state transition matrix, and B is used to represent the preset control matrix.

[0113] The fan loss torque refers to the change in the output torque of the motor under test due to the loading of the fan.

[0114] Step 502: Calculate the prior error covariance matrix of the true value and the predicted value of the fan loss torque through formula (3).

[0115]

[0116] In the above formula (3), P, which is used to represent the prior error covariance matrix of the true value and the predicted value of the fan loss torque at time k. [k-1] Q, which is used to represent the posterior error covariance matrix of the true value and the optimal estimate of the fan loss torque at time k-1 determined based on the data to be calibrated, and Q is used to represent the covariance of the process noise determined based on the data to be calibrated.

[0117] Step 503: Calculate the Kalman gain at time k through formula (4).

[0118]

[0119] In the above formula (4), K [k] K, which is used to represent the Kalman gain at time k, H is used to represent the observation transfer matrix determined based on the data to be calibrated, and R is used to represent the covariance of the measurement noise determined based on the data to be calibrated.

[0120] In the embodiments of the present invention, the predicted value of the fan loss torque at time k is calculated through formula (2). Based on the calculation result of formula (2), the obvious error covariance matrix of the true value and the predicted value of the fan loss torque is calculated through formula (3). Based on the calculation result of formula (3), the Kalman gain is calculated through formula (4). Furthermore, the calibrated data can be determined according to the Kalman gain, making full use of the advantages of the fan loss simulation data and the true reflection of the data to be calibrated, which can improve the accuracy and robustness of the determined calibrated data, and further ensure the accuracy of the measured wind friction loss.

[0121] In a possible implementation manner, after determining the Kalman gain, the fan loss simulation data and the data to be calibrated can be weighted through the following formula (5) to obtain the optimal estimate of the fan loss torque at time k, and then the optimal estimates of the fan loss torque at each time are determined as the calibrated data.

[0122]

[0123] In the above formula (5), z, which is used to represent the optimal estimate of the fan loss torque at time k. [k] z, which is used to represent the true value of the fan loss torque corresponding to time k in the data to be calibrated.

[0124] In an embodiment of the present invention, intelligent weighting is performed on the fan loss simulation data and the data to be calibrated based on the Kalman gain. By dynamically adjusting the weights, the filter can adapt to measurement data with different noise levels, which can reduce the negative impact of noise on state estimation and ensure that the calibrated data obtained can accurately reflect the windage and friction loss.

[0125] In a possible implementation manner, after obtaining the Kalman gain at the k-th moment, the true value of the fan loss torque and the posterior error covariance matrix of the optimal estimate at the k-th moment can be calculated by the following formula (6):

[0126]

[0127] In the above formula (6), P [k] is used to represent the posterior error covariance matrix of the true value of the fan loss torque and the optimal estimate at the k-th moment, and I is used to represent the identity matrix.

[0128] In an embodiment of the present invention, since the prior error covariance matrix of the true value and the predicted value of the fan loss torque at the subsequent moment needs to be determined based on the true value of the fan loss torque and the posterior error covariance matrix of the optimal estimate at the previous moment, after determining the Kalman gain at the k-th moment, the true value of the fan loss torque and the posterior error covariance matrix of the optimal estimate at the k-th moment can be calculated by the above formula (6). Furthermore, the prior error covariance matrix of the true value and the predicted value of the fan loss torque at the (k + 1)-th moment can be calculated, so that the Kalman gain at each moment can be calculated in sequence, ensuring the calibration of each data point included in the data to be calibrated and ensuring the accuracy of the calibrated data.

[0129] In a possible implementation manner, after obtaining the calibrated data, the windage and friction loss can be calculated by the following formula (7).

[0130]

[0131] In the above formula (7), P fw is used to represent the windage and friction loss, T fw is used to represent the calibrated data, f p is used to represent the power frequency of the motor under test, P m is used to represent the number of pole pairs of the motor under test, and d is used to represent a constant.

[0132] Figure 6 is a schematic diagram of a motor windage and friction loss measurement device 600 according to an embodiment of the present invention. As Figure 6 shown, the motor windage and friction loss measurement device 600 includes:

[0133] The detection unit 601 is configured to detect a first torque data and a second torque data of the motor under test, wherein the first torque data is used to indicate the output torque of the motor under test when it is no-load, and the second torque data is used to indicate the output torque of the motor under test when a fan is loaded;

[0134] The calculation unit 602 is configured to determine fan loss measurement data according to the first torque data and the second torque data;

[0135] The simulation unit 603 is configured to perform simulation analysis on the motor under test to obtain fan loss simulation data;

[0136] The calibration unit 604 is configured to calibrate the fan loss measurement data according to the fan loss simulation data to obtain calibrated data;

[0137] The mapping unit 605 is configured to determine the windage and friction loss of the motor under test according to the calibrated data.

[0138] In the embodiment of the present invention, after the detection unit 601 detects the first torque data of the motor under test when it is no-load and the second torque data when a fan is loaded, the calculation unit 602 determines the fan loss measurement data according to the first torque data and the second torque data. The fan loss measurement data can indicate the torque change caused by the fan. Therefore, the fan loss measurement data is linearly related to the windage and friction loss of the motor under test. After the simulation unit 603 obtains the fan loss simulation data, the calibration unit 604 calibrates the fan loss measurement data through the fan loss simulation data to obtain the calibrated data. The calibrated data is linearly related to the windage and friction loss of the motor under test. Furthermore, the mapping unit 605 can determine the windage and friction loss of the motor under test according to the calibrated data. By measuring the fan loss measurement data caused by the windage and friction loss and calibrating the fan loss measurement data based on the fan loss simulation data, the noise and constant data included in the fan loss measurement data are reduced. Furthermore, the windage and friction loss of the motor under test is determined based on the calibrated data obtained by calibrating the fan loss measurement data. Since no parameters with uncertain errors are introduced during the measurement process of the windage and friction loss, the accuracy of the obtained windage and friction loss can be improved.

[0139] It should be noted that the interaction and other contents among the various parts in the above-mentioned motor windage and friction loss measurement device 600 are based on the same concept as those in the foregoing embodiment of the motor windage and friction loss measurement method. For the specific content and beneficial effects, reference can be made to the description in the foregoing embodiment of the motor windage and friction loss measurement method, which will not be elaborated here.

[0140] Figure 7 It is a schematic diagram of an electronic device provided by an embodiment of the present invention. The specific implementation of the electronic device is not limited in the specific embodiment of the present invention. Refer to Figure 7, the electronic device 700 provided by the embodiment of the present invention includes: a processor 702, a communication interface 704, a memory 706, and a communication bus 708. Among them:

[0141] The processor 702, the communication interface 704, and the memory 706 communicate with each other through the communication bus 708.

[0142] The communication interface 704 is used to communicate with other electronic devices or servers.

[0143] The processor 702 is used to execute the program 710, and specifically can execute the relevant steps in the above-mentioned embodiments of the motor wind and friction loss measurement method.

[0144] Specifically, the program 710 may include program code, and the program code includes computer operation instructions.

[0145] The processor 702 may be a central processing unit CPU, or a specific integrated circuit ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of the present invention. One or more processors included in the intelligent device may be of the same type of processor, such as one or more CPUs; or may be of different types of processors, such as one or more CPUs and one or more ASICs.

[0146] The memory 706 is used to store the program 710. The memory 706 may include a high-speed RAM memory, and may also include a non-volatile memory, such as at least one disk memory.

[0147] The program 710 is specifically used to cause the processor 702 to execute the motor wind and friction loss measurement method in any of the foregoing embodiments.

[0148] For the specific implementation of each step in the program 710, reference may be made to the corresponding steps and descriptions in the corresponding units in the above-mentioned embodiments of the motor wind and friction loss measurement method, which will not be elaborated here. Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working processes of the above-described devices and modules can refer to the corresponding process descriptions in the foregoing method embodiments, which will not be elaborated here.

[0149] Through the electronic device of this embodiment, after detecting the first torque data of the motor under test when it is no-load and the second torque data when a fan is loaded, the fan loss measurement data is determined based on the first torque data and the second torque data. The fan loss measurement data can indicate the torque change caused by the fan. Therefore, the fan loss measurement data is linearly related to the windage and friction loss of the motor under test. The calibrated data is obtained by calibrating the fan loss measurement data with the fan loss simulation data. The calibrated data is linearly related to the windage and friction loss of the motor under test. So, the windage and friction loss of the motor under test can be determined based on the calibrated data. By measuring the fan loss measurement data caused by the windage and friction loss and calibrating the fan loss measurement data based on the fan loss simulation data, the noise and constant data included in the fan loss measurement data are reduced. Furthermore, based on the calibrated data obtained by calibrating the fan loss measurement data, the windage and friction loss of the motor under test is determined. Since no parameters with uncertain errors are introduced during the measurement process of the windage and friction loss, the accuracy of the obtained windage and friction loss can be improved.

[0150] The present invention also provides a computer-readable storage medium storing instructions for causing a machine to execute the method for measuring the windage and friction loss of a motor as described herein. Specifically, a system or device equipped with the storage medium can be provided. On this storage medium, software program codes for implementing the functions of any one of the above embodiments are stored, and the computer (or CPU or MPU) of the system or device reads and executes the program codes stored in the storage medium.

[0151] In this case, the program code read from the storage medium itself can implement the functions of any one of the above embodiments. Therefore, the program code and the storage medium storing the program code constitute a part of the present invention.

[0152] Examples of the storage medium for providing the program code include floppy disks, hard disks, magneto-optical disks, optical disks (such as CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RAM, DVD-RW, DVD+RW), magnetic tapes, non-volatile memory cards, and ROMs. Optionally, the program code can be downloaded from a server computer via a communication network.

[0153] In addition, it should be clear that not only can the actual operations be completed in part or in whole by executing the program code read by the computer, but also by the operating system or the like operating on the computer based on the instructions of the program code, so as to implement the functions of any one of the above embodiments.

[0154] In addition, it can be understood that the program code read from the storage medium is written into the memory provided in the expansion board inserted into the computer or into the memory provided in the expansion module connected to the computer, and then based on the instructions of the program code, the CPU etc. installed on the expansion board or the expansion module is / are made to execute part or all of the actual operations, thereby implementing the functions of any of the above embodiments.

[0155] An embodiment of the present invention also provides a computer program product, which is tangibly stored on a computer-readable medium and includes computer-executable instructions. When the computer-executable instructions are executed, at least one processor is made to execute the design method of the motor heat dissipation ribs provided in the above embodiments. It should be understood that the various solutions in this embodiment have the corresponding technical effects in the above method embodiments, and will not be elaborated here.

[0156] It should be noted that not all steps and modules in the above-mentioned various processes and system structure diagrams are necessary, and some steps or modules can be ignored according to actual needs. The execution order of the steps is not fixed and can be adjusted according to needs. The system structure described in the above embodiments can be a physical structure or a logical structure, that is, some modules may be implemented by the same physical entity, or some modules may be implemented separately by multiple physical entities, or some components in multiple independent devices can be jointly implemented.

[0157] In this patent application, nouns and pronouns related to people are not limited to specific genders.

[0158] In the above embodiments, the hardware modules can be implemented mechanically or electrically. For example, a hardware module can include a permanent dedicated circuit or logic (such as a dedicated processor, FPGA or ASIC) to complete the corresponding operations. The hardware module can also include programmable logic or circuits (such as a general-purpose processor or other programmable processors), which can be temporarily set by software to complete the corresponding operations. The specific implementation method (mechanical method, or dedicated permanent circuit, or temporarily set circuit) can be determined based on cost and time considerations.

[0159] The present invention has been described in detail above through the drawings and preferred embodiments. However, the present invention is not limited to these disclosed embodiments. Based on the above-mentioned multiple embodiments, those skilled in the art can know that more embodiments of the present invention can be obtained by combining the code review means in the above different embodiments, and these embodiments are also within the protection scope of the present invention.

Claims

1. A method for measuring the wind friction loss of a motor (100), characterized in that, Including: Detecting first torque data and second torque data of the motor under test (204), wherein the first torque data is used to indicate the output torque of the motor under test (204) when it is unloaded, and the second torque data is used to indicate the output torque of the motor under test (204) when a fan is loaded; Determining fan loss measurement data according to the first torque data and the second torque data; Performing simulation analysis on the motor under test (204) to obtain fan loss simulation data; Calibrating the fan loss measurement data according to the fan loss simulation data to obtain calibrated data; Determining the windage and friction loss of the motor under test (204) according to the calibrated data.

2. The method according to claim 1, wherein The detecting the first torque data and the second torque data of the motor under test (204) includes: Fixing the motor under test (204) on the first support platform (201); Adjusting the height of the auxiliary centering platform (203), wherein a V-block (208) is arranged on the auxiliary centering platform (203); Based on the V-block (208), adjusting the spatial positions of the first support platform (201) and the second support platform (202) to make the output shaft of the motor under test (204) coincide with the axis of the torque sensor (206), wherein a driving motor (205) and the torque sensor (206) are fixed on the second support platform (202), and the output shaft of the driving motor (205) is connected to the torque sensor (206); After lowering the auxiliary centering platform (203) to separate the output shaft of the motor under test (204) from the V-block (208), connecting the output shaft of the motor under test (204) to the torque sensor (206) through a second coupling (209); Controlling the driving motor (205) to drive the motor under test (204) to rotate through the torque sensor (206); Obtaining the torque data detected by the torque sensor (206), wherein the first torque data is obtained when the motor under test (204) is unloaded, and the second torque data is obtained when the motor under test (204) is loaded with a fan.

3. The method according to claim 1, wherein The determining the fan loss measurement data according to the first torque data and the second torque data includes: Calculating the difference between the data points corresponding to the same rotational speed in the first torque data and the second torque data, and determining the absolute value of the calculated difference as the fan loss measurement data.

4. The method according to claim 1, wherein The calibrating the fan loss measurement data according to the fan loss simulation data to obtain calibrated data includes: Performing preprocessing on the fan loss measurement data to obtain preprocessed data, wherein the preprocessing includes at least one of deleting duplicate data, supplementing missing data, deleting discrete outliers, deleting noise data, and normalizing the data; Analyzing the preprocessed data to obtain a feature analysis result; If the feature analysis result indicates that the preprocessed data does not include repetitive abnormal features, determining the preprocessed data as the data to be calibrated; If the feature analysis result indicates that the preprocessed data includes repetitive abnormal features, after removing the abnormal feature data included in the preprocessed data, calibration data to be calibrated is obtained; The calibration data to be calibrated is calibrated according to the fan loss simulation data to obtain calibrated data.

5. The method according to claim 4, wherein Removing the abnormal feature data in the preprocessed data to obtain the calibration data to be calibrated includes: Inputting the repetitive abnormal features included in the preprocessed data into a data filtering model, and removing the abnormal feature data included in the preprocessed data through the data filtering model to obtain the calibration data to be calibrated.

6. The method according to claim 5, wherein The method further includes: Inputting the repetitive abnormal features into an anomaly analysis model, and performing anomaly analysis on the motor under test through the anomaly analysis model to determine the abnormal event that causes the repetitive abnormal features.

7. The method according to any one of claims 4 to 6, characterized in that, Calibrating the calibration data to be calibrated according to the fan loss simulation data to obtain calibrated data includes: Calculating a Kalman gain according to the fan loss simulation data and the calibration data to be calibrated; Weighting the fan loss simulation data and the calibration data to be calibrated according to the Kalman gain to obtain the calibrated data.

8. The method according to claim 7, wherein Calculating the Kalman gain according to the fan loss simulation data and the calibration data to be calibrated includes: According to the fan loss simulation data, calculating a predicted value of the fan loss torque at the k-th moment through the following formula: Used to represent the predicted value of the fan loss torque at the k-th moment, Used to represent the optimal estimate of the fan loss torque at the (k - 1)-th moment, u [k-1] Used to represent the control input quantity at the (k - 1)-th moment during the process of detecting the first torque data and the second torque data, A is used to represent a preset state transition matrix, and B is used to represent a preset control matrix; Calculating a prior error covariance matrix of the true value and the predicted value of the fan loss torque through the following formula: The prior error covariance matrix, P, for characterizing the true value and predicted value of the fan loss torque at time k [k-1] The posterior error covariance matrix, Q, for characterizing the true value and optimal estimate of the fan loss torque at time k-1 determined based on the data to be calibrated; the covariance of the process noise characterized by Q is determined based on the data to be calibrated Calculating the Kalman gain at the k-th moment through the following formula: K [k] K is used to represent the Kalman gain at the k-th moment, H is used to represent the observation transition matrix determined based on the data to be calibrated, and R is used to represent the covariance of the measurement noise determined based on the data to be calibrated.

9. The method according to claim 8, wherein Weighting the fan loss simulation data and the calibration data to be calibrated according to the Kalman gain to obtain the calibrated data includes: Calculating an optimal estimate of the fan loss torque at the k-th moment through the following formula: For characterizing the optimal estimate of the fan loss torque at the k-th moment, z [k] For characterizing the true value of the fan loss torque corresponding to the k-th moment in the data to be calibrated; Determining the optimal estimates of the fan loss torque at each moment as the calibrated data.

10. The method according to claim 8, wherein The method further includes: After obtaining the Kalman gain at the k-th moment, calculating a posterior error covariance matrix of the true value and the optimal estimate of the fan loss torque at the k-th moment through the following formula: P [k] which is used to represent the true value of the fan loss torque at the k-th moment and the posterior error covariance matrix of the optimal estimate, and I is used to represent the identity matrix.

11. A motor windage and friction loss measuring device (600), characterized in that, Includes: A detection unit (601) for detecting first torque data and second torque data of the motor under test (204), wherein the first torque data is used to indicate the output torque of the motor under test (204) when it is unloaded, and the second torque data is used to indicate the output torque of the motor under test (204) when it is loaded with a fan; A calculation unit (602) for determining fan loss measurement data according to the first torque data and the second torque data; A simulation unit (603) for performing simulation analysis on the motor under test (204) to obtain fan loss simulation data; A calibration unit (604) for calibrating the fan loss measurement data according to the fan loss simulation data to obtain calibrated data; A mapping unit (605) for determining the windage and friction loss of the motor under test (204) according to the calibrated data.

12. An electronic device (700), characterized in that, Includes: A processor (702), a communication interface (704), a memory (706), and a communication bus (708), wherein the processor (702), the memory (706), and the communication interface (704) communicate with each other through the communication bus (708); The memory (706) is configured to store at least one executable instruction, and the executable instruction causes the processor (702) to perform operations corresponding to the motor fan wind friction loss measurement method (100) described in any one of claims 1-10.

13. A computer-readable storage medium, characterized in that, Computer instructions are stored on the computer-readable storage medium, and when the computer instructions are executed by a processor, the processor is caused to execute the method described in any one of claims 1-10.

14. A computer program product, characterized in that, The computer program product is tangibly stored on a computer-readable medium and includes computer-executable instructions that, when executed, cause at least one processor to execute the method according to any one of claims 1-10.

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