A method and system for controlling motor speed

Abstract

The application discloses a motor speed control method and system, and relates to the technical field of motors, and the technical scheme points of the application comprise the following steps: acquiring real-time load fluctuation values, stator winding temperature values, bearing vibration frequency values and insulation resistance variation values which affect speed stability during target motor operation; processing and analyzing motor historical operation and maintenance data to obtain a first correlation coefficient, a second correlation coefficient, a third correlation coefficient and a fourth correlation coefficient; processing the real-time load fluctuation values and the first correlation coefficient to obtain a speed deviation value; processing the stator winding temperature values and the second correlation coefficient to obtain an insulation aging risk value; and the effect is to improve the adaptability and stability of motor speed control.

Description

Technical Field

[0001] This invention relates to the field of motor technology, and more specifically, to a motor speed control method and system. Background Technology

[0002] In the field of motor control, traditional motor speed control methods often adjust the motor's output speed based on load changes, neglecting the impact of the motor's own health status and complex operating environment factors on speed control. Traditional methods fail to fully consider the synergistic relationship between motor health and speed control, easily leading to insufficient speed control and accelerated deterioration of motor health. Electromagnetic interference can cause abnormal changes in the motor's electrical parameters, but traditional methods, failing to fully consider the synergistic relationship between motor health and speed control, cannot perceive the impact of interference on motor parameters in electromagnetic interference environments. Therefore, speed regulation will deviate significantly, affecting the processing accuracy of products on the production line. Furthermore, the frequent occurrence of unstable operating states by the motor increases motor wear and tear. Summary of the Invention

[0003] In view of the shortcomings of the existing technology, the purpose of this invention is to provide a motor speed control method and system.

[0004] To achieve the above objectives, the present invention provides the following technical solution:

[0005] A method for controlling motor speed, the method comprising the following steps:

[0006] Acquire real-time load fluctuation values, stator winding temperature values, bearing vibration frequency values, and insulation resistance variation values ​​that affect speed stability during the operation of the target motor;

[0007] The first correlation coefficient, the second correlation coefficient, the third correlation coefficient, and the fourth correlation coefficient were obtained by processing and analyzing the historical operation and maintenance data of the motor.

[0008] The speed deviation value is obtained by processing the real-time load fluctuation value and the first correlation coefficient; the insulation aging risk value is obtained by processing the stator winding temperature value and the second correlation coefficient.

[0009] If no sudden electromagnetic interference is detected in the operating environment of the target motor, the initial speed control parameters of the target motor are dynamically adjusted by combining the speed deviation value, insulation aging risk value, third correlation coefficient, fourth correlation coefficient, bearing vibration frequency value and insulation resistance variation value to obtain the first collaborative control parameter group;

[0010] If a sudden electromagnetic interference is detected in the operating environment of the target motor, the electromagnetic interference data, the second correction correlation coefficient, the third correction correlation coefficient, the speed deviation value, the insulation aging risk value, the bearing vibration frequency value, and the insulation resistance variation value are processed and analyzed to obtain the second collaborative control parameter group;

[0011] The target motor adjustment command is generated based on the first or second collaborative control parameter group.

[0012] Preferably, the historical operation and maintenance data of the motor are processed and analyzed to obtain the first correlation coefficient, the second correlation coefficient, the third correlation coefficient, and the fourth correlation coefficient, specifically including the following steps:

[0013] Extract historical load fluctuation values ​​and corresponding historical speed deviation values ​​for different load fluctuation conditions from historical motor operation and maintenance data, and calculate the ratio of historical speed deviation value to historical load fluctuation value to obtain the first correlation coefficient.

[0014] Extract historical temperature values ​​of different stator winding temperatures and corresponding insulation aging time values ​​from historical motor operation and maintenance data, and calculate the ratio of insulation aging time value to historical temperature value to obtain the second correlation coefficient.

[0015] The historical vibration frequency values ​​of different bearing vibration frequencies and the corresponding mechanical wear are extracted from the historical operation and maintenance data of the motor. The ratio of mechanical wear to historical vibration frequency values ​​is calculated to obtain the third correlation coefficient.

[0016] The historical resistance value and corresponding health decay time value corresponding to different insulation resistances are extracted from the historical operation and maintenance data of the motor, and the ratio of the health decay time value to the historical resistance value is calculated to obtain the fourth correlation coefficient.

[0017] Preferably, the electromagnetic interference data, the second corrected correlation coefficient, the third corrected correlation coefficient, the speed deviation value, the insulation aging risk value, the bearing vibration frequency value, and the insulation resistance variation value are processed and analyzed to obtain the second collaborative control parameter set, specifically including the following steps:

[0018] The electromagnetic interference data includes electromagnetic interference intensity and interference frequency;

[0019] The bearing vibration frequency offset and stator winding temperature increment of the target motor are obtained based on the electromagnetic interference intensity, interference frequency, and load fluctuation exceedance.

[0020] The second corrected correlation coefficient is obtained by correcting the second correlation coefficient using the bearing vibration frequency offset;

[0021] The third corrected correlation coefficient is obtained by correcting the third correlation coefficient using the stator winding temperature increment;

[0022] The second modified correlation coefficient, the third modified correlation coefficient, the speed deviation value, the insulation aging risk value, the bearing vibration frequency value, and the insulation resistance variation value are input into the parameter adjustment model to modify the initial speed control parameters of the target motor and obtain the second collaborative control parameter group.

[0023] Preferably, acquiring real-time load fluctuation values, stator winding temperature values, bearing vibration frequency values, and insulation resistance variation values ​​that affect the speed stability during the operation of the target motor specifically includes the following steps:

[0024] The real-time load fluctuation value of the target motor during operation is detected by a torque sensor;

[0025] The real-time temperature value of the stator winding of the target motor is detected by a temperature sensor, and the stator winding temperature value that exceeds the safe temperature threshold of the stator winding is filtered out from the real-time temperature value.

[0026] The bearing vibration frequency value is obtained by detecting the real-time vibration frequency of the target motor bearing using a vibration sensor.

[0027] The insulation resistance variation of the stator winding of the target motor is detected by an insulation resistance tester.

[0028] Preferably, if no sudden electromagnetic interference is detected in the operating environment of the target motor, the initial speed control parameters of the target motor are dynamically adjusted by combining the speed deviation value, insulation aging risk value, third correlation coefficient, fourth correlation coefficient, bearing vibration frequency value, and insulation resistance variation value to obtain the first collaborative control parameter group, which specifically includes the following steps:

[0029] The electromagnetic interference signal strength of the operating environment of the target motor is detected by an electromagnetic interference detector;

[0030] If the electromagnetic interference signal strength is less than or equal to the preset interference strength threshold, it is determined that there is no sudden electromagnetic interference in the operating environment of the target motor.

[0031] The mechanical wear risk value and health degradation risk value are obtained by processing the third correlation coefficient, the fourth correlation coefficient, the bearing vibration frequency value, and the insulation resistance variation value.

[0032] The speed deviation value, insulation aging risk value, mechanical wear risk value, and health degradation risk value are input into the parameter adjustment model to correct the initial speed control parameters of the target motor and obtain the first collaborative control parameter group.

[0033] Preferably, the mechanical wear risk value and health degradation risk value are obtained by processing the third correlation coefficient, the fourth correlation coefficient, the bearing vibration frequency value, and the insulation resistance variation value. Specifically, this includes the following steps:

[0034] The mechanical wear risk value is obtained by calculating the third correlation coefficient and the bearing vibration frequency value;

[0035] The health degradation risk value is obtained by calculating the fourth correlation coefficient and the insulation resistance variation value.

[0036] Preferably, the bearing vibration frequency offset and stator winding temperature increment of the target motor are obtained based on the electromagnetic interference intensity, interference frequency, and load fluctuation exceedance, specifically including the following steps:

[0037] The bearing vibration frequency offset is obtained by the correspondence between electromagnetic interference and vibration frequency offset.

[0038] The stator winding temperature increment is obtained based on the correspondence between the real-time load fluctuation exceedance and the stator winding temperature increment.

[0039] Preferably, generating a target motor adjustment command based on a first set of coordinated control parameters or a second set of coordinated control parameters specifically includes the following steps:

[0040] Generate corresponding motor speed adjustment commands and health protection commands based on the first collaborative control parameter group;

[0041] Based on the second set of collaborative control parameters, motor speed adjustment commands and health protection commands are generated to adapt to disturbance and load fluctuation conditions.

[0042] Preferably, the speed deviation value is obtained by processing the real-time load fluctuation value and the first correlation coefficient; the insulation aging risk value is obtained by processing the stator winding temperature value and the second correlation coefficient, specifically including the following steps:

[0043] The speed deviation value is obtained by multiplying the real-time load fluctuation value and the first correlation coefficient.

[0044] The insulation aging risk value is obtained by multiplying the stator winding temperature value and the second correlation coefficient.

[0045] A motor speed control system, comprising:

[0046] Acquisition module: Acquires real-time load fluctuation values, stator winding temperature values, bearing vibration frequency values, and insulation resistance variation values ​​that affect the speed stability of the target motor during operation;

[0047] Analysis module: Processes and analyzes historical motor operation and maintenance data to obtain the first correlation coefficient, second correlation coefficient, third correlation coefficient, and fourth correlation coefficient;

[0048] Processing module: Processes the real-time load fluctuation value and the first correlation coefficient to obtain the speed deviation value; processes the stator winding temperature value and the second correlation coefficient to obtain the insulation aging risk value;

[0049] First adjustment module: If no sudden electromagnetic interference is detected in the operating environment of the target motor, the initial speed control parameters of the target motor are dynamically adjusted by combining the speed deviation value, insulation aging risk value, third correlation coefficient, fourth correlation coefficient, bearing vibration frequency value and insulation resistance variation value to obtain the first collaborative control parameter group;

[0050] Second adjustment module: If sudden electromagnetic interference is detected in the operating environment of the target motor, the electromagnetic interference data, the second correction correlation coefficient, the third correction correlation coefficient, the speed deviation value, the insulation aging risk value, the bearing vibration frequency value, and the insulation resistance variation value are processed and analyzed to obtain the second collaborative control parameter group;

[0051] Generation module: Generates target motor adjustment commands based on the first or second collaborative control parameter group.

[0052] Compared with the prior art, the present invention has the following beneficial effects:

[0053] This invention achieves comprehensive real-time monitoring of key motor operating parameters by acquiring real-time load fluctuation values, stator winding temperature values, bearing vibration frequency values, and insulation resistance variation values ​​that affect speed stability during the operation of the target motor. This provides a solid data foundation for subsequent precise control. Processing and analyzing historical motor operation and maintenance data yields a first, second, third, and fourth correlation coefficient. By leveraging historical data, the inherent correlation patterns between motor operating parameters are uncovered, more accurately reflecting the actual operating status and potential risks of the motor. When no sudden electromagnetic interference is detected, the initial speed control parameters are dynamically adjusted based on multiple parameters to obtain a first set of coordinated control parameters. When sudden electromagnetic interference exists, a second set of coordinated control parameters is obtained by combining electromagnetic interference data and corrected correlation coefficients. This allows for adjustments to the control strategy according to different operating environments and interference conditions, maintaining the motor in a good operating state and effectively improving the adaptability and stability of motor speed control. Based on different sets of coordinated control parameters, target motor adjustment commands are generated to regulate the motor speed, ensuring both speed accuracy and motor health, reducing malfunctions caused by improper speed control or neglect of motor health, extending motor lifespan, and improving motor efficiency and reliability. Attached Figure Description

[0054] Figure 1 This is a schematic diagram illustrating the steps of a motor speed control method proposed in this invention;

[0055] Figure 2 This invention presents a schematic diagram of a motor speed control system. Detailed Implementation

[0056] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0057] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0058] Secondly, the term "an embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places throughout this specification does not necessarily refer to the same embodiment, nor is it a single embodiment or an embodiment selectively excluded from other embodiments.

[0059] Reference Figures 1-2 As shown.

[0060] The embodiments further illustrate the motor speed control method and system proposed in this invention.

[0061] A method for controlling motor speed, the method comprising the following steps:

[0062] Acquire real-time load fluctuation values, stator winding temperature values, bearing vibration frequency values, and insulation resistance variation values ​​that affect speed stability during the operation of the target motor;

[0063] The first correlation coefficient, the second correlation coefficient, the third correlation coefficient, and the fourth correlation coefficient were obtained by processing and analyzing the historical operation and maintenance data of the motor.

[0064] The speed deviation value is obtained by processing the real-time load fluctuation value and the first correlation coefficient; the insulation aging risk value is obtained by processing the stator winding temperature value and the second correlation coefficient.

[0065] If no sudden electromagnetic interference is detected in the operating environment of the target motor, the initial speed control parameters of the target motor are dynamically adjusted by combining the speed deviation value, insulation aging risk value, third correlation coefficient, fourth correlation coefficient, bearing vibration frequency value and insulation resistance variation value to obtain the first collaborative control parameter group;

[0066] If a sudden electromagnetic interference is detected in the operating environment of the target motor, the electromagnetic interference data, the second correction correlation coefficient, the third correction correlation coefficient, the speed deviation value, the insulation aging risk value, the bearing vibration frequency value, and the insulation resistance variation value are processed and analyzed to obtain the second collaborative control parameter group;

[0067] The target motor adjustment command is generated based on the first or second collaborative control parameter group.

[0068] The historical operation and maintenance data of the motor are processed and analyzed to obtain the first correlation coefficient, the second correlation coefficient, the third correlation coefficient, and the fourth correlation coefficient. The specific steps include:

[0069] Extract historical load fluctuation values ​​and corresponding historical speed deviation values ​​for different load fluctuation conditions from historical motor operation and maintenance data, and calculate the ratio of historical speed deviation value to historical load fluctuation value to obtain the first correlation coefficient.

[0070] Extract historical temperature values ​​of different stator winding temperatures and corresponding insulation aging time values ​​from historical motor operation and maintenance data, and calculate the ratio of insulation aging time value to historical temperature value to obtain the second correlation coefficient.

[0071] The historical vibration frequency values ​​of different bearing vibration frequencies and the corresponding mechanical wear are extracted from the historical operation and maintenance data of the motor. The ratio of mechanical wear to historical vibration frequency values ​​is calculated to obtain the third correlation coefficient.

[0072] The historical resistance value and corresponding health decay time value corresponding to different insulation resistances are extracted from the historical operation and maintenance data of the motor, and the ratio of the health decay time value to the historical resistance value is calculated to obtain the fourth correlation coefficient.

[0073] By collecting operation and maintenance records from the past five years, the quantitative relationships between key parameters were extracted. When the load fluctuation increased from 10% to 20%, the corresponding speed deviation increased from 3 r / min to 6 r / min. The first correlation coefficient was calculated to be 0.3, meaning that every 1% load fluctuation would result in a speed deviation of 0.3 r / min. When the stator winding temperature increased from 60℃ to 90℃, the insulation aging period shortened from 1200 days to 600 days. The second correlation coefficient was calculated to be 20, meaning that for every 1℃ increase in temperature, the insulation aging rate increased by 20 days / ℃. Similarly, the third correlation coefficient was calculated to be 0.02, and the fourth correlation coefficient was 5. This means that for every 10Hz increase in bearing vibration frequency, the mechanical wear increased by 0.2mm, and for every 100MΩ decrease in insulation resistance, the healthy decay period shortened by 500 hours.

[0074] The second collaborative control parameter set is obtained by processing and analyzing electromagnetic interference data, second and third corrected correlation coefficients, speed deviation values, insulation aging risk values, bearing vibration frequency values, and insulation resistance variation values. This process includes the following steps:

[0075] Electromagnetic interference data includes electromagnetic interference intensity and interference frequency;

[0076] The bearing vibration frequency offset and stator winding temperature increment of the target motor are obtained based on the electromagnetic interference intensity, interference frequency, and load fluctuation exceedance.

[0077] The second corrected correlation coefficient is obtained by correcting the second correlation coefficient using the bearing vibration frequency offset;

[0078] The third corrected correlation coefficient is obtained by correcting the third correlation coefficient using the stator winding temperature increment;

[0079] The second modified correlation coefficient, the third modified correlation coefficient, the speed deviation value, the insulation aging risk value, the bearing vibration frequency value, and the insulation resistance variation value are input into the parameter adjustment model to modify the initial speed control parameters of the target motor and obtain the second collaborative control parameter group.

[0080] Electromagnetic interference (EMI) data includes EMI intensity and interference frequency. When a motor is operating in an environment with EMI, the bearing vibration frequency offset and stator winding temperature increment of the target motor are determined based on the EMI intensity, interference frequency, and load fluctuation exceeding the normal range. Assuming the EMI intensity of the motor's environment is 150 dB, the interference frequency is 500 Hz, and the load fluctuation exceeds the normal range by 10%, historical data is used to summarize the pattern that for every 50 dB increase in EMI intensity and every 100 Hz increase in interference frequency, combined with the load fluctuation exceeding the normal range, the bearing vibration frequency offset increases by 3 Hz, and the stator winding temperature increment increases by 5 °C.

[0081] A second corrected correlation coefficient is obtained by correcting the second correlation coefficient using the bearing vibration frequency offset. The second correlation coefficient reflects the correlation between stator winding temperature and insulation aging risk. Bearing vibration frequency offset indirectly affects the operating state of the stator winding, so the second correlation coefficient needs correction. If the original second correlation coefficient is 0.8, and the bearing vibration frequency offset is 3Hz, according to the preset correction rule, each 1Hz offset increases the second correlation coefficient by 0.05, then the second corrected correlation coefficient is 0.8 + 3 × 0.05 = 0.95. Similarly, a third corrected correlation coefficient is obtained by correcting the third correlation coefficient using the stator winding temperature increment. The third correlation coefficient relates to the correlation between bearing vibration frequency and mechanical wear. Changes in stator winding temperature affect the bearing's operating environment, thus affecting the correlation between vibration and wear. Assuming the original third correlation coefficient is 0.6, and the stator winding temperature increment is 5℃, each 1℃ increment increases the third correlation coefficient by 0.03, then the third corrected correlation coefficient is 0.6 + 5 × 0.03 = 0.75.

[0082] A large amount of operating data of motors under different operating conditions was collected, including real-time load fluctuation values, stator winding temperature values, bearing vibration frequency values, insulation resistance variation values, electromagnetic interference data, as well as corresponding motor speed control parameters and actual motor operating performance data. This data was preprocessed to remove outliers and fill in missing values.

[0083] By mining the intrinsic correlations between various parameters in the data using neural networks, real-time load fluctuations, stator winding temperature, bearing vibration frequency, insulation resistance variation, electromagnetic interference data, and second and third corrected correlation coefficients are used as input layer variables, while motor speed control parameters are used as output layer variables. The neural network is trained using collected historical data. During training, the weights and biases of the neural network are continuously adjusted to ensure that the output conforms to the actual motor speed control parameters. The structure and parameters of the parameter adjustment model are constrained and optimized by combining the influence mechanism of electromagnetic interference on the motor windings and the relationship mechanism between load fluctuations and motor speed. This ensures that the model not only fits historical data but also conforms to the physical laws of motor operation. After multiple iterations of training and optimization, when the prediction error of the parameter adjustment model meets the preset accuracy requirements, a parameter adjustment model that can accurately output motor speed control parameters based on the input parameters is obtained.

[0084] The second and third correction correlation coefficients, speed deviation value, insulation aging risk value, bearing vibration frequency value, and insulation resistance variation value are input into the parameter adjustment model. The parameter adjustment model corrects the initial speed control parameters of the target motor. For example, if the initial speed control parameters are set to 1500 r / min, the parameter adjustment model will adjust the speed to 1450 r / min, ultimately obtaining the second set of coordinated control parameters to guide the stable operation of the motor under electromagnetic interference conditions.

[0085] To obtain real-time load fluctuation values, stator winding temperature values, bearing vibration frequency values, and insulation resistance variation values ​​that affect the speed stability of the target motor during operation, the specific steps include:

[0086] The real-time load fluctuation value of the target motor during operation is detected by a torque sensor;

[0087] The real-time temperature value of the stator winding of the target motor is detected by a temperature sensor, and the stator winding temperature value that exceeds the safe temperature threshold of the stator winding is filtered out from the real-time temperature value.

[0088] The bearing vibration frequency value is obtained by detecting the real-time vibration frequency of the target motor bearing using a vibration sensor.

[0089] The insulation resistance variation of the stator winding of the target motor is detected by an insulation resistance tester.

[0090] The torque sensor detects real-time load fluctuations during the operation of the target motor. When the load on the production equipment changes, the torque sensor captures these fluctuations in real time and measures the numerical value of the load fluctuation, for example, from... Change to These real-time data reflect the dynamic changes in motor load, and load fluctuations directly affect the speed stability of the motor.

[0091] A temperature sensor detects the real-time temperature of the stator windings of the target motor. The stator winding temperature is a crucial indicator of the motor's healthy operation, as excessively high temperatures accelerate the aging of the winding insulation and can even lead to motor failure. During the detection process, temperatures exceeding the safe temperature threshold for the stator windings are filtered from the real-time values. For example, if the safe temperature threshold for a motor's stator windings is set at 120℃, and the temperature sensor detects a real-time temperature of 130℃, then the stator winding temperature is considered to be 130℃.

[0092] A vibration sensor detects the real-time vibration frequency of the target motor bearing, thus obtaining the bearing vibration frequency value. The bearing is a critical rotating component of the motor, and its vibration reflects bearing wear and installation accuracy. For example, under normal circumstances, the bearing vibration frequency is around 50Hz. When the bearing experiences slight wear, the vibration frequency rises to 60Hz. By detecting the vibration frequency value in real time using a vibration sensor, the health condition of the bearing can be determined.

[0093] An insulation resistance tester is used to detect variations in the insulation resistance of the stator windings of a target motor. Insulation resistance is a crucial parameter for evaluating the insulation performance of stator windings; excessively low insulation resistance increases the risk of motor leakage faults. For example, the insulation resistance of a new motor's stator windings may be 500 MΩ, but as usage time and insulation aging occur, the insulation resistance could decrease to 200 MΩ. An insulation resistance tester can detect these changes in real time, providing critical data for assessing the motor's health.

[0094] If no sudden electromagnetic interference is detected in the operating environment of the target motor, the initial speed control parameters of the target motor are dynamically adjusted by combining the speed deviation value, insulation aging risk value, third correlation coefficient, fourth correlation coefficient, bearing vibration frequency value, and insulation resistance variation value to obtain the first collaborative control parameter group, which specifically includes the following steps:

[0095] The electromagnetic interference signal strength of the operating environment of the target motor is detected by an electromagnetic interference detector;

[0096] If the electromagnetic interference signal strength is less than or equal to the preset interference strength threshold, it is determined that there is no sudden electromagnetic interference in the operating environment of the target motor.

[0097] The mechanical wear risk value and health degradation risk value are obtained by processing the third correlation coefficient, the fourth correlation coefficient, the bearing vibration frequency value, and the insulation resistance variation value.

[0098] The speed deviation value, insulation aging risk value, mechanical wear risk value, and health degradation risk value are input into the parameter adjustment model to correct the initial speed control parameters of the target motor and obtain the first collaborative control parameter group.

[0099] The mechanical wear risk value and health degradation risk value are obtained by processing the third correlation coefficient, the fourth correlation coefficient, the bearing vibration frequency value, and the insulation resistance variation value. The specific steps include:

[0100] The mechanical wear risk value is obtained by calculating the third correlation coefficient and the bearing vibration frequency value;

[0101] The health degradation risk value is obtained by calculating the fourth correlation coefficient and the insulation resistance variation value.

[0102] This application utilizes an electromagnetic interference detector to detect the electromagnetic interference signal strength in the operating environment of a target motor. For example, various electrical devices exist around the motor, and the electromagnetic interference detector monitors the electromagnetic interference situation in the environment in real time. If the electromagnetic interference signal strength is less than or equal to a preset interference strength threshold, for example, if the preset threshold is 50dB and the actual detected value is 30dB, then it is determined that there is no sudden electromagnetic interference in the operating environment of the target motor.

[0103] The mechanical wear risk value and health degradation risk value are obtained by processing the third correlation coefficient, the fourth correlation coefficient, the bearing vibration frequency value, and the insulation resistance variation value. The third correlation coefficient reflects the relationship between the bearing vibration frequency and mechanical wear. Assuming the third correlation coefficient is 0.6, the fourth correlation coefficient reflects the relationship between the insulation resistance and health degradation. Assuming the fourth correlation coefficient is 0.8, the real-time detected bearing vibration frequency value is 100Hz, and the insulation resistance variation value is 200MΩ. Multiplying the third correlation coefficient by the bearing vibration frequency value yields a mechanical wear risk value of 60. Multiplying the fourth correlation coefficient by the insulation resistance variation value yields a health degradation risk value of 80. The insulation resistance variation value is the difference between the baseline value and the insulation resistance variation value. Here, the baseline value is assumed to be 300MΩ, i.e., 0.8×(300-200)=80.

[0104] Input the speed deviation value, insulation aging risk value, mechanical wear risk value, and health degradation risk value into the parameter adjustment model. For example, the speed deviation value is 5 r / min, and the insulation aging risk value is 40. The parameter adjustment model corrects the initial speed control parameters of the target motor. The initial speed control parameters include a speed setting of 1500 r / min and a torque limit of... After adjusting the parameters of the model, the engine speed was set to 1450 r / min, and the torque limit was adjusted to... This yields the first set of coordinated control parameters, which guides the stable operation of the motor in an environment free from sudden electromagnetic interference.

[0105] The bearing vibration frequency offset and stator winding temperature increment of the target motor are obtained based on the electromagnetic interference intensity, interference frequency, and load fluctuation amplitude. Specifically, the steps include:

[0106] The bearing vibration frequency offset is obtained by the correspondence between electromagnetic interference and vibration frequency offset.

[0107] The stator winding temperature increment is obtained based on the correspondence between the real-time load fluctuation exceedance and the stator winding temperature increment.

[0108] This application first utilizes the correlation between electromagnetic interference and vibration frequency shift to obtain the bearing vibration frequency shift. During long-term motor operation, a large amount of data regarding the relationship between electromagnetic interference intensity, interference frequency, and bearing vibration frequency shift will accumulate. For example, when the electromagnetic interference intensity is 100dB and the interference frequency is 500Hz, historical data analysis reveals that the bearing vibration frequency shifts by 10Hz relative to normal conditions. Therefore, in practical applications, after detecting the current electromagnetic interference intensity and frequency, the bearing vibration frequency shift is obtained based on a predetermined correlation.

[0109] The stator winding temperature increment is obtained by correlating the magnitude of real-time load fluctuations with the stator winding temperature increase. Historical operating data of the motor is used to summarize the relationship between load fluctuations exceeding the normal range and the increase in stator winding temperature. If the load fluctuation exceeds the normal range by 20%, the stator winding temperature increases by 15°C compared to normal operation. Therefore, after actually detecting a real-time load fluctuation exceeding the normal range, the stator winding temperature increment is obtained according to this correlation. This method is used to determine the bearing vibration frequency offset and the stator winding temperature increment.

[0110] The target motor adjustment command is generated based on the first or second collaborative control parameter group, specifically including the following steps:

[0111] Generate corresponding motor speed adjustment commands and health protection commands based on the first collaborative control parameter group;

[0112] Based on the second set of collaborative control parameters, motor speed adjustment commands and health protection commands are generated to adapt to disturbance and load fluctuation conditions.

[0113] When the motor is free from sudden electromagnetic interference, commands are generated based on the first set of coordinated control parameters. Assuming the motor speed should be stable at 1500 r / min, the stator winding temperature should be controlled below 80℃, and the bearing vibration frequency should not exceed 50 Hz, the generated motor speed adjustment command is to adjust the current speed to 1500 r / min and maintain it. The health protection command includes real-time monitoring of the stator winding temperature; if it approaches 80℃, cooling measures will be initiated. It also includes periodic monitoring of the bearing vibration frequency; if it exceeds 50 Hz, a maintenance warning will be issued.

[0114] When the motor experiences sudden electromagnetic interference, instructions are generated based on the second collaborative control parameter group. For example, considering an electromagnetic interference intensity of 100dB and a load fluctuation exceeding the limit by 30%, the second collaborative control parameter group determines that the motor speed should be adjusted to 1200r / min to reduce the impact of interference and load. The generated motor speed adjustment instruction is to reduce the speed to 1200r / min. Health protection instructions include increasing the stator winding temperature detection frequency, thereby enabling the motor to operate stably and healthily even under complex operating conditions of interference and load fluctuation.

[0115] The speed deviation value is obtained by processing the real-time load fluctuation value and the first correlation coefficient; the insulation aging risk value is obtained by processing the stator winding temperature value and the second correlation coefficient. Specifically, the following steps are included:

[0116] The speed deviation value is obtained by multiplying the real-time load fluctuation value and the first correlation coefficient.

[0117] The insulation aging risk value is obtained by multiplying the stator winding temperature value and the second correlation coefficient.

[0118] For example, statistical analysis of a large amount of historical data reveals that when the load fluctuation changes by 1 unit, the speed deviation changes by 0.5 units accordingly. Therefore, the first correlation coefficient is 0.5. In actual operation, if the sensor detects a real-time load fluctuation of 10 units, the real-time load fluctuation value is multiplied by the first correlation coefficient to obtain a speed deviation value of 5.

[0119] The second correlation coefficient is determined based on the historical correlation between stator winding temperature and insulation aging. For example, historical data shows that for every 1°C increase in stator winding temperature, the risk of insulation aging increases by 0.3 units, so the second correlation coefficient is 0.3. When the real-time stator winding temperature is detected to be 80°C, multiplying the stator winding temperature value by the second correlation coefficient yields an insulation aging risk value of 24 units. This value is used to assess the risk of motor insulation aging at the current stator winding temperature, providing a basis for subsequent motor health maintenance and speed control.

[0120] A motor speed control system, comprising:

[0121] Acquisition module: Acquires real-time load fluctuation values, stator winding temperature values, bearing vibration frequency values, and insulation resistance variation values ​​that affect the speed stability of the target motor during operation;

[0122] Analysis module: Processes and analyzes historical motor operation and maintenance data to obtain the first correlation coefficient, second correlation coefficient, third correlation coefficient, and fourth correlation coefficient;

[0123] Processing module: Processes the real-time load fluctuation value and the first correlation coefficient to obtain the speed deviation value; processes the stator winding temperature value and the second correlation coefficient to obtain the insulation aging risk value;

[0124] First adjustment module: If no sudden electromagnetic interference is detected in the operating environment of the target motor, the initial speed control parameters of the target motor are dynamically adjusted by combining the speed deviation value, insulation aging risk value, third correlation coefficient, fourth correlation coefficient, bearing vibration frequency value and insulation resistance variation value to obtain the first collaborative control parameter group;

[0125] Second adjustment module: If sudden electromagnetic interference is detected in the operating environment of the target motor, the electromagnetic interference data, the second correction correlation coefficient, the third correction correlation coefficient, the speed deviation value, the insulation aging risk value, the bearing vibration frequency value, and the insulation resistance variation value are processed and analyzed to obtain the second collaborative control parameter group;

[0126] Generation module: Generates target motor adjustment commands based on the first or second collaborative control parameter group.

[0127] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. 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. Those skilled in the art can understand and implement this without any creative effort.

[0128] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0129] 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; 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 spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method of motor speed control, characterized by, The method comprises the following steps: Obtaining real-time load fluctuation values, stator winding temperature values, bearing vibration frequency values and insulation resistance variation values affecting speed stability during operation of the target motor; Processing and analyzing historical operation and maintenance data of the motor to obtain first, second, third and fourth correlation coefficients, specifically comprising the following steps: Extracting historical load fluctuation values and corresponding historical speed deviation values under different load fluctuation conditions from the historical operation and maintenance data of the motor, and calculating the ratio of the historical speed deviation values to the historical load fluctuation values to obtain the first correlation coefficient; Extracting historical temperature values and corresponding insulation aging time values under different stator winding temperatures from the historical operation and maintenance data of the motor, and calculating the ratio of the insulation aging time values to the historical temperature values to obtain the second correlation coefficient; Extracting historical vibration frequency values and corresponding mechanical wear amounts under different bearing vibration frequencies from the historical operation and maintenance data of the motor, and calculating the ratio of the mechanical wear amounts to the historical vibration frequency values to obtain the third correlation coefficient; Extracting historical resistance values corresponding to different insulation resistances and corresponding health decay time values from the historical operation and maintenance data of the motor, and calculating the ratio of the health decay time values to the historical resistance values to obtain the fourth correlation coefficient; Processing the real-time load fluctuation values and the first correlation coefficient to obtain a speed deviation value, and processing the stator winding temperature values and the second correlation coefficient to obtain an insulation aging risk value; If it is detected that the target motor is in an operating environment without sudden electromagnetic interference, the initial speed control parameters of the target motor are dynamically adjusted in combination with the speed deviation value, the insulation aging risk value, the third correlation coefficient, the fourth correlation coefficient, the bearing vibration frequency value and the insulation resistance variation value to obtain a first cooperative control parameter group; If it is detected that the target motor is in an operating environment with sudden electromagnetic interference, the electromagnetic interference data, the second modified correlation coefficient, the third modified correlation coefficient, the speed deviation value, the insulation aging risk value, the bearing vibration frequency value and the insulation resistance variation value are processed and analyzed to obtain a second cooperative control parameter group, specifically comprising the following steps: The electromagnetic interference data includes electromagnetic interference intensity and interference frequency; According to the electromagnetic interference intensity, the interference frequency and the load fluctuation amplitude, the bearing vibration frequency offset of the target motor and the stator winding temperature increment are obtained; The second correlation coefficient is modified by the bearing vibration frequency offset to obtain the second modified correlation coefficient; The third correlation coefficient is modified by the stator winding temperature increment to obtain the third modified correlation coefficient; The second modified correlation coefficient, the third modified correlation coefficient, the speed deviation value, the insulation aging risk value, the bearing vibration frequency value and the insulation resistance variation value are input into a parameter adjustment model to modify the initial speed control parameters of the target motor to obtain the second cooperative control parameter group; Generating a target motor adjustment instruction according to the first cooperative control parameter group or the second cooperative control parameter group.

2. A method of motor speed control according to claim 1, wherein Obtaining real-time load fluctuation values, stator winding temperature values, bearing vibration frequency values and insulation resistance variation values affecting speed stability during operation of the target motor, specifically comprising the following steps: Detecting the real-time load fluctuation values of the target motor during operation by a torque sensor; Detecting a real-time temperature value of a target motor stator winding through a temperature sensor, and screening a stator winding temperature value exceeding a stator winding safety temperature threshold from the real-time temperature value; Detecting a bearing vibration frequency value of a target motor bearing through a vibration sensor; Detecting an insulation resistance variation value of a target motor stator winding through an insulation resistance tester.

3. A method of motor speed control according to claim 2, wherein If it is detected that no sudden electromagnetic interference exists in an operating environment of the target motor, the initial speed control parameter of the target motor is dynamically adjusted in combination with the speed deviation value, the insulation aging risk value, the third correlation coefficient, the fourth correlation coefficient, the bearing vibration frequency value and the insulation resistance variation value to obtain a first collaborative control parameter group, specifically including the following steps: Detecting an electromagnetic interference signal strength of an operating environment of the target motor through an electromagnetic interference detector; If the electromagnetic interference signal strength is less than or equal to a preset interference strength threshold, it is determined that no sudden electromagnetic interference exists in the operating environment of the target motor; Processing the third correlation coefficient, the fourth correlation coefficient, the bearing vibration frequency value and the insulation resistance variation value to obtain a mechanical wear risk value and a health attenuation risk value; Inputting the speed deviation value, the insulation aging risk value, the mechanical wear risk value and the health attenuation risk value into a parameter adjustment model to correct the initial speed control parameter of the target motor to obtain the first collaborative control parameter group.

4. A method of motor speed control according to claim 3, wherein Processing the third correlation coefficient, the fourth correlation coefficient, the bearing vibration frequency value and the insulation resistance variation value to obtain a mechanical wear risk value and a health attenuation risk value, specifically including the following steps: Calculating the third correlation coefficient and the bearing vibration frequency value to obtain the mechanical wear risk value; Calculating the fourth correlation coefficient and the insulation resistance variation value to obtain the health attenuation risk value.

5. A method of motor speed control according to claim 4, wherein According to the electromagnetic interference strength, the interference frequency and the load fluctuation exceeding amplitude, a bearing vibration frequency offset and a stator winding temperature increment of the target motor are obtained, specifically including the following steps: Obtaining the bearing vibration frequency offset through a corresponding relationship between the electromagnetic interference and the vibration frequency offset; Obtaining the stator winding temperature increment according to a corresponding relationship between the real-time load fluctuation exceeding amplitude and the stator winding temperature increment.

6. A method of motor speed control according to claim 5, wherein Generating a target motor adjustment instruction according to the first collaborative control parameter group or the second collaborative control parameter group, specifically including the following steps: Generating a corresponding motor speed adjustment instruction and a health protection instruction according to the first collaborative control parameter group; Generating a motor speed adjustment instruction and a health protection instruction adapted to the interference and load fluctuation working condition according to the second collaborative control parameter group.

7. A method of motor speed control according to claim 1, wherein Processing the real-time load fluctuation value and the first correlation coefficient to obtain a speed deviation value, and processing the stator winding temperature value and the second correlation coefficient to obtain an insulation aging risk value, specifically including the following steps: Multiplying the real-time load fluctuation value and the first correlation coefficient to obtain the speed deviation value; Multiplying the stator winding temperature value and the second correlation coefficient to obtain the insulation aging risk value.

8. A motor speed control system for use in a motor speed control method according to any one of claims 1 to 7, characterized by The method comprises the following steps: An acquisition module is configured to acquire a real-time load fluctuation value, a stator winding temperature value, a bearing vibration frequency value and an insulation resistance variation value of a target motor during operation, which affect speed stability; An electromagnetic interference detector is configured to detect an electromagnetic interference signal strength of an operating environment of the target motor; A parameter adjustment model is configured to dynamically adjust an initial speed control parameter of the target motor in combination with a speed deviation value, an insulation aging risk value, a third correlation coefficient, a fourth correlation coefficient, a bearing vibration frequency value and an insulation resistance variation value of the target motor to obtain a first collaborative control parameter group or a second collaborative control parameter group; A health protection instruction generator is configured to generate a corresponding motor speed adjustment instruction and a health protection instruction according to the first collaborative control parameter group or the second collaborative control parameter group; and A parameter adjustment model is configured to dynamically adjust an initial speed control parameter of the target motor in combination with a speed deviation value, an insulation aging risk value, a third correlation coefficient, a fourth correlation coefficient, a bearing vibration frequency value and an insulation resistance variation value of the target motor to obtain a first collaborative control parameter group or a second collaborative control parameter group. The analysis module: the motor historical operation and maintenance data are processed and analyzed to obtain a first correlation coefficient, a second correlation coefficient, a third correlation coefficient and a fourth correlation coefficient; The processing module: the real-time load fluctuation value and the first correlation coefficient are processed to obtain a speed deviation value; the stator winding temperature value and the second correlation coefficient are processed to obtain an insulation aging risk value; The first adjustment module: if it is detected that the running environment of the target motor does not exist sudden electromagnetic interference, the initial speed control parameter of the target motor is dynamically adjusted in combination with the speed deviation value, the insulation aging risk value, the third correlation coefficient, the fourth correlation coefficient, the bearing vibration frequency value and the insulation resistance variation value to obtain a first cooperative control parameter group; The second adjustment module: if it is detected that the running environment of the target motor exists sudden electromagnetic interference, the electromagnetic interference data, the second correction correlation coefficient, the third correction correlation coefficient, the speed deviation value, the insulation aging risk value, the bearing vibration frequency value and the insulation resistance variation value are processed and analyzed to obtain a second cooperative control parameter group; The generation module: the target motor adjustment instruction is generated according to the first cooperative control parameter group or the second cooperative control parameter group.

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