Motor vibration suppression system, device and method based on active magnetic field compensation

Abstract

The invention relates to a motor vibration suppression system, device and method based on active magnetic field compensation. The motor vibration suppression system comprises a vibration sensor which monitors the vibration condition of a motor and collects vibration data; the control device is used for generating a control signal according to the vibration data; the compensation module is used for generating a compensation electromagnetic field according to the control signal and generating reverse compensation force based on the compensation electromagnetic field for the motor; wherein the compensation module comprises a plurality of groups of electromagnetic coils and a driving unit electrically connected with the electromagnetic coils; and the driving unit adjusts the driving current on the electromagnetic coil according to the control signal so as to control the electromagnetic coil to generate a specified compensation electromagnetic field. The compensation electromagnetic field is actively generated according to the motor vibration data, the risk of motor abrasion is avoided, and a better damping effect and a wider application range are achieved.

Description

Technical Field

[0001] The present invention relates to the field of motor technology, and in particular to a motor vibration suppression system, device and method based on active magnetic field compensation. Background Art

[0002] Permanent magnet synchronous servo motor is a synchronous motor that uses permanent magnets to generate magnetic fields. It has a prominent vibration problem when running at high speed, which not only affects the motor control accuracy, but also has a negative impact on the service life and operation stability of the equipment. How to suppress motor vibration and thus improve motor control accuracy and stability is a technical problem that needs to be solved in the motor field.

[0003] Existing motor vibration suppression solutions mainly rely on mechanical shock absorbers or damping materials. These shock absorbers or damping materials generally need to directly contact the motor body, which has the risk of wearing out the motor. In addition, existing motor vibration suppression solutions can only passively reduce vibration, usually only deal with vibrations in a specific frequency range, and cannot be dynamically adjusted according to changes in motor operating conditions. Summary of the invention

[0004] Based on this, the purpose of the present invention is to provide a motor vibration suppression system, device and method based on active magnetic field compensation, which actively generates a compensating electromagnetic field according to the motor vibration data. It not only has no risk of wearing the motor, but also has better shock absorption effect and a wider range of application.

[0005] The present invention provides a motor vibration suppression system based on active magnetic field compensation, which comprises: a vibration sensor, which monitors the vibration of the motor and collects vibration data; a control device, which generates a control signal according to the vibration data; a compensation module, which generates a compensation electromagnetic field according to the control signal, and generates a reverse compensation force based on the compensation electromagnetic field on the motor; wherein the compensation module comprises a plurality of groups of electromagnetic coils and a drive unit electrically connected to the electromagnetic coils; the drive unit adjusts the drive current on the electromagnetic coil according to the control signal, thereby controlling the electromagnetic coil to generate a specified compensation electromagnetic field.

[0006] The present invention applies a reverse compensation force to suppress vibration to the motor through a compensation electromagnetic field, without direct contact with the motor body, without the risk of wearing the motor, and ensuring the service life of the motor; the present invention belongs to active shock absorption rather than passive shock absorption, and has a better vibration suppression effect than passive shock absorption; the present invention can be dynamically adjusted following the changes in the working conditions of the motor, and can cope with complex and changeable vibration conditions. Furthermore, the compensation electromagnetic field generated by the compensation module of the present invention is independent of the rotating magnetic field generated by the original stator winding of the motor, and can not only compensate for vibrations in directions other than the motor rotation direction, and has a larger scope of application, but can also operate independently without interfering with the motor drive, thereby improving system stability and reliability.

[0007] Furthermore, the control device includes: a machine learning model, a feedback unit and an automatic controller; the control device generates a control signal through the following process: the machine learning model generates an initial basic control signal based on historical vibration data; the feedback unit controls the compensation module to generate a compensating electromagnetic field based on the initial basic control signal, and simultaneously obtains current vibration data through the vibration sensor; the machine learning model updates the basic control signal based on historical vibration data and current vibration data; the automatic controller fine-tunes the basic control signal based on the current vibration data to obtain a control signal.

[0008] Furthermore, the control device also includes: a data update unit and a loop unit; after the automatic controller obtains the control signal, the data update unit merges the current vibration data at the previous moment with the historical vibration data to obtain new historical vibration data, and obtains new current vibration data through the vibration sensor; the loop unit repeatedly calls the machine learning model, the automatic controller and the data update unit, so as to continuously update the control signal according to the current vibration data.

[0009] Furthermore, the machine learning model is trained by historical vibration data, and when the historical vibration data is updated, the parameters of the machine learning model are also continuously updated.

[0010] Furthermore, the automatic controller specifically includes: a feature extraction unit, which is used to analyze the current vibration data and extract vibration characteristic values; a reverse compensation force calculation unit, which is used to calculate the corresponding reverse compensation force according to the vibration characteristic values; a compensation electromagnetic field calculation unit, which is used to calculate the corresponding compensation electromagnetic field according to the reverse compensation force; a fine-tuning amount generation unit, which is used to generate a corresponding control signal fine-tuning amount according to the compensation electromagnetic field; and a control signal output unit, which is used to superimpose the basic control signal and the control signal fine-tuning amount to obtain a fine-tuned control signal.

[0011] Furthermore, it also includes a magnetic field sensor for detecting an actual compensating electromagnetic field; the control device also includes: a magnetic field correction unit; the magnetic field correction unit, after the fine-tuning amount generating unit obtains the control signal fine-tuning amount, compares the difference between the actual compensating electromagnetic field and the target compensating electromagnetic field obtained by the compensating electromagnetic field calculating unit, and corrects the control signal fine-tuning amount.

[0012] Furthermore, the control device also includes a preprocessing unit; after acquiring the current vibration data, the preprocessing unit performs normalization and filtering and denoising preprocessing on the current vibration data, and then outputs the preprocessed current vibration data to the machine learning model and the automatic controller.

[0013] Furthermore, the machine learning model is an LSTM model.

[0014] Based on the same inventive concept, the present invention also provides a control method applied to the above-mentioned motor vibration suppression system, which includes the steps: S1, generating an initial basic control signal according to historical vibration data; S2, generating a compensating electromagnetic field according to the initial basic control signal, and simultaneously acquiring current vibration data; S3, updating the basic control signal according to historical vibration data and current vibration data; S4, fine-tuning the basic control signal according to the current vibration data to obtain a control signal.

[0015] Based on the same inventive concept, the present invention also provides a control device applied to the above-mentioned motor vibration suppression system, which includes: a machine learning model, a feedback unit and an automatic controller; the machine learning model generates an initial basic control signal according to historical vibration data; the feedback unit controls the compensation module to generate a compensating electromagnetic field according to the initial basic control signal, and obtains the current vibration data through the vibration sensor at the same time; the machine learning model updates the basic control signal according to the historical vibration data and the current vibration data; the automatic controller fine-tunes the basic control signal according to the current vibration data to obtain a control signal.

[0016] For better understanding and implementation, the present invention is described in detail below with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A schematic diagram of a module of a motor vibration suppression system based on active magnetic field compensation according to an embodiment of the present invention;

[0018] Figure 2 A schematic diagram of the installation positions of a vibration sensor and a compensation module according to an embodiment of the present invention;

[0019] Figure 3 A schematic diagram of a module of a control device according to an embodiment of the present invention;

[0020] Figure 4 The figure is a flow chart of a control method executed by a control device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0021] In view of the defects of existing mechanical shock absorbing devices or damping materials that can wear the motor and can only passively absorb shock, the present invention adds a compensation module for generating a compensation electromagnetic field at the motor bearing or rotor, and applies a reverse compensation force to the motor to suppress vibration by controlling the compensation electromagnetic field. The present invention does not need to directly contact the motor body, can actively compensate for vibrations in any direction and any mode, and has a good vibration suppression effect and a wide range of applications.

[0022] See also Figure 1 , Figure 1 The module schematic diagram of a motor vibration suppression system based on active magnetic field compensation of an embodiment of the present invention. The motor vibration suppression system based on active magnetic field compensation of the present invention comprises: a vibration sensor 1, a control device 2 and a compensation module 3. The vibration sensor 1 monitors the vibration of the motor and collects vibration data. The control device executes the motor vibration suppression control method of the present invention according to the vibration data and generates a control signal. The compensation module generates an adjustable compensation electromagnetic field according to the control signal, and generates a reverse compensation force based on the compensation electromagnetic field on the motor; the reverse compensation force is opposite to the vibration direction of the motor, thereby achieving the effect of suppressing the vibration of the motor.

[0023] See also Figure 2 , Figure 2 The figure is a schematic diagram of the installation positions of a vibration sensor and a compensation module according to an embodiment of the present invention.

[0024] Specifically, the vibration sensor 1 includes: an acceleration sensor, a displacement sensor, a speed sensor and / or a position sensor installed on the motor bearing or rotor. The vibration data includes: acceleration data, displacement data, speed data and / or position data. Preferably, there are multiple vibration sensors, and the multiple vibration sensors are respectively arranged at different measuring points on the motor bearing or rotor, so as to collect vibration data from multiple angles.

[0025] Specifically, the compensation module 3 includes: a plurality of groups of electromagnetic coils arranged on the motor bearing or the rotor, and a drive unit electrically connected to the electromagnetic coils. The drive unit adjusts the drive current output to the electromagnetic coils according to the control signal output by the control device, thereby controlling the electromagnetic coils to generate a specified compensation electromagnetic field.

[0026] See also Figure 3-4 , Figure 3 is a module schematic diagram of a control device according to an embodiment of the present invention, Figure 4The figure is a flow chart of a control method executed by a control device according to an embodiment of the present invention.

[0027] Specifically, the control device 2 includes: a machine learning model, a feedback unit, an automatic controller, a data updating unit and a loop unit.

[0028] The machine learning model executes step S1: generating an initial basic control signal based on historical vibration data. Step S1 can be completed before the motor runs. Once the motor starts running, the initial basic control signal can be immediately obtained, avoiding the overshoot problem caused by lack of data in the initial stage of motor operation, greatly shortening the trial and error time, and improving control efficiency.

[0029] The feedback unit executes step S2: controlling the compensation module 3 to generate a compensation electromagnetic field according to the initial basic control signal, and acquiring current vibration data through the vibration sensor 1 at the same time.

[0030] The machine learning model executes step S3: updating the basic control signal according to the historical vibration data and the current vibration data.

[0031] The automatic controller executes step S4: fine-tuning the basic control signal according to the current vibration data to obtain a control signal.

[0032] The data updating unit executes step S5: controlling the compensation module 3 to generate a compensation electromagnetic field according to the control signal; merging the current vibration data at the previous moment with the historical vibration data to obtain new historical vibration data; and obtaining new current vibration data through the vibration sensor 1.

[0033] The loop unit executes step S6: repeatedly calling the machine learning model, the automatic controller and the data updating unit to repeat steps S3-S5, thereby continuously updating the control signal according to the current vibration data until the motor stops running and the repetition of steps S3-S5 is ended.

[0034] The machine learning model is used to extract and map features of historical vibration data and current vibration data, generate basic control signals, and thus provide a basic reference for generating the final control signal. Compared with directly generating the final control signal, the present invention first obtains the macro basic control signal, and then fine-tunes the basic control signal, thereby combining macro-adjustment with micro-adjustment, which not only improves the accuracy of the final generated control signal, but also shortens the trial and error time, achieves a faster calculation speed, and has a high cost-effectiveness.

[0035] The machine learning model is trained by historical vibration data. When the data updating unit updates the historical vibration data, the parameters of the machine learning model are also continuously updated, so that it can adapt to complex and changeable motor operating conditions.

[0036] The machine learning model can be a neural network model that can learn historical data features, such as RNN (Recurrent Neural Network), LSTM (Long Short-Term Memory), GRU (Gate Recurrent Unit), Bi-LSTM (Bidirectional Long Short-Term Memory), etc. In this embodiment, the machine learning model is preferably an LSTM model.

[0037] The automatic controller specifically includes: a feature extraction unit, a reverse compensation force calculation unit, a compensation electromagnetic field calculation unit, a fine-tuning amount generation unit and a control signal output unit.

[0038] The feature extraction unit is used to execute step S51: analyzing the current vibration data to extract vibration feature values.

[0039] The vibration characteristic values ​​include but are not limited to vibration amplitude, vibration frequency and vibration direction. Step S51 specifically comprises: performing fast Fourier transform on the current vibration data to obtain vibration amplitude and vibration frequency; performing mechanical analysis on the current vibration data of multiple points (collected by multiple vibration sensors 1) to obtain vibration direction.

[0040] The reverse compensation force calculation unit is used to execute step S52: calculate the corresponding reverse compensation force according to the vibration characteristic value.

[0041] The compensation electromagnetic field calculation unit is used to execute step S53: calculate the corresponding compensation electromagnetic field according to the reverse compensation force.

[0042] The fine-tuning amount generating unit is used to execute step S54: generating a corresponding control signal fine-tuning amount according to the compensation electromagnetic field.

[0043] The control signal output unit is used to execute step S55: superimpose the basic control signal and the control signal fine-tuning amount to obtain a fine-tuned control signal.

[0044] Step S52 can be implemented by automatic control algorithms such as PID, MPC, SMC, adaptive control or fuzzy control, with the control target of making the vibration characteristic value as small as possible, and determining the magnitude and direction of the reverse compensation force. Under the condition that the relevant parameters of the electromagnetic coil of the motor and the compensation module are determined, there is a definite mapping relationship between the reverse compensation force, the compensation electromagnetic field and the control signal, which can be determined by existing measurement technology, and then through steps S53-S54, the corresponding control signal fine-tuning amount is derived and calculated according to the reverse compensation force.

[0045] Furthermore, the motor vibration suppression system of the present invention further includes a magnetic field sensor. The magnetic field sensor is arranged at the motor bearing or rotor, and is used to detect the actual compensation electromagnetic field. The control device of the present invention further includes a magnetic field correction unit. The magnetic field correction unit is used to perform the step of comparing the difference between the actual compensation electromagnetic field and the target compensation electromagnetic field calculated in step S53, and correcting the control signal fine-tuning amount after the fine-tuning amount generating unit performs step S54.

[0046] Furthermore, the motor vibration suppression system of the present invention also includes a power management module. The power management module is used to dynamically adjust the power supply energy distribution of each module in the system; when the motor is running at high speed, the power supply to the electromagnetic coil of the compensation module is prioritized to ensure that the strength of the reverse compensation force for suppressing the motor vibration is large enough.

[0047] The present invention has the following technical effects:

[0048] 1. The present invention applies a reverse compensation force to suppress vibration to the motor by compensating the electromagnetic field, without direct contact with the motor body, without the risk of wearing the motor, and ensuring the service life of the motor. The present invention belongs to active shock absorption rather than passive shock absorption, and has a better vibration suppression effect than passive shock absorption. The present invention can be dynamically adjusted following the changes in the working conditions of the motor, and can cope with complex and changeable vibration conditions.

[0049] 2. Existing motor vibration suppression schemes based on magnetic field compensation are usually implemented by controlling the rotating magnetic field generated by the original stator winding of the motor. However, the rotating magnetic field generated by the original stator winding of the motor can only generate a reverse compensation force in the direction of motor rotation, and cannot suppress vibrations in directions other than motor rotation, and has a narrow scope of application. The compensation electromagnetic field generated by the compensation module of the present invention is independent of the rotating magnetic field generated by the original stator winding of the motor. It can not only compensate for vibrations in directions other than motor rotation, and has a larger scope of application, but can also operate independently without interfering with the motor drive, thereby improving system stability and reliability, being able to respond to vibrations more quickly, and achieving better vibration suppression effects.

[0050] 3. The control method of the present invention combines the advantages of machine learning models and traditional automatic control algorithms. It first obtains the macro basic control signal through the machine learning model, and then fine-tunes the basic control signal through the traditional automatic control algorithm, thereby combining macro adjustment and micro adjustment. It not only improves the accuracy of the control signal finally generated, but also achieves a faster calculation speed, with a high cost performance.

[0051] 4. The machine learning model of the present invention can learn the characteristics of historical vibration data and obtain the initial basic control signal in advance, thereby avoiding the overshoot problem caused by lack of data in the initial stage of motor operation, greatly shortening the trial and error time, and improving the efficiency of the control algorithm. The machine learning model of the present invention can also continuously update its own parameters according to the updated historical vibration data, so as to adapt to the complex and changeable motor working conditions.

[0052] The above-mentioned embodiments only express several implementation methods of the present invention, and the description is relatively specific and detailed, but it cannot be understood as limiting the scope of the invention patent. It should be pointed out that for ordinary technicians in this field, several modifications and improvements can be made without departing from the concept of the present invention, and the present invention is also intended to include these modifications and modifications.

Claims

1. A motor vibration suppression system based on active magnetic field compensation, characterized in that: include: Vibration sensor, which monitors the vibration of the motor and collects vibration data; a control device for generating a control signal according to the vibration data; A compensation module, which generates a compensation electromagnetic field according to the control signal, and generates a reverse compensation force based on the compensation electromagnetic field on the motor; Wherein, the compensation module includes several groups of electromagnetic coils and a driving unit electrically connected to the electromagnetic coils; the driving unit adjusts the driving current on the electromagnetic coils according to the control signal, thereby controlling the electromagnetic coils to generate a specified compensation electromagnetic field.

2. The motor vibration suppression system based on active magnetic field compensation according to claim 1, characterized in that: The control device comprises: a machine learning model, a feedback unit and an automatic controller; The control device generates a control signal through the following process: The machine learning model generates an initial basic control signal based on historical vibration data; The feedback unit controls the compensation module to generate a compensation electromagnetic field according to the initial basic control signal, and simultaneously obtains current vibration data through the vibration sensor; The machine learning model updates the basic control signal based on the historical vibration data and the current vibration data; The automatic controller fine-tunes the basic control signal according to the current vibration data to obtain a control signal.

3. The motor vibration suppression system based on active magnetic field compensation according to claim 2 is characterized in that: The control device further comprises: a data updating unit and a circulation unit; After the automatic controller receives the control signal, the data updating unit combines the current vibration data at the last moment with the historical vibration data to obtain new historical vibration data, and obtains new current vibration data through the vibration sensor; The loop unit repeatedly calls the machine learning model, the automatic controller and the data updating unit, thereby continuously updating the control signal according to the current vibration data.

4. The motor vibration suppression system based on active magnetic field compensation according to claim 3 is characterized in that: The machine learning model is trained by historical vibration data, and when the historical vibration data is updated, the parameters of the machine learning model are also continuously updated.

5. The motor vibration suppression system based on active magnetic field compensation according to claim 4 is characterized in that: The automatic controller specifically comprises: A feature extraction unit, which is used to analyze the current vibration data and extract vibration feature values; A reverse compensation force calculation unit, which is used to calculate the corresponding reverse compensation force according to the vibration characteristic value; A compensation electromagnetic field calculation unit, used for calculating a corresponding compensation electromagnetic field according to the reverse compensation force; A fine-tuning amount generating unit, which is used to generate a corresponding control signal fine-tuning amount according to the compensation electromagnetic field; The control signal output unit is used to superimpose the basic control signal and the control signal fine-tuning amount to obtain a fine-tuned control signal.

6. The motor vibration suppression system based on active magnetic field compensation according to claim 5, characterized in that: Also included is a magnetic field sensor for detecting the actual compensation electromagnetic field; The control device further comprises: a magnetic field correction unit; After the fine-tuning amount generating unit obtains the control signal fine-tuning amount, the magnetic field correcting unit compares the difference between the actual compensation electromagnetic field and the target compensation electromagnetic field obtained by the compensation electromagnetic field calculating unit, and corrects the control signal fine-tuning amount.

7. The motor vibration suppression system based on active magnetic field compensation according to claim 6, characterized in that: The control device also includes a pre-processing unit; After acquiring the current vibration data, the preprocessing unit performs normalization and filtering and denoising preprocessing on the current vibration data, and then outputs the preprocessed current vibration data to the machine learning model and the automatic controller.

8. The motor vibration suppression system based on active magnetic field compensation according to any one of claims 2 to 7, characterized in that: The machine learning model is an LSTM model.

9. A control method for the motor vibration suppression system according to claim 1, characterized in that: Includes steps: S1, generating an initial basic control signal based on historical vibration data; S2, generating a compensating electromagnetic field according to the initial basic control signal, and acquiring current vibration data at the same time; S3, updating the basic control signal according to the historical vibration data and the current vibration data; S4, fine-tuning the basic control signal according to the current vibration data to obtain a control signal.

10. A control device for the motor vibration suppression system according to claim 1, characterized in that: include: Machine learning models, feedback units, and automatic controllers; The machine learning model generates an initial basic control signal based on historical vibration data; The feedback unit controls the compensation module to generate a compensation electromagnetic field according to the initial basic control signal, and simultaneously obtains current vibration data through the vibration sensor; The machine learning model updates the basic control signal based on the historical vibration data and the current vibration data; The automatic controller fine-tunes the basic control signal according to the current vibration data to obtain a control signal.

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