Health degree assessment method, storage medium and electronic equipment

Abstract

The invention relates to the technical field of evaluation, in particular to a health degree evaluation method, a storage medium and electronic equipment, and the method takes rotating speed characteristic parameters capable of directly reflecting the rotating speed state of a motor as comparison parameters for evaluating the health state of the motor. Specifically, rotating speed characteristic parameters can be obtained according to data collected in a duration needing to be detected; and comparing the rotating speed characteristic parameter with a standard rotating speed characteristic parameter of a healthy operation state serving as a reference to obtain the difference of the distribution rules of the rotating speed characteristic parameter and the standard rotating speed characteristic parameter, so as to obtain the instant healthy state of the motor in the time length needing to be detected. Based on the method, the instant health state of the motor can be accurately detected.

Description

Technical Field

[0001] The present invention relates to the field of assessment technology, and in particular to a health assessment method, a storage medium and an electronic device. Background Art

[0002] Asynchronous motors are power sources for electronic or mechanical equipment. They are widely used in mining machinery, new energy vehicles, steel mill machinery, chemical pumps and other mechanical equipment to provide power for the equipment to drive the equipment to operate normally. In actual applications, asynchronous motors will inevitably decline over time, such as electrical faults such as short circuits between winding turns, current imbalance, and winding short circuits, or mechanical faults such as air gap eccentricity and bearing failure. The severity of the fault will have varying degrees of impact on the normal operation of the asynchronous motor. Therefore, the health of the asynchronous motor is closely related to the efficiency and safety of industrial production.

[0003] However, current methods for detecting whether an asynchronous motor is faulty are usually not accurate. Summary of the invention

[0004] The embodiments of the present invention provide a health evaluation method, a storage medium, and an electronic device, which can obtain a relatively accurate difference between the instantaneous operating state and the healthy operating state of a motor.

[0005] In the first aspect, an embodiment of the present invention provides a health assessment method, in which a first speed characteristic parameter (i.e., a speed characteristic parameter within the time period that needs to be detected) is obtained through data collected from the motor within a first time period (i.e., a time period that needs to be detected), and the first speed characteristic parameter within the first time period is compared with a standard speed characteristic parameter used as a benchmark to obtain the difference between the two, thereby obtaining the health status (i.e., health) of the motor within the first time period.

[0006] It can be understood that no matter what electrical or mechanical fault occurs in the motor, the health status of the motor can be directly reflected in the speed status of the motor. Therefore, the above health evaluation method uses the speed characteristic parameters that can directly reflect the speed status of the motor as the comparison parameters for evaluating the health status of the motor. When the fault types in the available data as a benchmark are not comprehensive or the amount of data for each fault type is insufficient, a more accurate comparison result between the instant health status of the motor and the healthy operating status can be obtained.

[0007] In a possible implementation of the first aspect above, the first speed characteristic parameter may include an equivalent slip within a first duration, an equivalent speed ratio within a first duration, and an equivalent acceleration ratio within a first duration. That is, the first speed characteristic parameter may include an equivalent slip of the motor within a duration to be detected, an equivalent speed ratio of the motor within a duration to be detected, and an equivalent acceleration ratio of the motor within a duration to be detected. And, the standard speed characteristic parameter as a benchmark may include an equivalent slip within a second duration, an equivalent speed ratio within a second duration, and an equivalent acceleration ratio within a second duration. That is, the standard speed characteristic parameter may include an equivalent slip within any duration when the motor is in a healthy operating state, an equivalent speed ratio within any duration when the motor is in a healthy operating state, and an equivalent acceleration ratio within any duration when the motor is in a healthy operating state.

[0008] It can be understood that in a possible implementation, when the motor is in a healthy operating state, for example, the speed characteristic parameters within any period of the motor's warranty period can be used as standard speed characteristic parameters. Therefore, the standard speed characteristic parameters can reflect the healthy operating state of the motor, and by comparing the speed characteristic parameters of the motor within the time period to be detected with the standard speed characteristic parameters, a more accurate healthy state of the motor within the time period to be detected and the difference from the healthy operating state of the motor can be obtained.

[0009] In a possible implementation of the first aspect above, the equivalent slip may be equal to the difference between the actual speed of the motor and the theoretical speed of the motor in the first case (i.e., the case where the theoretical speed of the motor and the actual speed of the motor are constant values). Similarly, the equivalent speed ratio may be equal to the ratio between the actual speed of the motor and the theoretical speed of the motor in the case where the theoretical speed of the motor and the actual speed of the motor are constant values. The equivalent acceleration ratio may be equal to the ratio between the actual acceleration of the motor and the theoretical acceleration sent by the controller of the motor in the second case (i.e., the case where the theoretical speed of the motor and the actual speed of the motor are not constant values).

[0010] It is understood that during the operation of the motor, there may be situations where the theoretical speed and the actual speed remain unchanged, or there may be situations where the theoretical speed and the actual speed change. The above evaluation method subdivides the operation of the motor into different situations according to the change of the speed to obtain the corresponding speed characteristic parameters, thereby improving the accuracy of the overall evaluation.

[0011] In a possible implementation of the first aspect above, when obtaining the health of the motor within the time required for detection, the first speed characteristic parameter (i.e., the speed characteristic parameter within the time required for detection) and the distribution difference of the standard speed characteristic parameter used as a benchmark in each working scene can be first obtained; then, the data of the speed characteristic parameter within the time required for detection in each working scene within a certain confidence range (i.e., the preset confidence range below) and the distribution ratio of all the speed characteristic parameter data within the time required for detection can be obtained; then, the health of the motor within the time required for detection can be obtained based on the aforementioned distribution difference and distribution ratio. Each working scene is a scene with the same working condition.

[0012] It can be understood that the distribution difference of each speed characteristic parameter (one of equivalent slip, equivalent speed ratio and equivalent acceleration ratio) in two time periods under each identical working condition can be used to obtain the distribution of each speed characteristic parameter in the time period to be detected under each working condition, and the difference between the distribution of each speed characteristic parameter in the standard speed characteristic parameter. Then, based on the distribution of each speed characteristic parameter in the time period to be detected as needed in each working condition (i.e., the distribution proportion), the overall distribution difference between the speed characteristic parameters in the time period to be detected and the standard speed characteristic parameters can be obtained, that is, the health level in the time period to be detected.

[0013] The above-mentioned health assessment method can obtain the difference in the overall distribution law of two time periods by obtaining the distribution law of the speed characteristic parameters under different working conditions. It can obtain a more accurate health status within the time period that needs to be detected, and the difference with the healthy operating status corresponding to the standard speed characteristic parameters.

[0014] In a possible implementation of the first aspect above, the same operating condition includes: having the same theoretical speed or the same average value of the theoretical speed, the same gear and the same torque; or having the same theoretical speed or the same average value of the theoretical speed, and the same torque; or having the same gear and the same torque.

[0015] It can be understood that the distribution law of the rotational speed characteristic parameters can be obtained based on different division principles, such as the same working condition, so that the distribution law of the rotational speed characteristic parameters obtained is more accurate.

[0016] In a possible implementation of the first aspect above, the distribution difference between the first speed characteristic parameter (i.e., the speed characteristic parameter within the time length that needs to be detected) and the standard speed characteristic parameter (i.e., the speed characteristic parameter within any time length when the motor is in a healthy operating state) in each working scene can be obtained by the following method, including: obtaining the distribution center of the equivalent slip within the first time length, the distribution center of the equivalent speed ratio within the first time length, and the distribution center of the equivalent acceleration ratio within the first time length corresponding to the first speed characteristic parameter in each working scene; obtaining the distribution center of the equivalent slip within the second time length, the distribution center of the equivalent speed ratio within the second time length, and the distribution center of the equivalent acceleration ratio within the second time length corresponding to the standard speed characteristic parameter in each working scene. Obtain, in each working scenario, a first distance between the distribution center of the equivalent slip in the first time length and the distribution center of the equivalent slip in the second time length; obtain, in each working scenario, a second distance between the distribution center of the equivalent speed ratio in the first time length and the distribution center of the equivalent speed ratio in the second time length; obtain, in each working scenario, a third distance between the distribution center of the equivalent acceleration ratio in the first time length and the distribution center of the equivalent acceleration ratio in the second time length.

[0017] It can be understood that by obtaining the distance between the distribution centers of various characteristic parameters in each identical working scene, that is, under each identical working condition, the distribution difference of each characteristic parameter can be obtained.

[0018] In a possible implementation of the first aspect above, the data of the first speed characteristic parameter in each working scene within a first confidence range (that is, a preset confidence range) and the distribution ratio of the data of the first speed characteristic parameter in all working scenes can be obtained by the following method, including: obtaining the data of the equivalent slip in each working scene within a first time length, the distance from the distribution center of each working scene, and the number of samples within the first confidence range, accounting for a first ratio of the total number of samples of the equivalent slip in the first time length in each working scene; obtaining the data of the equivalent speed ratio in each working scene within the first time length, the distance from the distribution center of each working scene, and the number of samples within the first confidence range, accounting for a second ratio of the total number of samples of the equivalent speed ratio in the first time length in each working scene; obtaining the data of the equivalent acceleration ratio in each working scene within the first time length, the distance from the distribution center of each working scene, and the number of samples within the first confidence range, accounting for a third ratio of the total number of samples of the equivalent acceleration ratio in the first time length in each working scene.

[0019] It can be understood that the distribution law of each characteristic parameter of each working scene (various working conditions) can be statistically analyzed by setting the signal range.

[0020] In a possible implementation of the first aspect above, the following method can be used to obtain the health of the motor in the first time period based on the distribution difference of the first speed characteristic parameter (i.e., the speed characteristic parameter within the time period that needs to be detected) and the standard speed characteristic parameter (i.e., the speed characteristic parameter within any period of time when the motor is in a healthy operating state); and the data of the speed characteristic parameter within the time period that needs to be detected in each working scene, and the distribution proportion of all the speed characteristic parameter within the time period that needs to be detected, including: according to the first distance and the first proportion in each working scene, obtain the equivalent slip within the first time period of all working scenes, and the equivalent slip within the second time period The first statistical difference of the equivalent slip; according to the second distance and the second proportion in each working scene, the second statistical difference of the equivalent speed ratio in the first time length and the equivalent speed ratio in the second time length are obtained for all working scenes; according to the third distance and the third proportion in each working scene, the third statistical difference of the equivalent acceleration ratio in the first time length and the equivalent acceleration ratio in the second time length are obtained for all working scenes; according to the first statistical difference, the second statistical difference, the third statistical difference, and the weight coefficients corresponding to the equivalent slip, the weight coefficient corresponding to the equivalent speed ratio, and the weight coefficient corresponding to the equivalent acceleration ratio, the health of the motor in the first time length is obtained.

[0021] It can be understood that the health of the motor within the time period that needs to be detected can be obtained by summarizing the distribution differences of various characteristic parameters in two time periods.

[0022] In a possible implementation of the first aspect, the first confidence range includes: any one of the ranges [-3σ, 3σ], [-2σ, 2σ], and [-σ, σ], wherein σ is the standard deviation of each parameter in the first speed characteristic parameter, such as σ is the standard deviation of the equivalent slip, equivalent speed ratio, and equivalent acceleration ratio in the first speed characteristic parameter. It can be understood that the first confidence range, i.e., the preset confidence range, can be adjusted according to specific needs, such as adjusting according to the accuracy of the required evaluation results, so as to obtain a more accurate health evaluation result.

[0023] In a second aspect, an embodiment of the present invention provides a health assessment device, comprising one or more processors and one or more memories; the one or more memories are coupled to the one or more processors, and the one or more memories are used to store computer program codes, the computer program codes comprising computer instructions, and when the one or more processors execute the computer instructions, the device executes any one of the health assessment methods provided in the first aspect and various possible implementations of the first aspect.

[0024] In a third aspect, an embodiment of the present invention provides a chip, the chip including a processor, the processor executing computer instructions stored in a computer storage medium, so that an electronic device implements any health assessment method provided by various possible implementations of the first aspect mentioned above.

[0025] In a fourth aspect, an embodiment of the present invention provides a readable storage medium having instructions stored thereon, which, when executed on an electronic device, enables the electronic device to implement any one of the health assessment methods provided by various possible implementations of the first aspect.

[0026] In a fifth aspect, an embodiment of the present invention provides an electronic device, comprising: a memory for storing instructions executed by one or more processors of the electronic device; and a processor, which is one of the processors of the electronic device, for executing the instructions stored in the memory to implement any one of the health assessment methods provided by various possible implementations of the first aspect above.

[0027] In a sixth aspect, an embodiment of the present invention provides a program product, which includes instructions, and when the instructions are executed by an electronic device, the electronic device can implement any health assessment method provided by various possible implementations of the first aspect above. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1A According to some embodiments of the present invention, a flow chart of a method for detecting whether an electrical fault occurs in a motor using a fault diagnosis model is shown;

[0029] Figure 1B According to some embodiments of the present invention, a flow chart of a method for detecting a mechanical fault of a motor using a fault feature library matching method is shown;

[0030] Figure 2 According to some embodiments of the present invention, a schematic diagram of software modules of a health assessment system is shown;

[0031] Figure 3 According to some embodiments of the present invention, a flow chart of a health assessment method is shown;

[0032] Figure 4 According to some embodiments of the present invention, a flow chart of another health assessment method is shown;

[0033] Figure 5A According to some embodiments of the present invention, a time domain schematic diagram of a set of current signals is shown;

[0034] Figure 5B According to some embodiments of the present invention, a set of current signal time-frequency analysis schematic diagrams are shown;

[0035] Figure 5C According to some embodiments of the present invention, a spectrum diagram of a set of current signals is shown;

[0036] Figure 5D According to some embodiments of the present invention, a time domain schematic diagram of a set of vibration signals is shown;

[0037] Figure 5E According to some embodiments of the present invention, a set of frequency spectrograms of vibration signals are shown;

[0038] Fig. 5F According to some embodiments of the present invention, a schematic diagram comparing the theoretical speed and the actual speed of a group of asynchronous motors is shown;

[0039] Figure 5G According to some embodiments of the present invention, another set of time domain schematic diagrams of current signals are shown;

[0040] Figure 5H According to some embodiments of the present invention, another group of time-frequency schematic diagrams of asynchronous motors are shown;

[0041] Fig.5I According to some embodiments of the present invention, another set of time domain schematic diagrams of vibration signals are shown;

[0042] Figure 5J According to some embodiments of the present invention, another group of time-frequency schematic diagrams of asynchronous motors are shown;

[0043] Figure 6 According to some embodiments of the present invention, a schematic diagram of the hardware structure of an electronic device is shown. DETAILED DESCRIPTION

[0044] The technical solutions in the embodiments of the present application will be described clearly and in detail below with reference to the accompanying drawings.

[0045] The illustrative embodiments of the present invention include, but are not limited to, a health assessment method, a storage medium, and an electronic device.

[0046] The following first explains the terms involved in this application.

[0047] (1) Asynchronous motor: Also known as induction motor, it is an AC motor that converts electromechanical energy into mechanical energy by generating electromagnetic torque through the interaction between the rotating magnetic field of the air gap and the induced current of the rotor winding.

[0048] The following is combined with Figures 1A to 6 The technical solution of the present invention is introduced.

[0049] As described in the background art, some current methods for detecting whether an asynchronous motor is faulty are usually not very accurate.

[0050] For example, in some detection methods, a fault diagnosis model is used to detect whether an asynchronous motor has a fault. Figure 1A The method of using the fault diagnosis model to detect whether the motor has an electrical fault is shown. Figure 1A As shown, the steps of this detection method include:

[0051] S101, obtaining historical operating electrical fault data of the motor to be tested.

[0052] S102, using the current signal in the historical running electrical fault data as input data and the electrical fault of the motor to be tested as output data, training a fault diagnosis model, and obtaining a trained fault diagnosis model.

[0053] It is understandable that when an asynchronous motor has an electrical fault, the asynchronous motor will then show abnormal characteristics, such as current imbalance, voltage imbalance, and a significant increase in the negative sequence impedance of the motor. Therefore, the current signal collected by the current sensor installed on the motor to be tested in the historical operating electrical fault data can be obtained, and the current signal can be used as input data to set the reference value for determining that the motor to be tested has an electrical fault to 1, and the reference value for determining that the motor to be tested has not an electrical fault to 0, and train the fault diagnosis model. Among them, the fault diagnosis model can include a convolutional neural network model, a random forest fault diagnosis model, and the like.

[0054] S103, inputting the real-time electrical data of the motor to be tested into the trained fault diagnosis model to determine whether the motor to be tested has an electrical fault.

[0055] It can be understood that after obtaining the real-time electrical data of the motor, the current signal in the real-time electrical data can be input into a trained fault diagnosis model, and then based on the output result of the fault diagnosis model, it is determined whether the motor to be tested has an electrical fault.

[0056] It can be understood that since the historical running electrical fault data only corresponds to electrical faults, if a fault diagnosis model is needed to detect whether a motor has a mechanical fault, the fault diagnosis model can be trained based on the historical running mechanical fault data of the motor.

[0057] It can be understood that the accuracy of the method for detecting whether an asynchronous motor has an electrical fault or a mechanical fault based on a fault diagnosis model depends on whether the working conditions and application scenarios covered by the training data set are comprehensive, and whether the amount of training data in each working condition and application scenario is sufficient. For example, whether the historical operating electrical fault data used to train the fault diagnosis model includes enough historical operating electrical fault data under various torque conditions, and whether it corresponds to the same application scenario, which may include mining scenarios, road driving scenarios, etc.

[0058] Therefore, if the historical operating electrical fault data or historical operating vibration fault data used to train the fault diagnosis model do not cover all the operating conditions and application scenarios, or the amount of training data in any operating condition and application scenario is insufficient, etc., then the method of using the fault diagnosis model to detect whether the asynchronous motor has an electrical fault or a mechanical fault will also have a low accuracy in the detection results.

[0059] In other detection methods, a fault feature library matching method is used to detect whether the asynchronous motor is faulty. Figure 1B The method of detecting the mechanical fault of the motor by using the fault feature library matching method is shown in FIG. Figure 1B As shown, the steps of this detection method include:

[0060] S111, obtaining historical running vibration fault data of the motor to be tested.

[0061] S112, performing feature decomposition on the vibration signal in the historical operation vibration fault data to obtain a fault feature library.

[0062] It can be understood that the historical operating vibration fault data can be feature decomposed, and based on the fault features obtained by the decomposition, such as the residual of the amplitude of the vibration signal, the intensity of the vibration, etc., a fault feature library can be obtained.

[0063] S113, obtaining an actual vibration signal of the motor to be tested.

[0064] S114, matching the fault feature in the actual vibration signal with the fault feature library to determine whether a mechanical fault occurs in the motor to be tested.

[0065] It can be understood that the actual vibration signal can be feature decomposed to obtain the residual of the amplitude of the actual vibration signal, the intensity of vibration, etc. Then the residual of the amplitude of the actual vibration signal, the intensity of vibration, etc. are matched with the fault features in the fault feature library, and the matching results are used to determine whether the motor to be tested has a mechanical fault.

[0066] It is understandable that, since the historical running mechanical fault data only corresponds to mechanical faults, if a fault feature library matching method is needed to detect whether an asynchronous motor has an electrical fault, a fault feature library such as a current signal needs to be re-established based on the historical running electrical fault data of the motor.

[0067] It can be understood that the accuracy of the method for detecting whether an asynchronous motor has a mechanical fault or an electrical fault based on a fault feature library depends on whether the fault feature is extracted accurately, whether the operating conditions and application scenarios covered by the historical operating electrical fault data or the historical operating vibration fault data are comprehensive, and whether the amount of data in each operating condition and application scenario is sufficient, and whether it includes enough types of fault features.

[0068] For example, if the motor is serving in a complex environment, when the motor has mechanical faults such as poor lubrication, the intensity of the vibration signal may not change significantly, and it will be difficult to extract the intensity of the vibration signal, that is, the fault characteristics of the vibration signal. Or because the industrial popularity of three-phase current and voltage sensors is low, it is difficult to obtain voltage and current data for all three phases, which will result in the fault characteristics extracted based on single-phase current and voltage data being incomplete. Or when the motor has abnormalities such as increased internal resistance, the current signal and voltage signal may not have obvious abnormalities, and it will be difficult to extract the fault characteristics of the current signal and voltage signal. Then, the method of detecting whether the asynchronous motor has an electrical fault or a mechanical fault based on the fault feature library will also have a low accuracy in the detection results.

[0069] Furthermore, if the historical operating vibration fault data or historical operating electrical fault data used to establish the fault feature library do not cover all operating conditions and application scenarios, or the amount of data in any one operating condition and application scenario is insufficient, or the number of types of fault features of the data in any one operating condition and application scenario is insufficient, then the method of detecting whether an asynchronous motor has an electrical fault or a mechanical fault based on the fault feature library will also have a low accuracy in the detection results obtained.

[0070] In view of this, the present invention proposes a method for evaluating the health of a motor. In this method, the speed characteristic parameters (hereinafter referred to as characteristic parameters) of the training data set (i.e., the data collected when the asynchronous motor is in a healthy operating state, such as the data collected during the warranty period of the asynchronous motor) and the test data set (i.e., the data collected during the time period that the asynchronous motor needs to be detected) corresponding to each working scene can be obtained respectively. Among them, the characteristic parameters are used to characterize the speed state of the asynchronous motor, including equivalent slip, equivalent speed ratio and equivalent acceleration ratio. Then, the characteristic parameters of the training data set and the test data set in each working scene (i.e., each distribution area mentioned later) are compared to obtain the distribution difference of the distribution law of the characteristic parameters of the training data set and the test data set in each working scene. Then, the health of the motor can be determined based on the distribution difference of the distribution law of the characteristic parameters of the training data set and the test data set in each working scene. For example, the distribution difference of the distribution rules of the characteristic parameters of the training data set and the test data set in each working scenario can be obtained; the distribution ratio of the characteristic parameters of the training data set in each working scenario in the characteristic parameters of the entire training data set can be obtained within a preset confidence range; the distribution ratio of the characteristic parameters of the test data set in each working scenario in the characteristic parameters of the entire test data set can be obtained within a preset confidence range; and the health status of the motor, that is, the specific value of the health of the asynchronous motor corresponding to the test data set, can be obtained based on the weight of each characteristic parameter.

[0071] It can be understood that no matter what type of fault occurs in the asynchronous motor (such as any fault in electrical faults or any fault in mechanical faults), the health status of the asynchronous motor will be directly reflected in the speed state of the asynchronous motor, such as the equivalent slip, equivalent speed ratio, and equivalent acceleration ratio in the characteristic parameters. Therefore, by comparing the characteristic parameters of the asynchronous motor, when the fault types in the benchmark data (i.e., the training data set) are not comprehensive or the amount of data for each fault type is insufficient, a more accurate health of the asynchronous motor can be obtained, that is, the comparison result of the instant health status of the asynchronous motor and the healthy operating status.

[0072] It can be understood that each working scene of the asynchronous motor has the same theoretical speed or the same average value of the theoretical speed, the same gear and the same torque; or each working scene has the same theoretical speed or the same average value of the theoretical speed, and the same torque; or each working scene has the same gear and the same torque. It can be understood that the working scene, that is, the principle of dividing the distribution area of ​​the characteristic parameters, is divided according to the same working conditions and other conditions, so that in the same working scene, each characteristic parameter in the training data set can be compared with the corresponding characteristic parameter in the test data set.

[0073] It can be understood that the equivalent slip of the asynchronous motor is equal to the difference between the actual speed of the asynchronous motor and the theoretical speed sent by the controller of the asynchronous motor in the first case. The equivalent speed ratio is equal to the ratio between the actual speed of the asynchronous motor and the theoretical speed sent by the controller of the asynchronous motor in the first case. The equivalent acceleration ratio is equal to the ratio between the actual acceleration of the asynchronous motor and the theoretical acceleration sent by the controller of the asynchronous motor in the second case. In the first case, that is, the asynchronous motor is in a stable state, the theoretical speed and actual speed of the asynchronous motor are constant values. In the second case, that is, the asynchronous motor is in a non-steady state, the theoretical speed and actual speed of the asynchronous motor are not constant values.

[0074] In a possible implementation, the method for obtaining the health of an asynchronous motor corresponding to a test data set may include: first, three characteristic parameters of a training data set, including data of an equivalent slip, an equivalent speed ratio, and an equivalent acceleration ratio, and three characteristic parameters of a test data set, including data of an equivalent slip, an equivalent speed ratio, and an equivalent acceleration ratio, may be obtained respectively. Then, in each working scene, the distribution difference of the distribution laws of the three characteristic parameters of the training data set and the test data set is obtained, such as the difference between the equivalent slip of the training data set in each working scene and the equivalent slip of the test data set; the difference between the equivalent speed ratio of the training data set in each working scene and the equivalent speed ratio of the test data set; and the difference between the equivalent acceleration ratio of the training data set in each working scene and the equivalent speed ratio of the test data set. Then, within the preset confidence range, the number of samples of the characteristic parameter data of the training data set in each working scene, the distribution proportion of the total number of samples of the characteristic parameter data of the entire training data set, and the distribution proportion of the characteristic parameter data of the test data set in each working scene in the characteristic parameter data of the entire test data set can be calculated. Then, according to the differences in the three characteristic parameters of the training data set and the test data set in each working scene, the distribution ratio of the characteristic parameters of the training data set in each working scene within the preset signal range in the data of the entire training data set, and the distribution ratio of the characteristic parameters of the test data set in each working scene within the preset signal range in the data of the entire test data set, the statistical difference of the equivalent slip of the training data set and the test data set of all working scenes, the statistical difference of the equivalent speed ratio of the training data set and the test data set of all working scenes, and the statistical difference of the equivalent acceleration ratio of the training data set and the test data set of all working scenes are obtained. Finally, according to the weight of each characteristic parameter, the statistical difference of the equivalent slip of the training data set and the test data set of all working scenes, the statistical difference of the equivalent speed ratio of the training data set and the test data set of all working scenes, and the statistical difference of the equivalent acceleration ratio of the training data set and the test data set of all working scenes, the specific value of the health of the asynchronous motor corresponding to the test data set can be obtained.

[0075] It can be understood that the data in the training data set can reflect the working state of the asynchronous motor under a healthy operating state. Therefore, by comparing the characteristic parameters corresponding to the test data set with the characteristic parameters corresponding to the test data set, the working state corresponding to the data in the test data set can be obtained, and the comparison result with the healthy operating state of the asynchronous motor can be obtained.

[0076] It can be understood that in some embodiments, if a fault diagnosis model is needed to detect whether an asynchronous motor has an electrical fault or a mechanical fault, then it is necessary to train the fault diagnosis model based on the historical electrical fault data of the motor, and to train the fault diagnosis model based on the historical mechanical fault data of the motor. That is, the same fault diagnosis model cannot be used to detect both electrical faults and mechanical faults. Moreover, the accuracy of the method for detecting whether an asynchronous motor has an electrical fault or a mechanical fault based on the fault diagnosis model depends on whether the working conditions and application scenarios covered by the training data set are comprehensive, and whether the amount of training data in each working condition and application scenario is sufficient.

[0077] It can be understood that in other embodiments, if it is necessary to use a fault feature library matching method to detect whether an asynchronous motor has an electrical fault or a mechanical fault, then it is necessary to establish a fault feature library such as a current signal based on the historical electrical fault data of the motor, and to establish a fault feature library such as a vibration signal based on the historical mechanical fault data of the motor. That is, the same fault feature library cannot be used to detect both electrical faults and mechanical faults. Moreover, the accuracy of the method for detecting whether an asynchronous motor has a mechanical fault or an electrical fault based on the fault feature library depends on whether the fault feature extraction is accurate, whether the operating conditions and application scenarios covered by the historical electrical fault data or the historical vibration fault data are comprehensive, and whether the amount of data in each operating condition and application scenario is sufficient, and whether enough types of fault features are included.

[0078] It can be understood that any type of fault of the asynchronous motor, including electrical faults and mechanical faults, as well as other possible faults, may directly cause the speed state of the asynchronous motor to be different from the speed state of the asynchronous motor in a healthy operating state. That is, when a fault occurs, the characteristic parameters representing the speed state of the asynchronous motor, including equivalent slip, equivalent speed ratio and equivalent acceleration ratio, have distribution differences compared with the characteristic parameters representing the speed state of the asynchronous motor in a healthy operating state, including equivalent slip, equivalent speed ratio and equivalent acceleration ratio, thereby affecting the normal use of the asynchronous motor. Therefore, the above-mentioned motor health assessment method based on characteristic parameters does not need to extract the characteristics of all types of faults, nor does it need to consider whether the data used as a benchmark includes enough types of fault characteristics, and can also obtain more accurate detection results. By comparing the characteristic parameters of the asynchronous motor, a more accurate difference between the immediate health state and the healthy operating state of the asynchronous motor can be obtained even when the application scenario is not comprehensive or the amount of data is insufficient.

[0079] The above health assessment method can more accurately assess the difference between the instantaneous operating state and the healthy operating state of the asynchronous motor by comparing the distribution difference between the characteristic parameters of the speed state of the asynchronous motor corresponding to the training data set when the asynchronous motor is in a healthy operating state and the characteristic parameters of the speed state of the asynchronous motor corresponding to the test data set. In addition, by storing the above parameters in different files, it can also provide accurate and rich reference basis for the subsequent life prediction, degradation prediction, etc. of the asynchronous motor.

[0080] Combine the following Figure 2 , Figure 3 , Figure 4 , FIG. 5A to FIG. 5J , Figure 6 A health assessment method of the present invention is described.

[0081] In the embodiments of the present application, an asynchronous motor will be used as an example for description. Other devices with similar functions may also be applicable to the health assessment method of the present application. The embodiments of the present application do not specifically limit the type of device applicable to the health assessment method of the present application.

[0082] Figure 2 A software module diagram of a health assessment system for implementing the health assessment method of the present application is shown. Figure 2 As shown, a health assessment system may include: an information collection module 210 , a feature calculation module 220 , a health assessment module 230 , and a data archiving module 240 .

[0083] The information acquisition module 210 is used to collect the vibration signal, current signal, torque signal and gear signal of the asynchronous motor, and send the collected vibration signal, current signal, torque signal and gear signal of the asynchronous motor to the feature calculation module 220. The information acquisition module 210 includes a vibration signal acquisition module 211, a current signal acquisition module 212, a torque signal acquisition module 213 and a gear signal acquisition module 214. The vibration signal acquisition module 211 is used to receive the vibration signal of the asynchronous motor collected and sent by the vibration acceleration sensor or the vibration velocity sensor. The current signal acquisition module 212 is used to receive the current signal of the asynchronous motor collected and sent by the current sensor. The torque signal acquisition module 213 is used to receive the torque signal of the asynchronous motor collected and sent by the torque sensor. The gear signal acquisition module 214 is used to receive the gear signal of the asynchronous motor sent by the control system of the asynchronous motor. For example, the gear signal of the asynchronous motor may include first gear, second gear, and third gear, where the first gear corresponds to the rated speed of 15 rpm, the second gear corresponds to the rated speed of 25 rpm, and the third gear corresponds to the rated speed of 35 rpm, and each gear corresponds to a different rated speed.

[0084] In a possible implementation, the information acquisition module 210 may include only a vibration signal acquisition module 211, a current signal acquisition module 212, and a gear signal acquisition module 214; or only a vibration signal acquisition module 211 and a current signal acquisition module 212. In the embodiment of the present application, the type and number of modules in the information acquisition module 210 are not specifically limited.

[0085] It is understood that current sensors, vibration acceleration sensors, vibration velocity sensors, torque sensors, etc., which can be used to collect various signals, can be installed on the asynchronous motor. In the embodiments of the present application, there is no specific limitation on the type of sensor used and the installation position of the sensor.

[0086] The feature calculation module 220 is used to calculate the theoretical speed, actual speed, equivalent slip, equivalent speed ratio, and equivalent acceleration ratio of the asynchronous motor, and send the acquired torque signal, gear signal, theoretical speed, actual speed, equivalent slip, equivalent speed ratio, and equivalent acceleration ratio of the asynchronous motor to the health assessment module 230. Among them, the equivalent slip of the asynchronous motor is equal to the difference between the actual speed of the asynchronous motor and the theoretical speed sent by the controller of the asynchronous motor. The equivalent speed ratio is equal to the ratio between the actual speed of the asynchronous motor and the theoretical speed sent by the controller of the asynchronous motor. The equivalent acceleration ratio is equal to the ratio between the actual acceleration of the asynchronous motor and the theoretical acceleration sent by the controller of the asynchronous motor. The feature calculation module 220 includes a theoretical speed and actual speed calculation module 221, an equivalent slip calculation module 222, an equivalent speed ratio calculation module 223, and an equivalent acceleration ratio calculation module 224.

[0087] Among them, the theoretical speed and actual speed calculation module 221 is used to calculate the actual speed according to the vibration signal of the asynchronous motor obtained by the vibration signal acquisition module 211, and the theoretical speed according to the current signal of the asynchronous motor obtained by the current signal acquisition module 212. The equivalent slip calculation module 222 is used to calculate the equivalent slip according to the theoretical speed and the actual speed calculated by the theoretical speed and the actual speed calculation module 221. The equivalent speed ratio calculation module 223 is used to calculate the equivalent speed ratio according to the theoretical speed and the actual speed obtained by the theoretical speed and the actual speed calculation module 221 in the first case. The equivalent acceleration ratio calculation module 224 is used to calculate the equivalent acceleration ratio according to the theoretical speed and the actual speed obtained by the theoretical speed and the actual speed calculation module 221 in the second case. Among them, in the first case, the asynchronous motor is in a stable state, that is, the theoretical speed and the actual speed of the asynchronous motor are constant values. In the second case, the asynchronous motor is in a non-steady state, that is, the theoretical speed and the actual speed of the asynchronous motor are not constant values.

[0088] The health assessment module 230 is used to calculate the health of the asynchronous motor according to the torque signal, gear signal, theoretical speed, actual speed, equivalent slip, equivalent speed ratio, and equivalent acceleration ratio of the asynchronous motor sent by the feature calculation module 220; and send the gear, torque, health, theoretical speed, equivalent slip, equivalent speed ratio, and equivalent acceleration ratio of the asynchronous motor to the data archiving module 240. The health assessment module 230 includes a gear acquisition module 231, a torque acquisition module 232, a health calculation module 233, and a health normalization module 234. Among them, the gear acquisition module 231 is used to receive the gear signal of the asynchronous motor sent by the feature calculation module 220. The torque acquisition module 232 is used to receive the torque signal of the asynchronous motor sent by the feature calculation module 220. The health calculation module 233 is used to calculate the health of the asynchronous motor according to the torque signal, gear signal, theoretical speed, actual speed, equivalent slip, equivalent speed ratio, and equivalent acceleration ratio of the asynchronous motor sent by the feature calculation module 220. The health normalization module 234 is used to clear the previous health information before the health calculation module 233 calculates the health this time. The method for calculating the health of the asynchronous motor by the health calculation module 233 will be described below in conjunction with Figure 4 Detailed description.

[0089] The data archiving module 240 is used to create a file according to the gear, torque, theoretical speed, health, equivalent slip, equivalent speed ratio, and equivalent acceleration ratio sent by the health assessment module 230. The data archiving module 240 includes a health archiving module 241, an equivalent slip archiving module 242, an equivalent acceleration ratio archiving module 243, an equivalent speed ratio archiving module 244, and a theoretical speed and torque archiving module 245. Among them, the health archiving module 241 is used to save the health data of the asynchronous motor sent by the health assessment module 230. The equivalent slip archiving module 242 is used to save the equivalent slip data of the asynchronous motor sent by the health assessment module 230. The equivalent acceleration ratio archiving module 243 is used to save the equivalent acceleration ratio data of the asynchronous motor sent by the health assessment module 230. The equivalent speed ratio archiving module 244 is used to save the equivalent speed ratio data of the asynchronous motor sent by the health assessment module 230. The theoretical speed and torque archive module 245 is used to store the theoretical speed and torque data of the asynchronous motor sent by the health assessment module 230; or to store the gear and torque data of the asynchronous motor sent by the health assessment module 230. In addition, the distribution area of ​​the data in the health archive module 241, the equivalent slip archive module 242, the equivalent acceleration ratio archive module 243 and the equivalent speed ratio archive module 244 will correspond to the distribution area of ​​the data in the theoretical speed and torque archive module 245.

[0090] It can be understood that the above-mentioned health assessment system can evaluate the health of the asynchronous motor based on the collected vibration signal, current signal, torque signal, and gear signal of the asynchronous motor, and save the assessment results and various data in grades, providing more accurate data support for the life prediction and degradation prediction of the asynchronous motor.

[0091] The following will be based on Figure 2 A health assessment system shown in FIG. Figure 3 and Figure 4 , introduces a health assessment method of the present invention.

[0092] Figure 3 A flow chart of a health assessment method is shown. Figure 3 The steps of a health assessment method may include:

[0093] S310, obtaining a training data set and a test data set for health assessment of an asynchronous motor.

[0094] It can be understood that the specific type of collected signals can be set based on the actual scenario. For example, from the perspective of saving overall costs and improving the efficiency of the evaluation, in one possible implementation, the data of the current signal and vibration signal of the asynchronous motor can be collected as the training data set and test data set for the health assessment. Alternatively, in another possible implementation, the data of the current signal, vibration signal, and gear signal of the asynchronous motor can be collected as the training data set and test data set for the health assessment. Alternatively, in another possible implementation, the data of the current signal, vibration signal, gear signal, and torque signal of the asynchronous motor can be collected as the training data set and test data set for the health assessment. In the present application, there is no specific limitation on the type of collected signals.

[0095] It can be understood that the collection time of the training data set and the test data set of the current signal can be set according to the actual situation. For example, it can be set according to the time of the healthy operating state of the asynchronous motor, such as the warranty period of the asynchronous motor. If the warranty period of the asynchronous motor is 12 months, then the collection time of the training data set of the current signal can be set to within 12 months of normal operation of the asynchronous motor after leaving the factory, and the collection time of the test data set of the current signal can be set to within 12 months and 24 months after the asynchronous motor leaves the factory.

[0096] In one possible implementation, the collection time of the training data set can be greater than or equal to 3 months, so as to provide sufficient data for evaluating the health of the asynchronous motor and improve the accuracy of the health of the asynchronous motor. In this application, there is no specific limitation on the collection time of the training data set and the test data set of the current signal.

[0097] It is understandable that a current clamp may be added to the asynchronous motor to collect the single-phase current signal x(t) in the frequency converter of the asynchronous motor. In the embodiments of the present application, no specific limitation is made to the device for collecting the current signal of the asynchronous motor.

[0098] For example, 50 groups of current signals can be collected within every 24 hours, the collection time of each group of current signals is 2 seconds, and the collection interval time between each two adjacent groups of current signals is greater than or equal to 20 minutes. It can be understood that the total time for collecting current signals, the number of groups of current signals collected, the collection time of each group of current signals, and the collection interval time between each two adjacent groups of current signals can all be set according to specific scenarios and needs. In the embodiments of the present application, the specific values ​​of the aforementioned parameters are not specifically limited.

[0099] It can be understood that the parameters such as time, frequency and duration of the collection of vibration signals and current signals are the same one-to-one, that is, the vibration signal and the current signal are collected at the same time with the same frequency and duration. The collection duration of the training data set and the test data set of the vibration signal can correspond to the current signal and be set according to actual needs.

[0100] For example, it can also be set according to the warranty period of the asynchronous motor. If the warranty period of the asynchronous motor is 12 months, then the collection time of the training data set of the vibration signal can be set to within 12 months of normal operation of the asynchronous motor after leaving the factory, and the collection time of the test data set of the vibration signal can be set to within 12 months to 24 months after the asynchronous motor leaves the factory. In this application, the collection time of the training data set and the test data set of the vibration signal is not specifically limited.

[0101] It is understandable that a vibration acceleration sensor or a vibration velocity sensor can be installed in the asynchronous motor to collect the vibration signal y(t) of the asynchronous motor. For example, 50 groups of vibration signals can be collected within every 24 hours, and the collection time of each group of vibration signals is 2 seconds, and the collection interval time of each adjacent group of vibration signals is greater than or equal to 20 minutes. It is understandable that the total time of collecting vibration signals, the number of groups collecting vibration signals, the collection time of each group of vibration signals, and the collection interval time of each adjacent group of vibration signals can be set according to specific scenarios and needs. In the embodiment of the present application, the specific numerical values ​​of the aforementioned parameters are not specifically limited.

[0102] S320, respectively calculating feature parameters corresponding to the training data set and the test data set.

[0103] In a possible implementation, the theoretical speed and actual speed of the training data set of the asynchronous motor can be calculated based on the current signal and vibration signal of the training data set of the asynchronous motor, and the characteristic parameters such as equivalent slip, equivalent speed ratio, and equivalent acceleration ratio of the training data set can be further calculated. In addition, the theoretical speed and actual speed of the test data set of the asynchronous motor can be calculated based on the current signal and vibration signal of the test data set of the asynchronous motor, and the characteristic parameters such as equivalent slip, equivalent speed ratio, and equivalent acceleration ratio of the training data set can be further calculated.

[0104] For example, in the first case, that is, the asynchronous motor is in a stable state, that is, the theoretical speed and the actual speed of the asynchronous motor are both constant values, the equivalent slip and the equivalent speed ratio of the asynchronous motor are calculated. In the second case, the asynchronous motor is in a non-steady state, that is, the theoretical speed and the actual speed of the asynchronous motor are not constant values, the equivalent acceleration ratio of the asynchronous motor is calculated. Among them, the equivalent slip is equal to the difference between the actual speed and the theoretical speed in the first case. The equivalent speed ratio is equal to the ratio between the actual speed and the theoretical speed in the first case. The equivalent acceleration ratio is equal to the ratio between the actual acceleration and the theoretical acceleration in the second case, that is, the ratio between the first-order difference of the actual speed and the first-order difference of the theoretical speed. The specific method for calculating the characteristic parameters corresponding to the training data set and the test data set will be combined below. Figure 4 Detailed description.

[0105] S330, calculating the health degree corresponding to the test data set according to the characteristic parameters corresponding to the training data set and the characteristic parameters corresponding to the test data set.

[0106] In a possible implementation, the distribution differences of the three characteristic parameters of the training data set and the test data set in each working scene can be calculated, such as the distribution differences of the equivalent slip, the distribution differences of the equivalent speed ratio, and the distribution differences of the equivalent acceleration ratio. Then, the distribution ratio of the characteristic parameter data of the training data set in each working scene within the preset confidence range in the characteristic parameter data of the entire training data set can be calculated, and the distribution ratio of the characteristic parameter data of the test data set in each working scene within the preset confidence range in the characteristic parameter data of the entire test data set can be calculated. Then, the distribution difference of the three characteristic parameters of the training data set and the test data set can be obtained according to each working scene; the distribution ratio of the characteristic parameters of the training data set in each working scene in the data of the entire training data set within the preset confidence range; the distribution ratio of the characteristic parameters of the test data set in each working scene in the data of the entire test data set within the preset confidence range; obtain the statistical difference between the equivalent slip of the training data set of all working scenes and the equivalent slip of the test data set, the statistical difference between the equivalent speed ratio of the training data set of all working scenes and the equivalent speed ratio of the test data set, and the statistical difference between the equivalent acceleration ratio of the training data set of all working scenes and the equivalent acceleration ratio of the test data set. Finally, the health of the asynchronous motor corresponding to the test data set can be obtained according to the weight of each characteristic parameter, the statistical difference between the equivalent slip of the training data set of all working scenes and the equivalent slip of the test data set, the statistical difference between the equivalent speed ratio of the training data set of all working scenes and the equivalent speed ratio of the test data set, and the statistical difference between the equivalent acceleration ratio of the training data set of all working scenes and the equivalent acceleration ratio of the test data set.

[0107] It can be understood that each working scene of the asynchronous motor has the same theoretical speed or the same average value of the theoretical speed, the same gear and the same torque; or each working scene has the same theoretical speed or the same average value of the theoretical speed, and the same torque; or each working scene has the same gear and the same torque. It can be understood that the working scene, that is, the principle of dividing the distribution area of ​​the characteristic parameters, is divided according to the same working conditions and other conditions, so that in the same working scene, each characteristic parameter in the training data set can be compared with the corresponding characteristic parameter in the test data set.

[0108] It can be understood that the data in the training data set can reflect the working state of the asynchronous motor in a healthy operating state. By comparing the characteristic parameters corresponding to the test data set with the characteristic parameters corresponding to the training data set, the working state corresponding to the data in the test data set can be obtained, and the difference from the healthy operating state of the asynchronous motor can be obtained, and the difference can be expressed through the numerical value of the health degree. In addition, regardless of any type of fault in the asynchronous motor, the health state of the asynchronous motor will be directly reflected in the equivalent slip, equivalent speed ratio, and equivalent acceleration ratio of the asynchronous motor. Therefore, by comparing the characteristic parameters such as the equivalent slip, equivalent speed ratio, and equivalent acceleration ratio of the asynchronous motor, the instantaneous health state of the asynchronous motor can be directly obtained, which improves the accuracy, efficiency, cost, and convenience of evaluating the health state of the asynchronous motor.

[0109] The specific method for calculating the health of the test data set will be combined below. Figure 4 Detailed description.

[0110] S340, storing the characteristic parameters corresponding to the training data set, the characteristic parameters corresponding to the test data set and the health level into the archive.

[0111] It can be understood that the characteristic parameters corresponding to the training data set, the characteristic parameters corresponding to the test data set and the health level can be stored in the archive, which can provide more accurate data support when the life prediction and degradation prediction of the asynchronous motor are performed later.

[0112] The above health evaluation method can obtain the difference between the working state corresponding to the data of the test data set and the working state corresponding to the data of the asynchronous motor training data set, that is, the healthy operating state of the asynchronous motor, by comparing the characteristic parameters of the test data set of the asynchronous motor with the characteristic parameters of the training data set, and intuitively express this difference through the numerical value of health, thereby obtaining the health of the test data set of the asynchronous motor, providing a more accurate reference for evaluating the health state of the asynchronous motor. In addition, by storing the characteristic parameters corresponding to the training data set, the characteristic parameters corresponding to the test data set and the health in the archive, a more accurate reference is provided for life prediction, degradation prediction, etc. of the asynchronous motor.

[0113] In some possible implementations, Figure 4 FIG. 2 shows a flow chart of another health assessment method. Figure 4 As shown, a health assessment method includes:

[0114] S410, obtaining a training data set and a test data set of current signals.

[0115] It can be understood that the method for obtaining the current signal can be based on the above Figure 3 Step S310 is not described again here.

[0116] S420, calculating the theoretical rotation speed.

[0117] It can be understood that the theoretical rotation speed of the asynchronous motor for each set of current signals in the training data set and the test data set can be calculated based on the data in the training data set and the test data set of the current signals acquired in step S410.

[0118] In a possible implementation, in the first case, the asynchronous motor is in a stable state, that is, the theoretical speed and the actual speed of the asynchronous motor are constant values, or the changes in the theoretical speed and the actual speed are both less than a first threshold value, for example, 0.1 / s. In the embodiment of the present application, the specific value of the first threshold value is not specifically limited.

[0119] For example, Figure 5A FIG. 1 shows a time domain schematic diagram of a set of current signals, and the acquisition time of this set of current signals is 2 seconds. Figure 5A The time domain schematic diagram of the current signal shown in the figure can be obtained by using methods such as short-time Fourier transform. Figure 5B The schematic diagram of the time-frequency analysis of the current signal shown in the figure can be obtained by using methods such as Fourier transform. Figure 5C The spectrum of the current signal is shown in Figure 1. Therefore, according to Figure 5C The spectrum of the current signal shown in the figure can be used to obtain the corresponding Figure 5AThe theoretical speed of the asynchronous motor obtained by the current signal, that is, the theoretical speed v0 = 15.0 Hz sent by the controller of the asynchronous motor.

[0120] It is understandable that other methods can also be used to calculate the theoretical speed of the asynchronous motor, and the theoretical speeds of the asynchronous motor calculated by different methods can be compared. If the theoretical speeds of the asynchronous motors calculated based on multiple methods are equal, or the difference between the theoretical speeds of the asynchronous motors is less than a preset threshold, then the final theoretical speed of the asynchronous motor can be determined. In the embodiment of the present application, the method for calculating the theoretical speed of the asynchronous motor and the rule for determining the theoretical speed of the asynchronous motor are not specifically limited.

[0121] S430, estimating the actual rotation speed fluctuation range.

[0122] It can be understood that the speed fluctuation range corresponding to each group of current signals of the asynchronous motor can be estimated based on the theoretical speed of the asynchronous motor corresponding to each group of current signals obtained in step S420. For example, if the theoretical speed v0 of the asynchronous motor is 15.0 Hz, then the speed fluctuation range of the asynchronous motor can be estimated to be [0, v0]. In the embodiment of the present application, the method for estimating the speed fluctuation range of the asynchronous motor is not specifically limited.

[0123] S440, obtaining a training data set and a test data set of vibration signals.

[0124] It can be understood that the method for obtaining the vibration signal can be based on the above Figure 3 Step S310 is not described again here.

[0125] S450, low-pass filter calculation.

[0126] It can be understood that if a vibration velocity sensor is used to obtain a vibration signal in step S440, since the sampling frequency of the vibration velocity sensor is low frequency and the sampling frequency is slightly different from the rotation frequency, the low-pass filtering calculation in step S450 may not be performed. If a vibration acceleration sensor is used to obtain a vibration signal in step S440, since the sampling frequency is high and the sampling frequency is significantly different from the rotation frequency, the sampled signal may include other more noise signals, so a low-pass filtering method may be used to remove the collected high-frequency signals and improve the accuracy of the vibration signals in the training data set and the test data set.

[0127] S460, calculating the actual rotation speed.

[0128] It is understandable that the actual rotation speed of each group of vibration signals in the training data set and the test data set can be calculated respectively according to the data in the training data set and the test data set of the vibration signals output in step S440 or step S450.

[0129] For example, Figure 5D The figure shows a time domain schematic diagram of a group of vibration signals obtained by using a vibration velocity sensor, and the acquisition time of this group of vibration signals is 2 seconds. Figure 5D The time domain diagram of the vibration signal shown in the figure can be obtained by using methods such as Fourier transform. Figure 5E The frequency spectrum of the vibration signal shown in the figure has a frequency range of [0, 50] Hz. Figure 5E The spectrum diagram of the vibration signal shown in FIG. 4 shows that, based on the actual speed fluctuation range of the asynchronous motor in step S420 being [0, v0], i.e., [0, 15] Hz, the maximum value can be obtained in the actual speed fluctuation range of the asynchronous motor, i.e., the value corresponding to Figure 5D The actual speed of the asynchronous motor with the vibration signal v1 = 13.3Hz. Then, according to the theoretical speed and actual speed of the asynchronous motor, Fig. 5F The schematic diagram of the comparison between the theoretical speed and the actual speed of the asynchronous motor is shown in FIG. It can be understood that the actual speed of the asynchronous motor is less than the theoretical speed.

[0130] S470, calculating an equivalent slip, an equivalent speed ratio, and an equivalent acceleration ratio.

[0131] It can be understood that the equivalent slip, equivalent speed ratio and equivalent acceleration ratio of each corresponding current signal and vibration signal in the training data set and the test data set can be calculated respectively based on the actual speed in step S460 and the theoretical speed in step S420.

[0132] In the first case, the asynchronous motor is in a stable state, that is, the theoretical speed and actual speed of the asynchronous motor are constant. According to the equivalent slip equal to the difference between the actual speed and the theoretical speed. Then, the equivalent slip corresponding to the theoretical speed v0 is ε v =1.70Hz.

[0133] According to the equivalent speed ratio of the asynchronous motor, it is equal to the ratio between the actual speed of the asynchronous motor and the theoretical speed sent by the controller of the asynchronous motor. It can be obtained that the equivalent speed ratio of the asynchronous motor is r v =v1 / v0=88.67%.

[0134] In the second case, the asynchronous motor outputs a non-steady speed, that is, the actual speed of the asynchronous motor changes, for example, the change of the actual speed per unit time is greater than or equal to the first threshold.

[0135] For example, Figure 5G FIG. 2 shows a time domain schematic diagram of another set of current signals, the acquisition time of this set of current signals is 4 seconds. Figure 5G , which can be obtained by Fourier transform method Figure 5H Time-frequency diagram of an asynchronous motor. Fig.5I FIG. 2 shows a time domain schematic diagram of another set of vibration signals, the collection time of this set of vibration signals is 4 seconds. Fig.5I , which can be obtained by Fourier transform method Figure 5J Time-frequency diagram of an asynchronous motor.

[0136] according to Figure 5H , we can get the theoretical speed v0 of the asynchronous motor and its average value The average theoretical speed according to Figure 5J , the actual speed v1 of the motor can be obtained. By performing first-order difference calculations on the theoretical speed v0 and actual speed v1 of the asynchronous motor, the theoretical acceleration a0 and actual acceleration a1 of the asynchronous motor can be obtained, where the theoretical acceleration a0 = v0 / Δt, the actual acceleration a1 = v1 / Δt, and t is Figure 5G Medium current signal and Fig.5I Then, according to the equivalent acceleration ratio, which is equal to the ratio between the actual acceleration of the asynchronous motor and the theoretical acceleration sent by the controller of the asynchronous motor, the equivalent acceleration ratio r of the asynchronous motor can be obtained. a =a1 / a0.

[0137] S480, calculating the mean square value of the vibration signal, and using the mean square value as the torque.

[0138] In a possible implementation, the torque may be acquired by acquiring a torque signal z(t) collected by a torque sensor, where t is a sampling duration.

[0139] In another possible implementation, the torque may be obtained by calculating the mean square value of the vibration signal of the asynchronous motor.

[0140] S490, calculating the equivalent slip, equivalent speed ratio and equivalent acceleration ratio of the data of the training data set in each distribution area, and their distribution proportions within a preset confidence range.

[0141] In a possible implementation, the data of equivalent slip, equivalent speed ratio and equivalent acceleration ratio of the training data set and the test data set of the asynchronous motor can be divided into several distribution areas according to the gear position, torque, theoretical speed in a steady state, and the average value of theoretical speed in an unsteady state of the asynchronous motor. For example, each distribution area of ​​the equivalent slip of the training data set of the asynchronous motor has the same theoretical speed or the average value of the theoretical speed, the same gear position and the same torque. Alternatively, each distribution area of ​​the equivalent slip of the training data set of the asynchronous motor has the same theoretical speed or the average value of the theoretical speed, and the same torque. Alternatively, each distribution area of ​​the equivalent slip of the training data set of the asynchronous motor has the same theoretical speed or the average value of the theoretical speed, and the same torque. Alternatively, each distribution area of ​​the equivalent slip of the training data set of the asynchronous motor has the same gear position and the same torque.

[0142] It can be understood that the principle of dividing the distribution area is based on dividing the data of the three characteristic parameters of equivalent slip, equivalent speed ratio and equivalent acceleration ratio in the training data set and test data set of the asynchronous motor according to the same working scene. The same distribution area is also the same working scene. Therefore, the working state of the asynchronous motor can be evaluated in the same working scene. In the embodiment of the present application, the principle of dividing the distribution area of ​​the asynchronous motor is not specifically limited.

[0143] In a possible implementation, if the distribution area is divided according to the same theoretical speed or the average value of the theoretical speed, the same gear and the same torque, assuming that the torque of the asynchronous motor is z(t), the gear is i, and i is a natural number, the torque parameter can be recorded as z i , a set of torque z within a sampling period i =mean(z(t)), where mean(·) represents the averaging operation.

[0144] Then, in a steady state, the theoretical speed is v0 and the torque is z i In the distribution area, Figure 5A Medium current signal and Figure 5D The equivalent slip of the asynchronous motor corresponding to the vibration signal in can be expressed as The equivalent speed ratio of the asynchronous motor is The equivalent acceleration ratio of the asynchronous motor is

[0145] In the unsteady state, the average value of the theoretical speed is v0 and the torque is z i In the distribution area, Figure 5G Medium current signal and Fig.5I The equivalent acceleration ratio of the asynchronous motor corresponding to the vibration signal in is

[0146] It can be understood that the distribution center of each distribution area can be obtained according to several distribution areas of the equivalent slip, the equivalent speed ratio and the equivalent acceleration ratio respectively.

[0147] For example, the distribution center of the distribution area of ​​the equivalent slip of the data in the training data set can be expressed as The distribution center of the distribution area of ​​the equivalent speed ratio of the training data set can be expressed as The distribution center of the distribution area of ​​the equivalent speedup ratio of the training data set can be expressed as k represents the sequence number of the distribution area, and k is a natural number.

[0148] Since the equivalent slip, equivalent speed ratio and equivalent acceleration ratio conform to the normal distribution, the preset confidence range can be set to any one of the following: [-3σ, 3σ] range, [-2σ, 2σ] range, [-σ, σ], where σ is the standard deviation of each characteristic parameter, such as the equivalent slip, equivalent speed ratio and equivalent acceleration ratio. The data in each distribution area of ​​the equivalent slip, equivalent speed ratio and equivalent acceleration ratio of the training data set, the distance from the distribution center of each distribution area, the number of samples in the preset confidence range, and the distribution proportion of the total number of samples in each distribution area can be counted respectively.

[0149] Among them, the data in each distribution area of ​​the equivalent transition of the data in the training data set, the distance from the distribution center of each distribution area, the number of samples within the preset confidence range, and the distribution ratio of the total number of samples can be expressed as The data in each distribution area of ​​the equivalent speed ratio of the data in the training data set, the distance from the distribution center of each distribution area, the number of samples within the preset confidence range, and the distribution ratio of the total number of samples can be expressed as The data in each distribution area of ​​the equivalent speedup ratio of the data in the training data set, the distance from the distribution center of each distribution area, the number of samples within the preset confidence range, and the distribution ratio of the total number of samples can be expressed as

[0150] S500, calculating the equivalent slip, equivalent speed ratio and equivalent acceleration ratio of the data of the test data set in each distribution area, and their distribution proportions within a preset confidence range.

[0151] It can be understood that a method similar to step S490 can be used, and the distribution center of the distribution area of ​​the equivalent slip of the data of the test data set can be expressed as The distribution center of the distribution area of ​​the equivalent speed ratio of the test data set can be expressed as The distribution center of the distribution area of ​​the equivalent speedup ratio of the test data set can be expressed as k represents the serial number of the distribution center, and k is a natural number.

[0152] In addition, the same preset confidence range as the training data set can be obtained to calculate the data in each distribution area of ​​the equivalent slip of the data in the test data set, the distance from the distribution center of each distribution area, the number of samples within the preset confidence range, and the distribution ratio of the total number of samples in each distribution area. The data in each distribution area of ​​the equivalent speed ratio of the data in the test data set, the distance from the distribution center of each distribution area, the number of samples within the preset confidence range, and the distribution ratio of the total number of data samples in each distribution area The data in each distribution area of ​​the equivalent speedup ratio of the test data set, the distance from the distribution center of each distribution area, the number of samples within the preset confidence range, and the distribution ratio of the total number of data samples in each distribution area

[0153] S510, calculating the health degree corresponding to the test data set.

[0154] Specifically, the distribution difference of the equivalent slip of the training data set and the test data set in each distribution area, that is, the distance between the two distribution centers, can be calculated according to the results in step S490 and step S500. The distribution difference of the equivalent speed ratio of the training data set and the test data set in each distribution area, that is, the distance between the two distribution centers And the distribution difference of the equivalent speedup ratio of the training data set and the test data set in each distribution area, that is, the distance between the two distribution centers

[0155] Then, the statistical difference of the equivalent slip between the training dataset and the test dataset in all distribution areas, the statistical difference of the equivalent speed ratio between the training dataset and the test dataset in all distribution areas, and the statistical difference of the equivalent acceleration ratio between the training dataset and the test dataset in all distribution areas are calculated.

[0156] Among them, the statistical difference of the equivalent shift of the training data set and the test data set in all distribution areas is:

[0157]

[0158] The statistical difference of the equivalent speed ratio between the training dataset and the test dataset in all distribution areas is:

[0159]

[0160] The statistical difference of the equivalent speedup ratio of the training dataset and the test dataset in all distribution areas is:

[0161]

[0162] Then, the above results are substituted into the health formula to obtain the health of the asynchronous motor corresponding to the test data set.

[0163]

[0164] Among them, health is expressed as score, ω v With ω a is the weight coefficient of equivalent speed ratio and equivalent acceleration ratio, ω v +ω a =1. ω can be adjusted according to actual usage. v,ω a , for example, setting ω v =ω a =0.5. k is the weight coefficient of equivalent slip, generally, You can also adjust according to the actual situation. Need to meet In the embodiment of the present application, for the specific values ​​of the weight coefficients of the equivalent speed ratio and the equivalent acceleration ratio, for λ k The specific value of is not limited.

[0165] It can be understood that when the health degree is greater than or equal to 0.85, the health state of the asynchronous motor is evaluated to be good; when the health degree is less than 85 and greater than or equal to 0.60, the health state of the asynchronous motor needs to be monitored; when the health degree is less than 60, the health state of the asynchronous motor is evaluated to be poor, that is, a fault occurs and maintenance is required. In the embodiments of the present application, the above settings are not specifically limited.

[0166] S520, a file is created according to the distribution area, the equivalent slip, the equivalent speed ratio, the equivalent acceleration ratio and the health level.

[0167] In the above health assessment method, characteristic parameters corresponding to the training data set and the test data set for characterizing the speed state of the asynchronous motor are calculated respectively. For example, the characteristic parameters may include equivalent slip, equivalent speed ratio and equivalent acceleration ratio. Then, the characteristic parameters of the training data set and the test data set in each distribution area are compared to obtain the distribution difference of the characteristic parameters of the training data set and the test data set in each distribution area, as well as the statistical difference of the characteristic parameters of the training data set and the test data set in all distribution areas. Finally, according to the statistical difference of the characteristic parameters of the training data set and the test data set in all distribution areas, as well as the weight of each characteristic parameter, the specific value of the health of the asynchronous motor corresponding to the test data set is obtained.

[0168] It can be understood that any type of failure of the asynchronous motor may directly cause a change in the output state of the asynchronous motor, thereby affecting the normal use of the asynchronous motor. Therefore, the above health assessment method can more accurately assess the health of the asynchronous motor's immediate operating state by comparing the characteristic parameters of the output state of the asynchronous motor corresponding to the training data set when it is in a healthy operating state, and the characteristic parameters of the output state of the asynchronous motor corresponding to the test data set when it is in an immediate operating state. In addition, by comparing the characteristic parameters of the asynchronous motor speed, a more accurate comparison result of the asynchronous motor's immediate health state and healthy operating state can be obtained even when the application scenario is not comprehensive or the amount of data is insufficient.

[0169] Moreover, by dividing the above parameters into different files for storage, it also provides accurate and rich reference basis for subsequent life prediction, degradation prediction, etc. of the asynchronous motor.

[0170] It can be understood that the electronic device 100 may include but is not limited to user equipment (UE), terminal, etc. For example, the electronic device may be a portable android device (PAD), a personal digital assistant (PDA), a handheld device with wireless communication function, a computing device, a vehicle-mounted device or a wearable device, a virtual reality (VR) terminal device, an augmented reality (AR) terminal device, a wireless terminal in industrial control, a wireless terminal in self-driving, a wireless terminal in remote medical, a wireless terminal in smart grid, a wireless terminal in transportation safety, a wireless terminal in smart city, a wireless terminal in smart home, and other mobile terminals or fixed terminals. In the embodiments of the present application, the form of the terminal device is not specifically limited.

[0171] Combine the following Figure 6 , the hardware structure of the electronic device 100 in the embodiment of the present application is described.

[0172] Figure 6 According to some embodiments of the present application, a hardware structure diagram of an electronic device 100 is shown. Figure 6 The electronic device 100 shown includes one or more processors 101, a system memory 102, a non-volatile memory (NVM) 103, a communication interface 104, an input / output (I / O) device 105, and a system control logic 106 for coupling the processor 101, the system memory 102, the non-volatile memory 103, the communication interface 104 and the input / output (I / O) device 105.

[0173] Wherein: the processor 101 may include one or more processing units, for example, it may include a central processing unit (CPU), an application processor (AP), a modem processor, a graphics processing unit (GPU), an image signal processor (ISP), a digital signal processor (DSP), a neural network processor (NPU), a microprocessor (MCU), a baseband processor, an artificial intelligence (AI) processor or, a processing module or processing circuit of a programmable logic device (field programmable gate array, FPGA) or other image processing devices including the present application, a processor that implements the above-mentioned image processing method, wherein different processing units may be independent devices or may be integrated in one or more processors. The specific processor type is not specifically required here. The processor 101 can be used to execute the health assessment method of the embodiment of the present application.

[0174] The system memory 102 is a volatile memory, such as a random-access memory (RAM), a double data rate synchronous dynamic random access memory (DDR SDRAM), etc. The system memory is used to temporarily store data and / or instructions.

[0175] The non-volatile memory 103 may include one or more tangible, non-transitory computer-readable media for storing data and / or instructions. In some embodiments, the non-volatile memory 103 may include any suitable non-volatile memory such as a flash memory and / or any suitable non-volatile storage device, such as a hard disk drive (HDD), a compact disc (CD), a digital versatile disc (DVD), a solid-state drive (SSD), etc. In some embodiments, the non-volatile memory 103 may also be a removable storage medium, such as a secure digital (SD) memory card, etc.

[0176] In particular, the system memory 102 and the non-volatile memory 103 may respectively include: a temporary copy and a permanent copy of the instruction 107. The instruction 107 may include: being executed by at least one of the processors 101 to enable the electronic device 100 to implement the model detection method provided by each embodiment of the present application.

[0177] In some embodiments, the processor 101 can call and execute the execution instructions of the model detection provided by the embodiments of the present application stored in the system memory 102 and the non-volatile memory 103 to implement the health assessment method provided by the embodiments of the present application.

[0178] The communication interface 104 may include a transceiver for providing a wired or wireless communication interface for the electronic device 100, thereby communicating with any other suitable device through one or more networks. In some embodiments, the communication interface 104 may be integrated into other components of the electronic device 100, for example, the communication interface 104 may be integrated into the processor 101. In some embodiments, the electronic device 100 may communicate with other devices through the communication interface 104.

[0179] The communication interface 104 may include an inter-integrated circuit (I2C) interface, an inter-integrated circuit sound (I2S) interface, a pulse code modulation (PCM) interface, a universal asynchronous receiver / transmitter (UART) interface, a mobile industry processor interface (MIPI), a general-purpose input / output (GPIO) interface, a subscriber identity module (SIM) interface, and / or a universal serial bus (USB) interface, etc.

[0180] The MIPI interface can be used to connect the processor 101 with peripheral devices such as a display screen and a camera. The MIPI interface includes a camera serial interface (CSI), a display serial interface (DSI), etc. In some embodiments, the processor 101 and the camera communicate via the CSI interface to realize the shooting function of the electronic device 100. The processor 101 and the display screen communicate via the DSI interface to realize the display function of the electronic device 100.

[0181] The GPIO interface can be configured by software. The GPIO interface can be configured as a control signal or as a data signal. In some embodiments, the GPIO interface can be used to connect the processor 110 with a camera, a display screen, a wireless communication module, an audio module, a sensor module, etc. The GPIO interface can also be configured as an I2C interface, an I2S interface, a UART interface, a MIPI interface, etc.

[0182] The USB interface is an interface that complies with the USB standard specification, and specifically can be a Mini USB interface, a Micro USB interface, a USB Type C interface, etc. The USB interface can be used to connect a charger to charge the electronic device 100, and can also be used to transmit data between the electronic device 100 and a peripheral device. The interface can also be used to connect other electronic devices.

[0183] It is understandable that the interface connection relationship between the modules illustrated in the embodiment of the present application is only a schematic illustration and does not constitute a structural limitation on the electronic device 100. In other embodiments of the present application, the electronic device 100 may also adopt different interface connection methods in the above embodiments, or a combination of various interface connection methods.

[0184] The input / output (I / O) device 105 may include input devices such as a keyboard, a mouse, etc., and output devices such as a display, etc. A user may interact with the electronic device 100 via the input / output (I / O) device 105 .

[0185] System control logic 106 may include any suitable interface controller to provide any suitable interface with other modules of terminal 100. For example, in some embodiments, system control logic 106 may include one or more memory controllers to provide interfaces to system memory 102 and non-volatile memory 103.

[0186] In some embodiments, at least one of the processors 101 may be packaged together with the logic of one or more controllers for the system control logic 106 to form a system in package (System in Package, SiP). In other embodiments, at least one of the processors 101 may also be integrated with the logic of one or more controllers for the system control logic 106 on the same chip to form a system-on-chip (System-on-Chip, SoC). The system control logic 106 may generate an operation control signal based on the instruction opcode and the timing signal to complete the control of fetching and executing instructions.

[0187] although Figure 6 Not shown, the electronic device 100 may further include a Bluetooth device, an external memory interface for connecting an external memory card, a near field communication (NFC) device, etc., which are not described in detail here.

[0188] Understandably, Figure 6 The structure of the electronic device 100 shown is only an example. In other embodiments, the electronic device 100 may include more or fewer components than shown, or combine some components, or separate some components, or arrange the components differently. The components shown in the figure may be implemented in hardware, software, or a combination of software and hardware.

[0189] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0190] It will be apparent to those skilled in the art that some of the specific details presented above regarding the electronic device 100 may not be necessary for practicing a specific described embodiment or its equivalent. Similarly, other electronic devices may include a greater number of subsystems, modules, components, etc. Where appropriate, some submodules may be implemented as software or hardware. Therefore, it should be understood that the above description is not intended to be exhaustive or to limit the present disclosure to the precise form described herein. On the contrary, it will be apparent to those skilled in the art that, based on the above teachings, many modifications and variations are possible.

[0191] The various embodiments disclosed in the present application may be implemented in hardware, software, firmware, or a combination of these implementation methods. The embodiments of the present application may be implemented as a computer program or program code executed on a programmable system, the programmable system including at least one processor, a storage system (including volatile and non-volatile memory and / or storage elements), at least one input device, and at least one output device.

[0192] Program code can be applied to input instructions to perform the functions described in this application and generate output information. The output information can be applied to one or more output devices in a known manner. For the purposes of this application, a processing system includes any system having a processor such as, for example, a digital signal processor (DSP), a microcontroller, an application specific integrated circuit (ASIC), or a microprocessor.

[0193] Program code can be implemented with high-level programming language or object-oriented programming language to communicate with the processing system. When necessary, program code can also be implemented with assembly language or machine language. In fact, the mechanism described in this application is not limited to the scope of any specific programming language. In either case, the language can be a compiled language or an interpreted language.

[0194] In some cases, the disclosed embodiments may be implemented in hardware, firmware, software, or any combination thereof. The disclosed embodiments may also be implemented as instructions carried or stored on one or more temporary or non-temporary machine-readable (e.g., computer-readable) storage media, which may be read and executed by one or more processors. For example, instructions may be distributed over a network or through other computer-readable media. Therefore, a machine-readable medium may include any mechanism for storing or transmitting information in a machine (e.g., computer) readable form, including, but not limited to, a floppy disk, an optical disk, an optical disk, a magneto-optical disk, a read-only memory (ROM), a random access memory (RAM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a magnetic card or an optical card, a flash memory, or a tangible machine-readable memory for transmitting information (e.g., a carrier wave, an infrared signal, a digital signal, etc.) using the Internet in an electrical, optical, acoustic, or other form of propagation signal. Accordingly, machine-readable media include any type of machine-readable media suitable for storing or transmitting electronic instructions or information in a form readable by a machine (eg, a computer).

[0195] In the accompanying drawings, some structural or method features may be shown in a specific arrangement and / or order. However, it should be understood that such a specific arrangement and / or order may not be required. Instead, in some embodiments, these features may be arranged in a manner and / or order different from that shown in the illustrative drawings. In addition, the inclusion of structural or method features in a particular figure does not mean that such features are required in all embodiments, and in some embodiments, these features may not be included or may be combined with other features.

[0196] It should be noted that the units / modules mentioned in the various device embodiments of the present application are all logical units / modules. Physically, a logical unit / module can be a physical unit / module, or a part of a physical unit / module, or can be implemented as a combination of multiple physical units / modules. The physical implementation method of these logical units / modules themselves is not the most important. The combination of functions implemented by these logical units / modules is the key to solving the technical problems proposed by the present application. In addition, in order to highlight the innovative part of the present application, the above-mentioned device embodiments of the present application do not introduce units / modules that are not closely related to solving the technical problems proposed by the present application, which does not mean that there are no other units / modules in the above-mentioned device embodiments.

[0197] It should be noted that, in the examples and description of the present application, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that the process, method, article or equipment including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or equipment. In the absence of further restrictions, the elements defined by the statement "comprise one" do not exclude the presence of other identical elements in the process, method, article or equipment including the elements.

[0198] Although the present application has been illustrated and described with reference to certain preferred embodiments thereof, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the scope of the present application.

Claims

1. A health assessment method, applied to electronic equipment, characterized in that: include: Acquire a first speed characteristic parameter of the motor within a first time period; The health of the motor during the first time period is determined according to a difference between the first speed characteristic parameter and a standard speed characteristic parameter.

2. The method according to claim 1, characterized in that The first speed characteristic parameters include the equivalent slip within the first time length, the equivalent speed ratio within the first time length, and the equivalent acceleration ratio within the first time length; the standard speed characteristic parameters include the equivalent slip within the second time length, the equivalent speed ratio within the second time length, and the equivalent acceleration ratio within the second time length, and the second time length is any period of time that the motor is in a healthy operating state.

3. The method according to claim 2, characterized in that The equivalent slip is determined based on the difference between the actual speed of the motor and the theoretical speed of the motor in the first case; The equivalent speed ratio is determined based on the ratio between the actual speed of the motor and the theoretical speed of the motor in the first case; The equivalent acceleration ratio is determined based on the ratio between the actual acceleration of the motor and the theoretical acceleration sent by the controller of the motor in the second case; The first situation includes a situation where the theoretical speed of the motor and the actual speed of the motor are constant values, and the second situation includes a situation where the theoretical speed of the motor and the actual speed of the motor are not constant values.

4. The method according to claim 1, characterized in that: The determining, according to the difference between the first speed characteristic parameter and the standard speed characteristic parameter, the health of the motor during the first time period includes: Obtaining a distribution difference between the first speed characteristic parameter and the standard speed characteristic parameter in each working scene; Obtaining data of a first speed characteristic parameter in each working scene within a first confidence range, and a distribution ratio of the data of the first speed characteristic parameter in all working scenes; Obtaining the health of the motor during the first duration according to the distribution difference and the distribution proportion; Among them, each of the working scenes is a scene of the same working condition.

5. The method according to claim 4, characterized in that The same working condition includes: have the same theoretical speed or the same average value of the theoretical speed, the same gear and the same torque; or have the same theoretical speed or the same average value of the theoretical speed, and the same torque; or Same gears and same torque.

6. The method according to claim 4, characterized in that The obtaining of the distribution difference between the first speed characteristic parameter and the standard speed characteristic parameter in each working scene includes: Obtaining, in each working scenario, a distribution center of an equivalent slip within the first time period, a distribution center of an equivalent speed ratio within the first time period, and a distribution center of an equivalent acceleration ratio within the first time period corresponding to the first speed characteristic parameter; Obtaining, in each working scenario, a distribution center of an equivalent slip within a second time length, a distribution center of an equivalent speed ratio within the second time length, and a distribution center of an equivalent acceleration ratio within the second time length corresponding to the standard speed characteristic parameter; Obtaining, in each working scene, a first distance between a distribution center of the equivalent slip within the first duration and a distribution center of the equivalent slip within the second duration; Obtaining, in each working scene, a second distance between a distribution center of the equivalent speed ratio within the first time length and a distribution center of the equivalent speed ratio within the second time length; Obtain, in each working scene, a third distance between a distribution center of the equivalent speedup ratio within the first duration and a distribution center of the equivalent speedup ratio within the second duration.

7. The method according to claim 6, characterized in that The obtaining of the data of the first speed characteristic parameter in each working scene within the first confidence range and the distribution ratio of the data of the first speed characteristic parameter in all working scenes includes: Obtaining, in each working scene, data of equivalent slip within the first duration, the distance from the distribution center of each working scene, the number of samples within the first confidence range, and a first proportion of the total number of samples of equivalent slip data within the first duration in each working scene; Obtaining data of the equivalent speed ratio within the first duration in each working scene, the distance from the distribution center of each working scene, the number of samples within the first confidence range, and the second proportion of the total number of samples of the equivalent speed ratio within the first duration in each working scene; Obtain the data of the equivalent acceleration ratio within the first time length in each work scene, the distance from the distribution center of each work scene, the number of samples within the first confidence range, and the third proportion of the total number of samples of the data of the equivalent acceleration ratio within the first time length in each work scene.

8. The method according to claim 7, characterized in that The obtaining the health of the motor in the first time period according to the distribution difference and the distribution proportion includes: According to the first distance and the first proportion in each working scene, obtain a first statistical difference between an equivalent slip within a first duration and an equivalent slip within a second duration of all working scenes; According to the second distance and the second proportion in each working scene, obtain a second statistical difference between the equivalent speed ratio within the first duration and the equivalent speed ratio within the second duration of all working scenes; Obtain, according to the third distance and the third proportion in each work scene, a third statistical difference between the equivalent acceleration ratio within the first duration and the equivalent acceleration ratio within the second duration of all work scenes; The health of the motor in the first duration is obtained based on the first statistical difference, the second statistical difference, the third statistical difference, and the weight coefficients corresponding to the equivalent slip, the weight coefficients corresponding to the equivalent speed ratio, and the weight coefficients corresponding to the equivalent acceleration ratio.

9. The method according to claim 4, characterized in that The first confidence range includes: any one of: [-3σ, 3σ] range, [-2σ, 2σ] range, [-σ, σ] range, wherein σ is the standard deviation of each parameter in the first rotation speed characteristic parameter.

10. A computer-readable storage medium, characterized in that: The readable storage medium stores instructions, which, when executed on an electronic device, enable the electronic device to implement the method according to any one of claims 1 to 9.

11. An electronic device, characterized in that: include: a memory for storing instructions to be executed by one or more processors of the electronic device; And a processor, which is one of the processors of the electronic device, configured to execute instructions stored in the memory to implement the method according to any one of claims 1 to 9.

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