CN116413595A - Method and device for detecting eccentric rotor of synchronous reluctance motor based on vibration test - Google Patents
Method and device for detecting eccentric rotor of synchronous reluctance motor based on vibration test Download PDFInfo
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Abstract
The invention discloses a method and a device for detecting an eccentric rotor of a synchronous reluctance motor based on vibration test, wherein the method comprises the following steps: the method comprises the steps of performing operation test processing on a synchronous reluctance motor under different working conditions and different vibration frequencies to obtain rotor eccentric distance and vibration amplitude data of the synchronous reluctance motor under different working conditions and different vibration frequencies; constructing a synchronous reluctance motor vibration model based on a neural network algorithm, and training the synchronous reluctance motor vibration model based on the neural network algorithm by utilizing rotor eccentric distance and vibration amplitude data of the synchronous reluctance motor at different vibration frequencies under different working conditions to obtain a trained synchronous reluctance motor vibration model based on the neural network algorithm; and acquiring vibration amplitude data of the target synchronous electromagnetic motor under different frequencies, and inputting the vibration amplitude data of the target synchronous electromagnetic motor under different frequencies into the trained synchronous reluctance motor vibration model based on the neural network algorithm to obtain the working condition and the rotor eccentric distance of the target synchronous electromagnetic motor.
Description
Technical Field
The invention relates to the technical field of motors, in particular to a synchronous reluctance motor eccentric rotor detection method and device based on vibration test.
Background
For better electromagnetic performance, the air gap length of synchronous reluctance motors is smaller than other types of motors, typically 0.3-0.5mm. In the process of manufacturing the motor and in daily use, rotor eccentricity may occur, that is, the center of the rotor deviates from the center of the motor or the center of the rotor deviates from the center of rotation of the motor. At this time, uneven distribution of the length of the air gap on the circumference of the outer surface of the rotor is caused, so that the performance of the motor is affected, and friction between the rotor and the stator of the motor and even damage of the motor can be caused when the motor is serious.
The traditional motor rotor eccentric state detection generally adopts a stator three-phase current detection method, or the size of an air gap magnetic field is measured by a magnetic induction sensor. Both of the above methods have certain difficulties in operation: the design air gap length of the synchronous reluctance motor is smaller, the influence of rotor eccentricity in the error allowable range on the electromagnetic performance of the motor is limited, and the detection effect of utilizing stator current is poor; meanwhile, the air gap is positioned between the stator and the rotor, the length is generally extremely small, and the sensor is difficult to place, so that the magnetic density value cannot be accurately measured.
Rotor eccentricity of synchronous reluctance motors is generally divided into static eccentricity and dynamic eccentricity, and in the two eccentric states, the air gaps between the stator and the rotor are not uniformly distributed, so that unbalanced magnetic tension phenomenon which is even aggravated on the rotor is generated in the air gaps. Electromagnetic forces generated in the air gap act on the stator slots and are transmitted to the motor housing through the stator slots, causing irregular and intense vibration of the motor housing.
Disclosure of Invention
The invention provides a method and a device for detecting an eccentric rotor of a synchronous reluctance motor based on vibration test, so as to solve the technical problems of low accuracy and high operation difficulty of the existing motor rotor eccentric detection technology.
The embodiment of the invention provides a method for detecting an eccentric rotor of a synchronous reluctance motor based on vibration test, which comprises the following steps:
the method comprises the steps of performing operation test processing on a synchronous reluctance motor under different working conditions and different vibration frequencies to obtain rotor eccentric distance and vibration amplitude data of the synchronous reluctance motor under different working conditions and different vibration frequencies;
constructing a synchronous reluctance motor vibration model based on a neural network algorithm, and training the synchronous reluctance motor vibration model based on the neural network algorithm by utilizing rotor eccentric distance and vibration amplitude data of the synchronous reluctance motor at different vibration frequencies under different working conditions to obtain a trained synchronous reluctance motor vibration model based on the neural network algorithm;
and acquiring vibration amplitude data of the target synchronous electromagnetic motor under different frequencies, and inputting the vibration amplitude data of the target synchronous electromagnetic motor under different frequencies into the trained synchronous reluctance motor vibration model based on the neural network algorithm to obtain the working condition and the rotor eccentric distance of the target synchronous electromagnetic motor.
Preferably, the operating conditions include a non-eccentric operating condition, a static eccentric operating condition, and a dynamic eccentric operating condition.
Preferably, the acquiring vibration amplitude data of the target synchronous electromagnetic motor at different frequencies includes:
and during the operation of the target electromagnetic motor, acquiring one or more acceleration sensors arranged on the shell of the target electromagnetic motor in real time to acquire vibration amplitude data of the target synchronous electromagnetic motor under different frequencies.
Preferably, the operation test processing is performed on the synchronous reluctance motor under different working conditions and different vibration frequencies, and obtaining the rotor eccentric distance and vibration amplitude data of the synchronous reluctance motor under different working conditions and different vibration frequencies includes:
and constructing a vibration simulation model of the synchronous reluctance motor, and performing operation test processing on the synchronous reluctance motor through the vibration simulation model of the synchronous reluctance motor to obtain rotor eccentric distance and vibration amplitude data of the synchronous reluctance motor under different vibration frequencies under different working conditions.
Preferably, the vibration simulation model of the synchronous reluctance motor takes electromagnetic force data under different working conditions as input of the vibration simulation model of the synchronous reluctance motor, and takes vibration amplitude data acquired by one or more acceleration sensors arranged on an electromagnetic motor shell as output of the vibration simulation model of the synchronous reluctance motor.
Preferably, the method further comprises: calculating and acquiring electromagnetic force data under different working conditions, wherein the electromagnetic force data specifically comprises:
respectively building a first synchronous reluctance motor model under a non-eccentric working condition, a second synchronous reluctance motor model under a static eccentric working condition and a third synchronous reluctance motor model under a dynamic eccentric working condition;
analyzing the air gap flux density distribution of the synchronous reluctance motor by using the first synchronous reluctance motor model to obtain first air gap flux density data under the non-eccentric working condition, calculating first electromagnetic force density according to the first air gap flux density data, and calculating first electromagnetic force data under the non-eccentric working condition by using the first electromagnetic force density;
analyzing the air gap flux density distribution of the synchronous reluctance motor by using the second synchronous reluctance motor model to obtain second air gap flux density data under a static eccentric working condition, calculating second electromagnetic force density according to the second air gap flux density data, and calculating second electromagnetic force data under the static eccentric working condition by using the second electromagnetic force density;
and analyzing the air gap flux density distribution of the synchronous reluctance motor by using the third synchronous reluctance motor model to obtain third air gap flux density data under a dynamic eccentric working condition, calculating third electromagnetic force density according to the third air gap flux density data, and calculating third electromagnetic force data under the dynamic eccentric working condition by using the third electromagnetic force density.
The embodiment of the invention provides a synchronous reluctance motor eccentric rotor detection device based on vibration test, which comprises:
the acquisition module is used for obtaining rotor eccentric distance and vibration amplitude data of the synchronous reluctance motor under different working conditions and different vibration frequencies by carrying out operation test treatment on the synchronous reluctance motor under different working conditions and different vibration frequencies;
the building and training module is used for building a synchronous reluctance motor vibration model based on a neural network algorithm, and training the synchronous reluctance motor vibration model based on the neural network algorithm by utilizing rotor eccentric distance and vibration amplitude data of the synchronous reluctance motor under different vibration frequencies under different working conditions to obtain a trained synchronous reluctance motor vibration model based on the neural network algorithm;
the detection module is used for acquiring vibration amplitude data of the target synchronous electromagnetic motor under different frequencies, and inputting the vibration amplitude data of the target synchronous electromagnetic motor under different frequencies into the trained synchronous reluctance motor vibration model based on the neural network algorithm to obtain working conditions and rotor eccentric distance of the target synchronous electromagnetic motor.
Preferably, the operating conditions include a non-eccentric operating condition, a static eccentric operating condition, and a dynamic eccentric operating condition.
Preferably, the detection module is specifically configured to acquire, in real time, vibration amplitude data of the target synchronous electromagnetic motor at different frequencies during operation of the target electromagnetic motor, where the vibration amplitude data is acquired in real time by one or more acceleration sensors disposed on a housing of the target electromagnetic motor.
Preferably, the obtaining module is specifically configured to construct a vibration simulation model of the synchronous reluctance motor, and perform operation test processing on the synchronous reluctance motor through the vibration simulation model of the synchronous reluctance motor, so as to obtain rotor eccentric distance and vibration amplitude data of the synchronous reluctance motor at different vibration frequencies under different working conditions.
The beneficial effects of the invention are as follows: firstly, the difficulty of placing the acceleration sensor on the motor housing is lower than placing the magnetic induction sensor in a narrow air gap; secondly, the air gap flux density caused by rotor eccentricity is unevenly distributed, a large number of magnetic induction sensors are required to be placed for measuring the magnetic field intensity in the circumference outside the rotor, and only three acceleration sensors are required in the technical scheme of the invention, so that the cost and the testing difficulty are greatly reduced; third, compared with the change of other parameters (such as air gap flux density, stator current, etc.), the vibration amplitude of the motor shell has the characteristics of more intuitionistic and obvious, and is convenient for state observation and data acquisition.
Drawings
FIG. 1 is a flow chart of a method for detecting an eccentric rotor of a synchronous reluctance motor based on vibration test;
FIG. 2 is a schematic diagram of a device for detecting an eccentric rotor of a synchronous reluctance motor based on vibration test;
FIG. 3 is a schematic diagram of static eccentricity and dynamic eccentricity of a rotor of a synchronous reluctance motor provided by the invention;
FIG. 4 is a schematic illustration of a static eccentric condition provided by the present invention;
FIG. 5 is a schematic illustration of a dynamic eccentric condition provided by the present invention;
FIG. 6 is a graph of air gap flux density versus 0 for a rotor provided by the present invention;
FIG. 7 is a graph comparing air gap density at 90 rotor position provided by the present invention;
FIG. 8 is a graph of electromagnetic force distribution in an air gap in an unontric state of a rotor provided by the present invention;
FIG. 9 is a graph of electromagnetic force distribution in an air gap in a static eccentric condition of a rotor provided by the present invention;
FIG. 10 is a graph of electromagnetic force distribution in an air gap in a dynamic eccentric rotor condition provided by the present invention;
FIG. 11 is a graph showing the comparison of vibration amplitude of a motor housing in an eccentric state of a rotor of a synchronous reluctance motor provided by the invention;
fig. 12 is a structural model diagram of a neural network algorithm provided by the invention.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention. In the following description, suffixes such as "module", "part" or "unit" for representing elements are used only for facilitating the description of the present invention, and have no particular meaning in themselves. Thus, "module," "component," or "unit" may be used in combination.
Fig. 1 is a flowchart of a method for detecting an eccentric rotor of a synchronous reluctance motor based on vibration test, and as shown in fig. 1, the method may include:
step S101: the method comprises the steps of performing operation test processing on a synchronous reluctance motor under different working conditions and different vibration frequencies to obtain rotor eccentric distance and vibration amplitude data of the synchronous reluctance motor under different working conditions and different vibration frequencies;
step S102: constructing a synchronous reluctance motor vibration model based on a neural network algorithm, and training the synchronous reluctance motor vibration model based on the neural network algorithm by utilizing rotor eccentric distance and vibration amplitude data of the synchronous reluctance motor at different vibration frequencies under different working conditions to obtain a trained synchronous reluctance motor vibration model based on the neural network algorithm;
step S103: and acquiring vibration amplitude data of the target synchronous electromagnetic motor under different frequencies, and inputting the vibration amplitude data of the target synchronous electromagnetic motor under different frequencies into the trained synchronous reluctance motor vibration model based on the neural network algorithm to obtain the working condition and the rotor eccentric distance of the target synchronous electromagnetic motor.
Wherein, the working condition comprises a non-eccentric working condition, a static eccentric working condition and a dynamic eccentric working condition.
Specifically, the obtaining vibration amplitude data of the target synchronous electromagnetic motor at different frequencies includes: and during the operation of the target electromagnetic motor, acquiring one or more acceleration sensors arranged on the shell of the target electromagnetic motor in real time to acquire vibration amplitude data of the target synchronous electromagnetic motor under different frequencies.
Further, the operation test processing is performed on the synchronous reluctance motor by different vibration frequencies under different working conditions, and obtaining the rotor eccentric distance and vibration amplitude data of the synchronous reluctance motor under different vibration frequencies under different working conditions comprises the following steps: and constructing a vibration simulation model of the synchronous reluctance motor, and performing operation test processing on the synchronous reluctance motor through the vibration simulation model of the synchronous reluctance motor to obtain rotor eccentric distance and vibration amplitude data of the synchronous reluctance motor under different vibration frequencies under different working conditions.
The vibration simulation model of the synchronous reluctance motor takes electromagnetic force data under different working conditions as input of the vibration simulation model of the synchronous reluctance motor, and meanwhile takes vibration amplitude data acquired by one or more acceleration sensors arranged on an electromagnetic motor shell as output of the vibration simulation model of the synchronous reluctance motor.
The embodiment of the invention also comprises the following steps: calculating and acquiring electromagnetic force data under different working conditions, wherein the electromagnetic force data specifically comprises: respectively building a first synchronous reluctance motor model under a non-eccentric working condition, a second synchronous reluctance motor model under a static eccentric working condition and a third synchronous reluctance motor model under a dynamic eccentric working condition; analyzing the air gap flux density distribution of the synchronous reluctance motor by using the first synchronous reluctance motor model to obtain first air gap flux density data under the non-eccentric working condition, calculating first electromagnetic force density according to the first air gap flux density data, and calculating first electromagnetic force data under the non-eccentric working condition by using the first electromagnetic force density; analyzing the air gap flux density distribution of the synchronous reluctance motor by using the second synchronous reluctance motor model to obtain second air gap flux density data under a static eccentric working condition, calculating second electromagnetic force density according to the second air gap flux density data, and calculating second electromagnetic force data under the static eccentric working condition by using the second electromagnetic force density; and analyzing the air gap flux density distribution of the synchronous reluctance motor by using the third synchronous reluctance motor model to obtain third air gap flux density data under a dynamic eccentric working condition, calculating third electromagnetic force density according to the third air gap flux density data, and calculating third electromagnetic force data under the dynamic eccentric working condition by using the third electromagnetic force density.
Fig. 2 is a schematic diagram of a device for detecting an eccentric rotor of a synchronous reluctance motor based on vibration test, as shown in fig. 2, including: the acquisition module is used for obtaining rotor eccentric distance and vibration amplitude data of the synchronous reluctance motor under different working conditions and different vibration frequencies by carrying out operation test treatment on the synchronous reluctance motor under different working conditions and different vibration frequencies; the building and training module is used for building a synchronous reluctance motor vibration model based on a neural network algorithm, and training the synchronous reluctance motor vibration model based on the neural network algorithm by utilizing rotor eccentric distance and vibration amplitude data of the synchronous reluctance motor under different vibration frequencies under different working conditions to obtain a trained synchronous reluctance motor vibration model based on the neural network algorithm; the detection module is used for acquiring vibration amplitude data of the target synchronous electromagnetic motor under different frequencies, and inputting the vibration amplitude data of the target synchronous electromagnetic motor under different frequencies into the trained synchronous reluctance motor vibration model based on the neural network algorithm to obtain working conditions and rotor eccentric distance of the target synchronous electromagnetic motor.
Wherein, the working condition comprises a non-eccentric working condition, a static eccentric working condition and a dynamic eccentric working condition.
Further, the detection module is specifically configured to acquire, in real time, vibration amplitude data of the target synchronous electromagnetic motor at different frequencies during operation of the target electromagnetic motor, where the vibration amplitude data is acquired in real time by one or more acceleration sensors disposed on a housing of the target electromagnetic motor.
The acquisition module is specifically used for constructing a vibration simulation model of the synchronous reluctance motor, and performing operation test processing on the synchronous reluctance motor through the vibration simulation model of the synchronous reluctance motor to obtain rotor eccentric distance and vibration amplitude data of the synchronous reluctance motor under different working conditions and at different vibration frequencies.
The technical contents of the present invention will be explained with reference to FIG. 3-FIG. 12
In the running process of the motor, uneven air gap flux density distribution in the air gap can generate electromagnetic force in the tangential direction and the radial direction of the rotor, and the electromagnetic force acts on the inner side wall of the stator groove, is transmitted to the motor shell through the transmission of the stator, and finally generates motor vibration on the motor shell.
When the motor rotor normally runs, the rotating circle center is concentric with the stator, at the moment, the air gap between the rotor and the stator is uniformly distributed, and the correspondingly generated electromagnetic force is also uniform. When the rotor is improperly installed, or the rotor is worn due to long-time work, or other faults occur, the center of the rotor can deviate, at the moment, the air gap distribution between the rotor and the stator becomes uneven, and the correspondingly generated electromagnetic force is uneven, so that the vibration amplitude and the law of the motor shell are influenced to a certain extent. Through observing and recording the vibration amplitude and the rule of the motor under the normal working condition and comparing the vibration amplitude and the rule with the vibration amplitude and the rule of the rotor under the eccentric working condition, the eccentric fault of the rotor can be detected by a motor vibration test method. The method comprises the following specific steps:
(1) Aiming at the eccentric faults of the rotor of the synchronous reluctance motor, the radial tangential plane of the motor is taken as a research object, as shown in fig. 3, the eccentric working conditions of the rotor are divided into two types of static eccentricity and dynamic eccentricity, wherein: under the static eccentric working condition, the circle center of the rotor deviates from the circle center of the motor (namely the circle center of the stator), and the rotor rotates around the circle center of the rotor automatically; under the dynamic eccentric working condition, the circle center of the rotor deviates from the circle center of the motor (namely, the circle center of the stator), and the rotor automatically rotates around the circle center of the rotor and simultaneously integrally performs circular motion around the circle center of the motor (namely, the circle center of the stator);
(2) Building rotor non-eccentric, static eccentric and dynamic eccentric toolIn the motor electromagnetic model under the condition, firstly, the air gap flux density distribution of the motor is analyzed. Due to motor air gap flux density B g Can be obtained by the following formula:
wherein U is s Represents the magnetomotive force of a stator, U r Represents the magnetomotive force of the rotor L g Represents the length of the air gap, mu 0 Indicating vacuum permeability.
As can be seen from FIG. 3, the length L of the air gap is determined under the condition that the rotor is not eccentric g Uniformly distributed, namely the lengths of all the parts in the space are equal;
length L of air gap under static eccentric working condition g Non-uniform distribution, unequal length throughout the space, and unchanged with rotation of the rotor, i.e. L g (θ s ) Unchanged, θ s For the positions of the points on the circumference of the stator in the reference frame, let the eccentric distance of the rotor be δ, as shown in fig. 4, there are:
L g (θ s )＝L g -δcos(θ s )
length L of air gap under dynamic eccentric working condition g Uneven distribution, unequal length throughout the space, and varying with rotation of the rotor, θ m Similarly, assuming that the rotor is eccentric by a distance δ as shown in fig. 5, there are:
L g (θ s )＝L g -δcos(θ s -θ m )
when the length of the air gap is changed, the distribution of the air gap flux density is changed, and when the rotation angle of the rotor is 0, FIG. 6 shows the finite element simulation result of the distribution of the air gap flux density of the motor electromagnetic model under the working conditions of no eccentricity, static eccentricity and dynamic eccentricity (the distribution of the air gap flux density of the static eccentricity is the same as that of the air gap flux density of the dynamic eccentricity); fig. 7 shows finite element simulation results of air gap flux density distribution of the electromagnetic model of the motor under the working conditions of no eccentricity, static eccentricity and dynamic eccentricity when the rotation angle of the rotor is 90 degrees.
(3) Assuming any one timeThe rotor rotation angle is theta m I.e. rotor position theta m The method comprises the steps of carrying out a first treatment on the surface of the At this time, the magnetic density of any point in the air gap is B g (θ s ) And (3) representing. In this position, B g (θ s ) The radial and tangential directions of the point on the rotor are decomposed:
B r (θ s )＝B g (θ s )cosθ m
Bθ(θ s )＝B g (θ s )sinθ m
according to the obtained air gap flux density, the electromagnetic force density in the air gap can be solved by using a Maxwell stress tensor formula, such as:
wherein sigma is the electromagnetic force density in the radial direction of the rotor, tau is the electromagnetic force density in the tangential direction of the rotor, B r B is the component of the air gap flux density in the radial direction of the rotor θ Is the component of the air gap flux density in the tangential direction of the rotor.
Since the tangential component of the air gap flux density is typically much smaller than the radial component of the air gap flux density, the tangential component can be omitted from the analysis, thereby obtaining the electromagnetic force density f m The method comprises the following steps:
by electromagnetic force density f m Area S on the inner side wall of the stator slot acting therewith slot The electromagnetic force distribution acting on the circumference of the inner diameter of the stator can be calculated. The electromagnetic force acts on the inner side wall of the stator groove on the circumference of the inner diameter of the stator, and finally acts on the motor shell to generate vibration through the transmission of the stator silicon steel sheet.
FIG. 8 is a graph showing the time-space distribution of electromagnetic force in the air gap of the electromagnetic model of the motor under non-eccentric conditions; FIG. 9 is a graph showing the time-space distribution of electromagnetic force in the air gap of the electromagnetic model of the motor under static eccentric conditions; fig. 10 shows the time-space distribution diagram of the electromagnetic force in the air gap of the electromagnetic model of the motor under the dynamic eccentric condition.
(4) In the vibration module of finite element simulation, the obtained electromagnetic force is used as a model input and acts on the tooth slot opening of the motor stator slot, and the acceleration of each point of the motor shell is used as output to measure the vibration amplitude of the motor, so that the vibration amplitude comparison of the motor at each vibration frequency under the working conditions of no eccentricity, static eccentricity and dynamic eccentricity of the motor as shown in figure 9 can be obtained.
(5) As shown in fig. 11, the rotor eccentric distance and the corresponding motor housing vibration data are collected and arranged, a synchronous reluctance motor vibration model based on a neural network algorithm is constructed, and as shown in fig. 12, self-learning of the algorithm on the rotor eccentric distance, motor housing vibration amplitude and other data is realized.
(6) The difference between the experimental value and the simulation value is measured through the motor bench experiment. Firstly, a motor experiment bench is firmly fixed, 1-3 point positions are arbitrarily selected on a motor shell, and acceleration sensors are respectively placed so as to measure the vibration amplitude of the motor under each frequency (only the vibration amplitude of the motor shell under the non-eccentric working condition is measured in the experiment).
(7) And further collecting and arranging motor shell vibration data obtained in the bench experiment by using a neural network algorithm, and comparing and self-learning with motor shell vibration amplitude data under the non-eccentric working condition obtained in the finite element simulation.
(8) The vibration amplitude of the motor shell of the motor at different rotating speeds and frequencies is measured and automatically compared with empirical data in the system, and the motor working under the non-eccentric working condition, the static eccentric working condition or the dynamic eccentric working condition at the moment is analyzed and known.
The preferred embodiments of the present invention have been described above with reference to the accompanying drawings, and thus do not limit the scope of the claims of the present invention. Any modifications, equivalent substitutions and improvements made by those skilled in the art without departing from the scope and spirit of the present invention shall fall within the scope of the appended claims.
Claims (10)
1. The method for detecting the eccentric rotor of the synchronous reluctance motor based on the vibration test is characterized by comprising the following steps of:
the method comprises the steps of performing operation test processing on a synchronous reluctance motor under different working conditions and different vibration frequencies to obtain rotor eccentric distance and vibration amplitude data of the synchronous reluctance motor under different working conditions and different vibration frequencies;
constructing a synchronous reluctance motor vibration model based on a neural network algorithm, and training the synchronous reluctance motor vibration model based on the neural network algorithm by utilizing rotor eccentric distance and vibration amplitude data of the synchronous reluctance motor at different vibration frequencies under different working conditions to obtain a trained synchronous reluctance motor vibration model based on the neural network algorithm;
and acquiring vibration amplitude data of the target synchronous electromagnetic motor under different frequencies, and inputting the vibration amplitude data of the target synchronous electromagnetic motor under different frequencies into the trained synchronous reluctance motor vibration model based on the neural network algorithm to obtain the working condition and the rotor eccentric distance of the target synchronous electromagnetic motor.
2. The method of claim 1, wherein the operating conditions include a no-eccentricity operating condition, a static-eccentricity operating condition, and a dynamic-eccentricity operating condition.
3. The method of claim 1, wherein the acquiring vibration amplitude data for the target synchronous electromagnetic motor at different frequencies comprises:
and during the operation of the target electromagnetic motor, acquiring one or more acceleration sensors arranged on the shell of the target electromagnetic motor in real time to acquire vibration amplitude data of the target synchronous electromagnetic motor under different frequencies.
4. A method according to claim 3, wherein the obtaining rotor eccentricity and vibration amplitude data of the synchronous reluctance motor at different vibration frequencies under different working conditions by performing operation test processing on the synchronous reluctance motor at different vibration frequencies under different working conditions comprises:
and constructing a vibration simulation model of the synchronous reluctance motor, and performing operation test processing on the synchronous reluctance motor through the vibration simulation model of the synchronous reluctance motor to obtain rotor eccentric distance and vibration amplitude data of the synchronous reluctance motor under different vibration frequencies under different working conditions.
5. The method according to claim 4, wherein the vibration simulation model of the synchronous reluctance motor takes electromagnetic force data under different working conditions as input of the vibration simulation model of the synchronous reluctance motor, and takes vibration amplitude data acquired by one or more acceleration sensors arranged on a shell of the electromagnetic motor as output of the vibration simulation model of the synchronous reluctance motor.
6. The method as recited in claim 5, further comprising: calculating and acquiring electromagnetic force data under different working conditions, wherein the electromagnetic force data specifically comprises:
respectively building a first synchronous reluctance motor model under a non-eccentric working condition, a second synchronous reluctance motor model under a static eccentric working condition and a third synchronous reluctance motor model under a dynamic eccentric working condition;
analyzing the air gap flux density distribution of the synchronous reluctance motor by using the first synchronous reluctance motor model to obtain first air gap flux density data under the non-eccentric working condition, calculating first electromagnetic force density according to the first air gap flux density data, and calculating first electromagnetic force data under the non-eccentric working condition by using the first electromagnetic force density;
analyzing the air gap flux density distribution of the synchronous reluctance motor by using the second synchronous reluctance motor model to obtain second air gap flux density data under a static eccentric working condition, calculating second electromagnetic force density according to the second air gap flux density data, and calculating second electromagnetic force data under the static eccentric working condition by using the second electromagnetic force density;
and analyzing the air gap flux density distribution of the synchronous reluctance motor by using the third synchronous reluctance motor model to obtain third air gap flux density data under a dynamic eccentric working condition, calculating third electromagnetic force density according to the third air gap flux density data, and calculating third electromagnetic force data under the dynamic eccentric working condition by using the third electromagnetic force density.
7. Detection device of synchronous reluctance motor eccentric rotor based on vibration test, characterized by comprising:
the acquisition module is used for obtaining rotor eccentric distance and vibration amplitude data of the synchronous reluctance motor under different working conditions and different vibration frequencies by carrying out operation test treatment on the synchronous reluctance motor under different working conditions and different vibration frequencies;
the building and training module is used for building a synchronous reluctance motor vibration model based on a neural network algorithm, and training the synchronous reluctance motor vibration model based on the neural network algorithm by utilizing rotor eccentric distance and vibration amplitude data of the synchronous reluctance motor under different vibration frequencies under different working conditions to obtain a trained synchronous reluctance motor vibration model based on the neural network algorithm;
the detection module is used for acquiring vibration amplitude data of the target synchronous electromagnetic motor under different frequencies, and inputting the vibration amplitude data of the target synchronous electromagnetic motor under different frequencies into the trained synchronous reluctance motor vibration model based on the neural network algorithm to obtain working conditions and rotor eccentric distance of the target synchronous electromagnetic motor.
8. The apparatus of claim 7, wherein the operating conditions comprise a no-eccentricity operating condition, a static-eccentricity operating condition, and a dynamic-eccentricity operating condition.
9. The device according to claim 7, wherein the detection module is specifically configured to acquire, in real time, vibration amplitude data of the target synchronous electromagnetic motor at different frequencies during operation of the target electromagnetic motor, wherein the one or more acceleration sensors are provided on the target electromagnetic motor housing.
10. The device of claim 9, wherein the acquisition module is specifically configured to construct a vibration simulation model of the synchronous reluctance motor, and perform operation test processing on the synchronous reluctance motor through the vibration simulation model of the synchronous reluctance motor, so as to obtain rotor eccentric distance and vibration amplitude data of the synchronous reluctance motor at different vibration frequencies under different working conditions.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310044791.7A CN116413595A (en) | 2023-01-30 | 2023-01-30 | Method and device for detecting eccentric rotor of synchronous reluctance motor based on vibration test |
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