A method and system for online monitoring of the health status of a carbon brush of an electric machine

Abstract

The present application relates to the technical field of motor testing, and particularly relates to a kind of carbon brush health state online monitoring method and system of motor, the method comprises: according to the load condition and operating environment condition of target carbon brush in current operating period obtained in advance, determine target carbon brush target operating load environment cluster under current time;Filter out reference operating period;Determine the abnormal wear factor and potential abnormal wear feature vector of target carbon brush under current time;According to the Lyapunov exponent of vibration signal of target carbon brush in current operating period, and temperature change and current change of target carbon brush in current operating period, determine wear failure possible feature vector;Thus determine the health state of target carbon brush under current time.The present application considers the potential abnormal wear condition of target carbon brush under current time, realizes carbon brush health state monitoring, and improves the timeliness of carbon brush abnormal health state monitoring.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of motor testing, in particular to a carbon brush health state online monitoring method and system of a motor. BACKGROUND

[0002] Brushed motors are widely used in industrial equipment, transportation and household appliances due to their simple structure, low cost and convenient control. As a core component, carbon brushes bear the key functions of current conduction and commutation. In continuous operation, carbon brushes and commutators are prone to wear and tear due to mechanical friction, which may cause poor contact, abnormal sparks, efficiency decline and even shutdown failure. Therefore, real-time monitoring of carbon brush health state is of great significance to ensure stable operation of the motor, prolong the service life of the equipment and reduce maintenance costs. Currently, when monitoring the health state of carbon brushes, the method commonly used is to determine whether the running state of the carbon brush is abnormal by whether there is an abnormal spark. If there is an abnormal spark, especially a ring fire, it is determined that the health state of the carbon brush is abnormal.

[0003] However, when determining whether the running state of the carbon brush is abnormal by whether there is an abnormal spark, the following technical problems often exist:

[0004] In actual situations, abnormal sparks are a clear signal of faults in carbon brushes or motor systems, which can rapidly accelerate the ablation of carbon brushes and commutators, and often need to be handled immediately. That is, when an abnormal spark is detected, it usually means that the carbon brush has already appeared abnormal wear and tear, which may have caused local damage to the motor. Therefore, when monitoring the health state of carbon brushes based on whether there is an abnormal spark, it is often difficult to detect potential wear and tear risks of carbon brushes, resulting in poor timeliness of abnormal health state monitoring of carbon brushes. SUMMARY

[0005] In order to solve the technical problem of poor timeliness of abnormal health state monitoring of carbon brushes, the present application provides a carbon brush health state online monitoring method and system of a motor.

[0006] In the first aspect, the present application provides a carbon brush health state online monitoring method of a motor, which comprises:

[0007] According to the load condition and the running environment condition of the target carbon brush in the current running period obtained in advance, the target running load environment cluster of the target carbon brush at the current time is determined, wherein the current time is the end time of the current running period;

[0008] The historical running period belonging to the target running load environment cluster is selected from all historical running periods of all historical carbon brushes as a reference running period;

[0009] determine an abnormal wear factor of the target carbon brush at the current moment according to the overall wear amount of the target carbon brush in the current operation period and the overall wear amount of the historical carbon brush in the reference operation period of the historical carbon brush;

[0010] determine a potential abnormal wear feature vector of the target carbon brush at the current moment according to the current ripple time domain signal of the target carbon brush in the current operation period, the compression force and the abnormal wear factor of the target carbon brush at the current moment;

[0011] determine a wear failure emerging possible feature vector of the target carbon brush at the current moment according to the Lyapunov exponent of the vibration signal of the target carbon brush in the current operation period, and the temperature change and the current change of the target carbon brush in the current operation period;

[0012] determine the health state of the target carbon brush at the current moment according to the potential abnormal wear feature vector and the wear failure emerging possible feature vector.

[0013] In a possible implementation manner of the first aspect, the target operation load environment cluster of the target carbon brush at the current moment is determined according to the load condition and the operation environment condition of the target carbon brush in the current operation period.

[0014] construct a load environment feature vector of each historical operation period of each historical carbon brush according to the load rate at each moment in each historical operation period of each historical carbon brush and the dimension data in different preset environment dimensions;

[0015] cluster all historical operation periods of all historical carbon brushes according to the load environment feature vectors of all historical operation periods of all historical carbon brushes to obtain initial clustering clusters;

[0016] construct a load environment feature vector of the current operation period of the target carbon brush as a current load environment feature vector according to the load rate at each moment in the current operation period of the target carbon brush and the dimension data in different preset environment dimensions;

[0017] determine a reference distance corresponding to each initial clustering cluster as the Euclidean distance between the current load environment feature vector and the clustering center of each initial clustering cluster;

[0018] select an initial clustering cluster corresponding to the smallest reference distance from all initial clustering clusters as the target operation load environment cluster of the target carbon brush at the current moment.

[0019] In a possible implementation manner of the first aspect, the load environment feature vector of each historical running time period of each historical carbon brush is constructed according to the load rate of each historical carbon brush at all time points in each historical running time period and the dimension data of each historical carbon brush in different preset environmental dimensions, and the load environment feature vector of the target carbon brush in the current time point is constructed according to the carbon brush overall wear amount of the target carbon brush in the current running time period and the carbon brush overall wear amount of the historical carbon brush in the reference running time period.

[0020] Any one of the historical carbon brushes is determined as a marked historical carbon brush, and any one of the historical running time periods of the marked historical carbon brush is determined as a marked historical running time period.

[0021] A time sequence formed by the load rates of the marked historical carbon brush at all time points in the marked historical running time period is recorded as a load rate sequence corresponding to the marked historical running time period.

[0022] A time sequence formed by the dimension data of the marked historical carbon brush in the same preset environmental dimension at all time points in the marked historical running time period is recorded as a dimension data sequence of the marked historical running time period in the preset environmental dimension.

[0023] The mean value, the standard deviation, and the number of maximum values of the load rate sequence corresponding to the marked historical running time period, and the mean value of the dimension data sequence of the marked historical running time period in the same preset environmental dimension, constitute the load environment feature vector of the marked historical carbon brush in the marked historical running time period.

[0024] In a possible implementation manner of the first aspect, the abnormal loss factor of the target carbon brush in the current time point is determined according to the carbon brush overall wear amount of the target carbon brush in the current running time period and the carbon brush overall wear amount of the historical carbon brush in the reference running time period.

[0025] The difference between the length of the target carbon brush at the start time point of the current running time period and the length of the target carbon brush at the end time point of the current running time period is determined as the carbon brush overall wear amount of the target carbon brush in the current running time period, as the current carbon brush overall wear amount.

[0026] Similarly, the difference between the length of the historical carbon brush at the start time point of the reference running time period and the length of the historical carbon brush at the end time point of the reference running time period is determined as the carbon brush overall wear amount of the historical carbon brush in the reference running time period, as the reference carbon brush overall wear amount.

[0027] The probability density function of all the reference carbon brush overall wear amounts is determined, and the reference carbon brush overall wear amount corresponding to the maximum function value of the probability density function is determined as the standard carbon brush overall wear amount.

[0028] The difference between the current carbon brush overall wear amount and the standard carbon brush overall wear amount is normalized to obtain the initial abnormal factor of the target carbon brush in the current time point.

[0029] A sum value between the constant 1 and the initial abnormality factor is determined as the abnormal loss factor of the target carbon brush at the current moment.

[0030] In combination with the first aspect, in a possible implementation, the determining, according to the pre-acquired current ripple time domain signal of the target carbon brush in the current running period and the compression force and the abnormal loss factor of the target carbon brush at the current moment, of the potential abnormal wear feature vector of the target carbon brush at the current moment comprises:

[0031] The absolute value of the difference between the compression force of the target carbon brush at the current moment and the pre-acquired standard compression force is normalized to obtain a force deviation factor;

[0032] A sum value between the constant 1 and the force deviation factor is determined as a target force deviation index;

[0033] A product between the target force deviation index and the abnormal loss factor is determined as an abnormal amplification factor;

[0034] The current ripple time domain signal of the target carbon brush in the current running period is acquired;

[0035] The current ripple time domain signal is wavelet packet decomposed to decompose the signal into 8 characteristic frequency bands, and an energy proportion of each characteristic frequency band is determined, wherein the characteristic frequency bands are ordered, and the higher the characteristic frequency band is, the higher the frequency is;

[0036] A sum value between a preset positive factor and the energy proportion of each characteristic frequency band is determined as an energy representative value corresponding to each characteristic frequency band;

[0037] According to the abnormal amplification factor and the energy representative values corresponding to the first three characteristic frequency bands, a micro-discharge intensity target performance value is determined;

[0038] According to a sum value between the energy representative value corresponding to the fourth characteristic frequency band and the energy representative value corresponding to the fifth characteristic frequency band, and the abnormal amplification factor, a carbon brush elastic deformation recovery abnormality index is determined;

[0039] According to a ratio between the energy representative value corresponding to the sixth characteristic frequency band and the energy representative value corresponding to the seventh characteristic frequency band, and the abnormal amplification factor, a material carbonization abnormality index is determined;

[0040] The micro-discharge intensity target performance value, the carbon brush elastic deformation recovery abnormality index, and the material carbonization abnormality index constitute the potential abnormal wear feature vector of the target carbon brush at the current moment.

[0041] In a possible implementation manner of the first aspect, the determining the micro-discharge intensity target performance value according to the abnormal amplification factor and the energy representative values corresponding to the first three characteristic frequency bands comprises:

[0042] determining a sum value between the energy representative value corresponding to the first characteristic frequency band and the energy representative value corresponding to the second characteristic frequency band as a normal contact friction current ripple performance value;

[0043] determining a ratio between the energy representative value corresponding to the third characteristic frequency band and the normal contact friction current ripple performance value as a micro-discharge intensity initial performance value;

[0044] determining a product between the abnormal amplification factor and the micro-discharge intensity initial performance value as the micro-discharge intensity target performance value.

[0045] In a possible implementation manner of the first aspect, the determining the wear failure emerging possible feature vector of the target carbon brush at the current moment according to the Lyapunov exponent of the vibration signal of the target carbon brush in the current running period and the temperature change and the current change of the target carbon brush in the current running period comprises:

[0046] obtaining the vibration signal of the target carbon brush in the current running period, and obtaining the surface temperature, the internal temperature and the current value of the target carbon brush at each moment in the current running period;

[0047] determining a difference value between the surface temperature and the internal temperature of the target carbon brush at each moment in the current running period as an initial temperature difference factor of the target carbon brush at each moment in the current running period;

[0048] determining a difference value between the initial temperature difference factors of the target carbon brush at each adjacent moment in the current running period as a temperature difference change amount between each adjacent moment, to obtain a temperature difference change amount sequence;

[0049] determining an absolute value of a difference value between the current values of the target carbon brush at each adjacent moment in the current running period as a current change amount between each adjacent moment, to obtain a current change amount sequence;

[0050] determining a target change index between each adjacent moment according to the temperature difference change amount and the current change amount between each adjacent moment of the target carbon brush in the current running period, to obtain a target change index sequence;

[0051] determining a temperature-current coupling effect value according to the temperature difference change amount sequence, the current change amount sequence and the target change index sequence;

[0052] The temperature rise current coupling effect value and the maximum Lyapunov exponent of the vibration signal of the target carbon brush in the current operation period constitute a wear failure emerging possible feature vector of the target carbon brush at the current moment.

[0053] In combination with the first aspect, in a possible implementation, the temperature rise current coupling effect value is determined according to the temperature difference change amount sequence, the current change amount sequence and the target change index sequence, and includes:

[0054] The target change index sequence is linearly fitted to obtain a target change straight line;

[0055] The temperature rise current coupling effect value is determined according to the mean value of the temperature difference change amount sequence, the mean value of the current change amount sequence and the slope of the target change straight line.

[0056] In combination with the first aspect, in a possible implementation, the health state of the target carbon brush at the current moment is determined according to the potential abnormal wear feature vector and the wear failure emerging possible feature vector, and includes:

[0057] The multiplication values between all elements in the potential abnormal wear feature vector and the wear failure emerging possible feature vector are normalized to obtain a target abnormal state index of the target carbon brush at the current moment;

[0058] If the target abnormal state index is greater than a preset abnormal threshold, it is determined that the target carbon brush has a potential abnormality or a real abnormality at the current moment;

[0059] If the target abnormal state index is less than or equal to the preset abnormal threshold, it is determined that the health state of the target carbon brush is good at the current moment.

[0060] Secondly, the application provides a carbon brush health state online monitoring system of a motor, which includes:

[0061] A target operation load environment cluster acquisition module is configured to determine a target operation load environment cluster of the target carbon brush at the current moment according to the load condition and the operation environment condition of the target carbon brush in the current operation period obtained in advance.

[0062] An operation period screening module is configured to screen historical operation periods belonging to the target operation load environment cluster from all historical operation periods of all historical carbon brushes obtained in advance as reference operation periods.

[0063] An abnormal loss factor determination module is configured to determine an abnormal loss factor of the target carbon brush at the current moment according to the carbon brush overall wear amount of the target carbon brush in the current operation period and the carbon brush overall wear amount of the historical carbon brush in the reference operation period thereof obtained in advance.

[0064] a potential abnormal wear feature vector acquisition module configured to determine a potential abnormal wear feature vector of the target carbon brush at the current time according to the pre-acquired current ripple time domain signal of the target carbon brush in the current operation period and the pre-acquired abnormal loss factor and compression force of the target carbon brush at the current time;

[0065] a wear failure appearance possible feature vector acquisition module configured to determine a wear failure appearance possible feature vector of the target carbon brush at the current time according to the pre-acquired Lyapunov exponent of the vibration signal of the target carbon brush in the current operation period and the pre-acquired temperature change and current change of the target carbon brush in the current operation period;

[0066] a health state monitoring module configured to determine a health state of the target carbon brush at the current time according to the potential abnormal wear feature vector and the wear failure appearance possible feature vector.

[0067] In a third aspect, a server is provided, including a memory and a processor. The memory is configured to store executable program code, and the processor is configured to invoke and run the executable program code from the memory, so that the device executes the method in the first aspect or any possible implementation manner of the first aspect.

[0068] In a fourth aspect, a computer program product is provided, including computer program code, which, when running on a computer, causes the computer to execute the method in the first aspect or any possible implementation manner of the first aspect.

[0069] In a fifth aspect, a computer readable storage medium is provided, which stores computer program code, which, when running on a computer, causes the computer to execute the method in the first aspect or any possible implementation manner of the first aspect.

[0070] The present application has the following beneficial effects:

[0071] The carbon brush health state online monitoring method of the motor considers the potential abnormal wear of the target carbon brush at the current time, realizes the carbon brush health state monitoring, solves the poor timeliness of the carbon brush abnormal health state monitoring, and improves the timeliness of the carbon brush abnormal health state monitoring. Specifically, the present application quantifies multiple indexes related to the potential abnormality of the carbon brush, such as the abnormal loss factor, the potential abnormal wear feature vector and the wear failure appearance possible feature vector, by analyzing the current ripple time domain signal, the load condition, the running environment condition, the vibration signal, the temperature change and the current change of the target carbon brush in the current operation period, so as to relatively timely identify the potential abnormality of the carbon brush and improve the timeliness of the carbon brush abnormal health state monitoring. Attached Figure Description

[0072] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0073] Figure 1 A flowchart illustrating an online monitoring method for the carbon brush health status of an electric motor according to the present invention;

[0074] Figure 2 This is a schematic diagram of the composition structure of an online monitoring system for the carbon brush health status of a motor according to the present invention;

[0075] Figure 3 This is a schematic diagram of the structure of a computer device according to the present invention. Detailed Implementation

[0076] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the specific implementation methods, structures, features, and effects of the technical solution proposed according to the present invention are described in detail below with reference to the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0077] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0078] refer to Figure 1 The flowchart illustrates some embodiments of an online monitoring method for the carbon brush health status of a motor according to the present invention. The online monitoring method for the carbon brush health status of the motor includes the following steps:

[0079] Step S1: Based on the load and operating environment of the target carbon brush during its current operating period, determine the target operating load environment cluster of the target carbon brush at the current moment.

[0080] The target carbon brush can be a brushed motor carbon brush used for online health status monitoring. The current running segment can be the time period during which the target carbon brush is running, and the current time can be the end time of the current running segment. The duration of the current running segment can be 1 hour.

[0081] As an example, this step may include the following steps:

[0082] The first step is to construct the load environment feature vector for each historical brush under each historical running segment based on the load rate and dimensional data under different preset environmental dimensions for each historical brush under each historical running segment.

[0083] The historical carbon brushes can be those that have operated normally in the past and are of the same specifications and model as the target carbon brush. The historical operating period can be the time during which the historical carbon brush operated normally in the past. The duration of the historical operating period can be equal to the duration of the current operating period. The carbon brush load rate typically refers to the current intensity passing through a unit contact area. The preset environmental dimensions can be pre-set environmental-related dimensions. The number of preset environmental dimensions can be pre-set. For example, preset environmental dimensions can be, but are not limited to, surface temperature, internal temperature, and humidity. The dimension data under the preset environmental dimensions can be normalized values ​​of the data collected under the preset environmental dimensions. For example, the dimension data under the surface temperature dimension can be the normalized value of the surface temperature of the collected carbon brush. The surface temperature can be the carbon brush surface temperature monitored in real time using an infrared thermal imager. The internal temperature can be the internal temperature of the carbon brush obtained through an RFID (Radio Frequency Identification) temperature tag embedded inside the carbon brush. Humidity can be collected through a humidity sensor.

[0084] For example, constructing the load environment feature vector for each historical runtime segment of each historical carbon brush may include the following sub-steps:

[0085] The first sub-step involves identifying any historical carbon brush as a marked historical carbon brush and identifying any historical running segment of the marked historical carbon brush as a marked historical running segment.

[0086] The second sub-step is to record the time series of the load rates of the marked historical carbon brushes collected at all times within the marked historical running period as the load rate sequence corresponding to the marked historical running period.

[0087] The third sub-step is to record the time series of dimensional data of the marked historical brushes collected at all times during the marked historical runtime period under the same preset environmental dimension as the dimensional data sequence of the marked historical runtime period under the preset environmental dimension.

[0088] The fourth sub-step involves taking the mean, standard deviation, and number of maxima of the load rate sequence corresponding to the marked historical running period, and the mean of the dimensional data sequence of the marked historical running period under the same preset environmental dimension, to form the load environment feature vector of the marked historical carbon brush under the marked historical running period.

[0089] For example, the load environment feature vector of the marking history carbon brush under the marking history running period can be expressed as: , , N, , ,..., , . Wherein, is the mean of all load rates in the load rate sequence corresponding to the marking history running period. is the standard deviation of all load rates in the load rate sequence corresponding to the marking history running period. N is the number of maximum values in the load rate sequence corresponding to the marking history running period. is the mean of all dimension data in the dimension data sequence under the first preset environmental dimension of the marking history running period. is the mean of all dimension data in the dimension data sequence under the second preset environmental dimension of the marking history running period. is the mean of all dimension data in the dimension data sequence under the n-1th preset environmental dimension of the marking history running period. is the mean of all dimension data in the dimension data sequence under the nth preset environmental dimension of the marking history running period. n is the number of preset environmental dimensions.

[0090] Secondly, according to the load environment feature vectors of all historical running periods of all historical carbon brushes, clustering is performed on all historical running periods of all historical carbon brushes to obtain initial clustering clusters.

[0091] For example, according to the load environment feature vectors of all historical running periods of all historical carbon brushes, K-means++ clustering algorithm can be used to cluster all historical running periods of all historical carbon brushes, and the clustering clusters obtained by clustering are recorded as initial clustering clusters. The number of clustering clusters can be determined by elbow method.

[0092] Thirdly, according to the load rates at all time points in the current running period of the target carbon brush and the dimension data under different preset environmental dimensions obtained in advance, the load environment feature vector of the target carbon brush under the current running period is constructed as the current load environment feature vector.

[0093] It should be noted that the construction method of the load environment feature vector of the target carbon brush under the current running period can be the same as the construction method of the load environment feature vector of the marking history carbon brush under the marking history running period, which will not be repeated here.

[0094] Fourthly, the Euclidean distance between the above current load environment feature vector and the clustering center of each initial clustering cluster is determined as the reference distance corresponding to each initial clustering cluster.

[0095] Step 5, screening the initial cluster corresponding to the minimum reference distance from all initial clusters as the target carbon brush in the current time target running load environment cluster.

[0096] It should be noted that the more history carbon brushes and the more history running periods, the more reasonable the target running load environment cluster is determined.

[0097] Step S2, screening the history running period belonging to the target running load environment cluster from all history running periods of all history carbon brushes as the reference running period.

[0098] As an example, any one history carbon brush can be determined as a marked history carbon brush, and the history running period belonging to the target running load environment cluster is screened from all history running periods of the marked history carbon brush as the reference running period of the marked history carbon brush.

[0099] It should be noted that the marked history carbon brush of the marked history carbon brush is close to the current running period in the past normal running process.

[0100] Step S3, determining the abnormal loss factor of the target carbon brush in the current time according to the carbon brush overall wear amount of the target carbon brush in the current running period and the carbon brush overall wear amount of the history carbon brush in its reference running period.

[0101] As an example, this step can include the following steps:

[0102] First, the difference between the length of the target carbon brush at the start time of the current running period and the length at the end time is determined as the carbon brush overall wear amount of the target carbon brush in the current running period as the current carbon brush overall wear amount.

[0103] For example, the formula for determining the carbon brush overall wear amount of the target carbon brush in the current running period can be:

[0104] ;

[0105] Wherein A is the carbon brush overall wear amount of the target carbon brush in the current running period. is the length of the target carbon brush at the start time of the current running period. is the length of the target carbon brush at the end time of the current running period.

[0106] Secondly, the difference between the length of the history carbon brush at the start time of its reference running period and the length at the end time is determined as the carbon brush overall wear amount of the history carbon brush in its reference running period as the reference carbon brush overall wear amount.

[0107] Thirdly, a density estimation is performed using a Gaussian kernel function to determine a probability density function of the overall wear amount of all reference carbon brushes, and a reference carbon brush overall wear amount corresponding to a maximum function value of the above-mentioned probability density function is determined as the standard carbon brush overall wear amount.

[0108] It should be noted that the standard carbon brush overall wear amount can represent the maximum overall wear amount of the carbon brush under the current load environment when the carbon brush is normally running, and can represent the general situation of the carbon brush under the current load environment when the carbon brush is normally running to some extent. Therefore, the higher the overall wear amount of the carbon brush under the current load environment is than the standard carbon brush overall wear amount, the more likely the carbon brush is to be relatively abnormal. Alternatively, the standard carbon brush overall wear amount can also be set by artificial experience.

[0109] For example, the formula for determining the carbon brush overall wear amount of the historical carbon brush in its reference running period can be:

[0110] ;

[0111] wherein, is the carbon brush overall wear amount of the a-th historical carbon brush in its b-th reference running period. a is the serial number of the historical carbon brush. b is the serial number of the reference running period of the a-th historical carbon brush. is the length of the a-th historical carbon brush at the beginning of its b-th reference running period. is the length of the a-th historical carbon brush at the end of its b-th reference running period.

[0112] Fourthly, the difference between the above-mentioned current carbon brush overall wear amount and the above-mentioned standard carbon brush overall wear amount is normalized to obtain the initial abnormality factor of the target carbon brush at the current time.

[0113] It should be noted that the higher the overall wear amount of the carbon brush under the current load environment is than the standard carbon brush overall wear amount, the more likely the target carbon brush is to be abnormal at the current time.

[0114] Fifthly, the sum of the constant 1 and the above-mentioned initial abnormality factor is determined as the abnormal wear factor of the target carbon brush at the current time.

[0115] Step S4, according to the current current ripple time domain signal of the target carbon brush in the current running period and the compression force and abnormal wear factor of the target carbon brush at the current time, the potential abnormal wear feature vector of the target carbon brush at the current time is determined.

[0116] As an example, this step can include the following steps:

[0117] The first step is to normalize the absolute value of the difference between the clamping force of the target carbon brush at the current moment and the pre-acquired standard clamping force to obtain the force deviation factor.

[0118] One method is to use a pressure sensor to measure the clamping force of the spring on the carbon brush. The standard clamping force characterizes the clamping force of the spring on the carbon brush during normal operation, and it can be the ideal clamping force set at the factory.

[0119] It should be noted that when the force deviation factor is larger, it often means that the friction force generated by the spring on the carbon brush deviates more from the ideal friction force, which often means that the subtle changes should be amplified.

[0120] The second step is to determine the sum of constant 1 and the above-mentioned force deviation factor as the target force deviation index.

[0121] The third step is to determine the abnormal amplification factor by multiplying the target force deviation index and the abnormal loss factor mentioned above.

[0122] The fourth step is to use a coupled current probe to obtain the current ripple time-domain signal of the target carbon brush during the current operating period.

[0123] Among them, the current ripple time-domain signal is also known as the current ripple time-domain signal.

[0124] The fifth step is to perform wavelet packet decomposition on the current ripple time-domain signal to decompose the signal into 8 characteristic frequency bands and determine the energy proportion of each characteristic frequency band.

[0125] Among them, the characteristic frequency bands can be ordered, and the earlier the characteristic frequency band is, the higher the frequency tends to be.

[0126] It should be noted that wavelet packet decomposition can be performed on the current ripple time-domain signal to decompose the signal into 8 characteristic frequency bands (0-10kHz). Each characteristic frequency band has a frequency range width of 1.25kHz, and the earlier the number, the higher the frequency. The energy proportion of each frequency band (E1-E8) can be calculated.

[0127] The sixth step is to determine the energy representative value corresponding to each characteristic frequency band by summing the preset positive factor and the energy proportion of each characteristic frequency band.

[0128] It should be noted that the preset positive factor can be a pre-set positive factor, which can be 1. This is mainly to avoid the denominator being 0 when using the energy representative value corresponding to the characteristic frequency band as the denominator.

[0129] Step 7, determining the target performance value of micro-discharge intensity based on the above-mentioned abnormal amplification factor and the energy representative values ​​corresponding to the first three characteristic frequency bands may include the following sub-steps:

[0130] The first sub-step, the sum value between the energy representative value corresponding to the first characteristic frequency band and the energy representative value corresponding to the second characteristic frequency band is determined as the normal contact friction current ripple performance value.

[0131] The second sub-step, the ratio between the energy representative value corresponding to the third characteristic frequency band and the normal contact friction current ripple performance value is determined as the initial performance value of the micro-discharge intensity.

[0132] The third sub-step, the product between the abnormal amplification factor and the initial performance value of the micro-discharge intensity is determined as the target performance value of the micro-discharge intensity.

[0133] For example, the formula corresponding to the determination of the target performance value of the micro-discharge intensity can be:

[0134] ;

[0135] Among them, is the target performance value of the micro-discharge intensity. is the energy representative value corresponding to the first characteristic frequency band. is the energy representative value corresponding to the second characteristic frequency band. is the energy representative value corresponding to the third characteristic frequency band. C is the abnormal amplification factor. is the normal contact friction current ripple performance value. is the initial performance value of the micro-discharge intensity.

[0136] It should be noted that the first characteristic frequency, the second characteristic frequency and the third characteristic frequency are often high frequency bands, wherein the third characteristic frequency is often closest to the micro-discharge signal generated by the poor contact between the carbon brush and the commutator, and the first characteristic frequency and the second characteristic frequency may be the friction current ripple of normal contact, therefore, The higher C is, the more intense the micro-discharge is. When C is larger, it means that the friction force generated by the spring on the carbon brush deviates from the ideal friction force, and the wear of the target carbon brush at the current time may be more abnormal, which means that the subtle changes should be amplified at this time.

[0137] The eighth step, according to the sum value between the energy representative value corresponding to the fourth characteristic frequency band and the energy representative value corresponding to the fifth characteristic frequency band, and the abnormal amplification factor, the carbon brush elastic deformation recovery abnormal index is determined.

[0138] For example, the formula corresponding to the determination of the carbon brush elastic deformation recovery abnormal index can be:

[0139] ;

[0140] Among them, is the carbon brush elastic deformation recovery abnormal index. is an exponential function with a natural constant as base. is the energy representative value corresponding to the 4th characteristic frequency band. is the energy representative value corresponding to the 5th characteristic frequency band. C is an abnormal amplification factor.

[0141] It should be noted that the 4th characteristic frequency and the 5th characteristic frequency are often mid-frequency bands, which jointly reflect the elastic deformation vibration frequency of the carbon brush caused by the spring pressure. A decrease in the proportion of mid-frequency components often indicates that the spring fatigue is more likely to cause a decrease in the deformation recovery ability.

[0142] In the ninth step, the material carbonization abnormality index is determined according to the ratio between the energy representative value corresponding to the 6th characteristic frequency band and the energy representative value corresponding to the 7th characteristic frequency band, and the above abnormal amplification factor.

[0143] For example, the formula corresponding to the material carbonization abnormality index can be:

[0144] ;

[0145] wherein, is the material carbonization abnormality index. is an exponential function with a natural constant as base. is the energy representative value corresponding to the 6th characteristic frequency band. is the energy representative value corresponding to the 7th characteristic frequency band. C is an abnormal amplification factor.

[0146] It should be noted that the 6th characteristic frequency and the 7th characteristic frequency often belong to low-frequency components, and the carbon brush often contains carbonized and non-carbonized parts. The carbonization process is often a material degradation process, so the friction frequency of the non-carbonized material is relatively higher, which can be used to represent the friction characteristics of the non-carbonized part of the carbon brush material, which can be used to represent the friction characteristics of the carbonized layer, the smaller the value, the more serious the carbonization.

[0147] In the tenth step, the above micro-discharge intensity target performance value, the above carbon brush elastic deformation recovery abnormality index and the material carbonization abnormality index are combined to form a potential abnormal wear feature vector of the target carbon brush at the current time.

[0148] It should be noted that the potential abnormal wear feature vector can often dynamically capture the subtle changes in the contact state of the carbon brush and the commutator. Even when there is no obvious fluctuation in the electrical parameters, potential wear risks can often be identified through the abnormal trends of the components.

[0149] Step S5, according to the Lyapunov exponent of the vibration signal of the target carbon brush in the current running period, and the temperature change and current change of the target carbon brush in the current running period, determine the wear failure emerging possible feature vector of the target carbon brush at the current time.

[0150] As an example, the present step can include the following steps:

[0151] First, obtain the vibration signal of the target carbon brush in the current running period, and obtain the surface temperature, internal temperature and current value of the target carbon brush at each time in the current running period.

[0152] Second, the difference between the surface temperature and the internal temperature of the target carbon brush at each time in the current running period is determined as the initial temperature difference factor of the target carbon brush at each time in the current running period.

[0153] It should be noted that the greater the difference between the surface temperature and the internal temperature of the target carbon brush, the more serious the heat conduction caused by wear inside the carbon brush.

[0154] Third, the difference between the initial temperature difference factors of the target carbon brush at each adjacent time in the current running period is determined as the temperature difference change between each adjacent time, and a temperature difference change sequence is obtained.

[0155] For example, the formula for determining the temperature difference change between adjacent times can be:

[0156] ;

[0157] Wherein, is the temperature difference change between the i-th time and the i+1-th time of the target carbon brush in the current running period. i is the serial number of the different time in the current running period. is the initial temperature difference factor of the target carbon brush at the i+1-th time in the current running period. is the initial temperature difference factor of the target carbon brush at the i-th time in the current running period.

[0158] Fourth, the absolute value of the difference between the current values of the target carbon brush at each adjacent time in the current running period is determined as the current change between each adjacent time, and a current change sequence is obtained.

[0159] Fifth, according to the temperature difference change and the current change between each adjacent time of the target carbon brush in the current running period, determine the target change index between each adjacent time, and obtain a target change index sequence.

[0160] For example, the formula for determining the target change index between adjacent times can be:

[0161] ;

[0162] wherein, is the target change indicator of the target carbon brush between the ith moment and the (i+1)th moment in the current running period. i is the serial number of different moments in the current running period. is the temperature difference change amount of the target carbon brush between the ith moment and the (i+1)th moment in the current running period. is the current change amount of the target carbon brush between the ith moment and the (i+1)th moment in the current running period. is a factor greater than 0 set in advance, mainly used to prevent the denominator from being 0, which can be 0.0001.

[0163] It should be noted that when is greater, it often indicates that the temperature difference change amplitude caused by unit current change is greater.

[0164] The sixth step of determining the temperature rise current coupling effect value according to the above temperature difference change amount sequence, the above current change amount sequence and the above target change indicator sequence can include the following sub-steps:

[0165] The first sub-step is to perform linear fitting on the above target change indicator sequence to obtain a target change straight line.

[0166] Wherein, the abscissa of the target change straight line can be time, and the ordinate can be the target change indicator in the target change indicator sequence.

[0167] The second sub-step is to determine the temperature rise current coupling effect value according to the mean value of the above temperature difference change amount sequence, the mean value of the above current change amount sequence, and the slope of the above target change straight line.

[0168] For example, the formula for determining the temperature rise current coupling effect value can be:

[0169] ;

[0170] Wherein, Q is the temperature rise current coupling effect value. is a normalization function. k is the slope of the target change straight line. is the mean value of all temperature difference change amounts in the temperature difference change amount sequence. is the mean value of all current change amounts in the current change amount sequence. is a factor greater than 0 set in advance, mainly used to prevent the denominator from being 0, which can be 0.0001.

[0171] It should be noted that when k is larger, the temperature difference change amplitude caused by the unit current change is more likely to increase, and the heat conduction caused by the wear inside the carbon brush is more likely to deteriorate. When Q is larger, the temperature difference change amplitude caused by the current change in the current running period is relatively larger. Therefore, when Q is larger, the temperature rise current coupling effect is more likely to increase.

[0172] In the seventh step, the temperature rise current coupling effect value and the maximum Lyapunov exponent of the vibration signal of the target carbon brush in the current running period are combined to form a wear failure appearance possible feature vector of the target carbon brush at the current time.

[0173] It should be noted that when the maximum Lyapunov exponent of the vibration signal of the target carbon brush in the current running period is larger, the chaotic characteristics of the friction system are more likely to increase, the friction state of the carbon brush and the commutator is more likely to be unstable, and the friction of the carbon brush and the commutator is from stable sliding to irregular collision, which is a sign of mechanical state deterioration, indicating that the carbon brush is about to enter the failure edge. Therefore, when the element in the wear failure appearance possible feature vector is larger, it is more likely to have entered the positive feedback stage of wear acceleration to state deterioration, and the fault risk is likely to be significantly improved.

[0174] In step S6, the health state of the target carbon brush at the current time is determined according to the potential abnormal wear feature vector and the wear failure appearance possible feature vector.

[0175] As an example, this step can include the following steps:

[0176] In the first step, the multiplication value between all elements in the potential abnormal wear feature vector and the wear failure appearance possible feature vector is normalized to obtain a target abnormal state index of the target carbon brush at the current time.

[0177] In the second step, if the target abnormal state index is greater than a preset abnormal threshold, it is determined that the target carbon brush has a potential abnormality or a real abnormality at the current time.

[0178] The preset abnormal threshold can be a preset threshold, which can be 0.6.

[0179] In the third step, if the target abnormal state index is less than or equal to the preset abnormal threshold, it is determined that the health state of the target carbon brush at the current time is good.

[0180] Optionally, according to the potential abnormal wear feature vector and the wear failure appearance possible feature vector, the health state of the target carbon brush at the current time can also include the following steps:

[0181] For the potential abnormal wear feature vector and the wear failure possible feature vector, the former describes the subtle change characteristics of the carbon brush and the commutator contact state under certain working condition-environmental condition when the electrical parameters do not appear obvious fluctuation; and the latter describes the change characteristics in the process of the wear of the brush to gradually evolve into failure.

[0182] Therefore, the two feature vectors can be used as the key extraction features of the real-time operation of the brush under the corresponding working condition-environment, and the health status of the carbon brush at any time can be quantified and evaluated by using the monitoring model trained by the two feature vectors, so as to assist the maintenance personnel to judge the real health status of the carbon brush.

[0183] The historical data under different working conditions-environment can be collected, the potential abnormal wear feature vector and the wear failure possible feature vector of each sample can be extracted, the samples are labeled with tags according to the actual health status (0 for normal, 1 for slight abnormality, and 2 for serious abnormality), the two feature vectors and the current, voltage, temperature and other conventional explicit features are used as feature inputs, a random forest classifier is trained, and the corresponding basic model of each combination is obtained, and the probabilities (P0, P1, P2) of the sample belonging to each category are output.

[0184] After the real-time monitoring data is synchronized, the potential abnormal wear feature vector and the wear failure possible feature vector under the current time are obtained, the corresponding basic model is called by matching the current working condition-environment label, and the real-time probabilities P0, P1 and P2 are obtained.

[0185] The standard carbon brush overall wear amount is introduced for dynamic correction: when the current carbon brush overall wear amount exceeds 1.2 times of the standard carbon brush overall wear amount, P1 and P2 are multiplied by 1.1 and 1.3 weights respectively; otherwise, P1 and P2 are multiplied by 0.9 and 0.7 weights respectively.

[0186] Finally, the health status evaluation result corresponding to the real-time monitoring data of the carbon brush of the brush motor is obtained, which provides a quantitative reference for the maintenance personnel.

[0187] Since the subtle change characteristics of the carbon brush and the commutator contact state when the electrical parameters do not appear obvious fluctuation and the change characteristics in the process of the wear of the brush to gradually evolve into failure are extracted respectively, the health status of the carbon brush of the brush motor throughout the cycle can be accurately captured, and the synchronous quantitative evaluation result is obtained, and the carbonization condition can be accurately judged according to the evaluation result to determine whether to replace the node.

[0188] Reference Figure 2, based on the same inventive concept as the method embodiments, the present application provides a carbon brush health state online monitoring system of a motor, which comprises a memory, a processor and a computer program stored in the memory and executable on the processor, and the computer program implements the steps of the carbon brush health state online monitoring method of the motor when executed by the processor, and can specifically include:

[0189] The target running load environment cluster acquisition module 201 is configured to determine a target running load environment cluster of the target carbon brush at the current moment according to the load condition and the running environment condition of the target carbon brush in the current running period obtained in advance.

[0190] The running period screening module 202 is configured to screen historical running periods belonging to the target running load environment cluster from all historical running periods of all historical carbon brushes as reference running periods.

[0191] The abnormal wear factor determination module 203 is configured to determine an abnormal wear factor of the target carbon brush at the current moment according to the total wear amount of the carbon brush of the target carbon brush in the current running period and the total wear amount of the carbon brush of the historical carbon brush in the reference running period thereof obtained in advance.

[0192] The potential abnormal wear feature vector acquisition module 204 is configured to determine a potential abnormal wear feature vector of the target carbon brush at the current moment according to the current ripple time domain signal of the target carbon brush in the current running period, and the compression force and the abnormal wear factor of the target carbon brush at the current moment obtained in advance.

[0193] The wear failure appearance possible feature vector acquisition module 205 is configured to determine a wear failure appearance possible feature vector of the target carbon brush at the current moment according to the Lyapunov exponent of the vibration signal of the target carbon brush in the current running period, and the temperature change and the current change of the target carbon brush in the current running period obtained in advance.

[0194] The health state monitoring module 206 is configured to determine the health state of the target carbon brush at the current moment according to the potential abnormal wear feature vector and the wear failure appearance possible feature vector.

[0195] Figure 3 is a structural schematic diagram of a computer device provided by the embodiment of the present application. As shown in the example, Figure 3 the computer device 300 comprises a memory 301, a processor 302 and a computer program 303 stored in the memory 301 and running on the processor 302, wherein the processor 302 executes the computer program 303, so that the computer device can execute any one of the carbon brush health state online monitoring methods of the motor introduced in the foregoing.

[0196] Based on the same inventive concept as the above method embodiments, the present application provides a server comprising a memory and a processor. The memory is configured to store executable program code, and the processor is configured to call and run the executable program code from the memory, so that the device executes any one of the above motor carbon brush health state online monitoring methods.

[0197] Based on the same inventive concept as the above method embodiments, the present application provides a computer program product comprising: computer program code, which, when executed on a computer, causes the computer to execute any one of the above motor carbon brush health state online monitoring methods.

[0198] Based on the same inventive concept as the above method embodiments, the present application provides a computer readable storage medium storing computer program code, which, when executed on a computer, causes the computer to execute any one of the above motor carbon brush health state online monitoring methods.

[0199] In summary, the present application quantifies a plurality of indicators related to potential abnormalities of the carbon brush, such as abnormal wear factor, potential abnormal wear feature vector, and wear failure appearance possible feature vector, by analyzing the current ripple time domain signal, load condition, running environment condition, vibration signal, temperature change and current change of the target carbon brush in the current running period, so that the potential abnormal condition of the carbon brush can be identified relatively timely, and the timeliness of the carbon brush abnormal health state monitoring is improved.

[0200] The above embodiments are only used to illustrate the technical solutions of the present application, but not limit it; although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the above embodiments, or make equivalent replacement to part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. A method for online monitoring of the health status of a carbon brush of an electric machine, characterized in that, The method comprises the following steps: According to the pre-acquired load condition and operating environment condition of the target carbon brush in the current operating period, the target operating load environment cluster of the target carbon brush at the current time is determined, wherein the current time is the end time of the current operating period; From all the historical operating periods of all the historical carbon brushes pre-acquired, the historical operating period belonging to the target operating load environment cluster is screened out as a reference operating period; According to the pre-acquired carbon brush overall wear amount of the target carbon brush in the current operating period and the carbon brush overall wear amount of the historical carbon brush in its reference operating period, the abnormal wear factor of the target carbon brush at the current time is determined; According to the pre-acquired current ripple time domain signal of the target carbon brush in the current operating period, the pre-acquired compression force of the target carbon brush at the current time and the abnormal wear factor, the potential abnormal wear feature vector of the target carbon brush at the current time is determined; According to the pre-acquired Lyapunov exponent of the vibration signal of the target carbon brush in the current operating period, the pre-acquired temperature change and current change of the target carbon brush in the current operating period, the wear failure appearance possible feature vector of the target carbon brush at the current time is determined; According to the potential abnormal wear feature vector and the wear failure appearance possible feature vector, the health status of the target carbon brush at the current time is determined; The method according to the pre-acquired current ripple time domain signal of the target carbon brush in the current operating period, the pre-acquired compression force of the target carbon brush at the current time and the abnormal wear factor, the potential abnormal wear feature vector of the target carbon brush at the current time comprises: The absolute value difference between the compression force of the target carbon brush at the current time and the pre-acquired standard compression force is normalized to obtain a force deviation factor; The sum value between a constant 1 and the force deviation factor is determined as a target force deviation index; The product between the target force deviation index and the abnormal wear factor is determined as an abnormal amplification factor; The current ripple time domain signal of the target carbon brush in the current operating period is acquired; The current ripple time domain signal is wavelet packet decomposed to decompose the signal into 8 characteristic frequency bands, and the energy proportion of each characteristic frequency band is determined, wherein the characteristic frequency bands are ordered, and the higher the characteristic frequency band, the higher the frequency; The sum value between a preset positive factor and the energy proportion of each characteristic frequency band is determined as the energy representative value corresponding to each characteristic frequency band; According to the abnormal amplification factor and the energy representative values corresponding to the first 3 characteristic frequency bands, a micro-discharge intensity target performance value is determined; According to the sum value between the energy representative value corresponding to the fourth characteristic frequency band and the energy representative value corresponding to the fifth characteristic frequency band, and the abnormal amplification factor, a carbon brush elastic deformation recovery abnormal index is determined; According to the ratio between the energy representative value corresponding to the sixth characteristic frequency band and the energy representative value corresponding to the seventh characteristic frequency band, and the abnormal amplification factor, a material carbonization abnormal index is determined; The micro-discharge intensity target performance value, the carbon brush elastic deformation recovery abnormal index and the material carbonization abnormal index constitute the potential abnormal wear feature vector of the target carbon brush at the current time. The Lyapunov exponent of the vibration signal of the target carbon brush in the current operation period and the temperature change and the current change of the target carbon brush in the current operation period are acquired in advance, and a wear failure appearance possible feature vector of the target carbon brush at the current time is determined, including: The vibration signal of the target carbon brush in the current operation period is acquired, and the surface temperature, the internal temperature and the current value of the target carbon brush at each time in the current operation period are acquired; The difference between the surface temperature and the internal temperature of the target carbon brush at each time in the current operation period is determined as an initial temperature difference factor of the target carbon brush at each time in the current operation period; The difference between the initial temperature difference factors of the target carbon brush at each adjacent time in the current operation period is determined as a temperature difference change amount between each adjacent time, and a temperature difference change amount sequence is obtained; The absolute value of the difference between the current values of the target carbon brush at each adjacent time in the current operation period is determined as a current change amount between each adjacent time, and a current change amount sequence is obtained; The target change index between each adjacent time is determined according to the temperature difference change amount and the current change amount between each adjacent time of the target carbon brush in the current operation period, and a target change index sequence is obtained; The temperature rise current coupling effect value is determined according to the temperature difference change amount sequence, the current change amount sequence and the target change index sequence; The temperature rise current coupling effect value and the maximum Lyapunov exponent of the vibration signal of the target carbon brush in the current operation period are combined to form a wear failure appearance possible feature vector of the target carbon brush at the current time.

2. The method of claim 1, wherein the method further comprises: The target running load environment cluster of the target carbon brush at the current time is determined according to the load condition and the running environment condition of the target carbon brush in the current operation period acquired in advance, including: The load environment feature vector of each historical operation period of each historical carbon brush is constructed according to the load rate and the dimension data in different preset environment dimensions of all times in each historical operation period of each historical carbon brush acquired in advance; The initial clustering clusters are obtained by clustering all historical operation periods of all historical carbon brushes according to the load environment feature vectors of all historical operation periods of all historical carbon brushes; The load environment feature vector of the current operation period of the target carbon brush is constructed as the current load environment feature vector according to the load rate and the dimension data in different preset environment dimensions of all times in the current operation period of the target carbon brush acquired in advance; The Euclidean distance between the current load environment feature vector and the clustering center of each initial clustering cluster is determined as the reference distance corresponding to each initial clustering cluster; The initial clustering cluster corresponding to the smallest reference distance is selected from all initial clustering clusters as the target running load environment cluster of the target carbon brush at the current time.

3. The method of claim 2, wherein the method further comprises: The load environment feature vector of each historical operation period of each historical carbon brush is constructed according to the load rate and the dimension data in different preset environment dimensions of all times in each historical operation period of each historical carbon brush. determining any one historical carbon brush as a marker historical carbon brush, and determining any one historical running period of the marker historical carbon brush as a marker historical running period; a time sequence composed of load rates of the marker historical carbon brush collected at all time points in the marker historical running period is recorded as a load rate sequence corresponding to the marker historical running period; a time sequence composed of dimension data of the marker historical carbon brush in a same preset environmental dimension collected at all time points in the marker historical running period is recorded as a dimension data sequence of the marker historical running period in the preset environmental dimension; a load environmental feature vector of the marker historical carbon brush in the marker historical running period is composed of a mean value, a standard deviation and a number of maximum values of the load rate sequence corresponding to the marker historical running period, and a mean value of the dimension data sequence of the marker historical running period in the same preset environmental dimension.

4. The method of claim 1, wherein the method further comprises: determining the abnormal loss factor of the target carbon brush at the current time point according to the carbon brush overall wear amount of the target carbon brush in the current running period and the carbon brush overall wear amount of the historical carbon brush in the reference running period of the historical carbon brush, comprising: a difference value between the length of the target carbon brush at the start time point of the current running period and the length of the target carbon brush at the end time point is determined as the carbon brush overall wear amount of the target carbon brush in the current running period, as a current carbon brush overall wear amount; similarly, a difference value between the length of the historical carbon brush at the start time point of the reference running period and the length of the historical carbon brush at the end time point is determined as the carbon brush overall wear amount of the historical carbon brush in the reference running period, as a reference carbon brush overall wear amount; a probability density function of all reference carbon brush overall wear amounts is determined, and a reference carbon brush overall wear amount corresponding to a maximum function value of the probability density function is taken as a standard carbon brush overall wear amount; a difference value between the current carbon brush overall wear amount and the standard carbon brush overall wear amount is normalized to obtain an initial abnormal factor of the target carbon brush at the current time point; a sum value between a constant 1 and the initial abnormal factor is determined as the abnormal loss factor of the target carbon brush at the current time point.

5. The method of claim 1, wherein the method further comprises: determining the micro-discharge intensity target performance value according to the abnormal amplification factor and the energy representative values corresponding to the first three characteristic frequency bands, comprising: a sum value between the energy representative value corresponding to the first characteristic frequency band and the energy representative value corresponding to the second characteristic frequency band is determined as a normal contact friction current ripple performance value; a ratio of the energy representative value corresponding to the third characteristic frequency band to the normal contact friction current ripple performance value is determined as a micro-discharge intensity initial performance value; a product of the abnormal amplification factor and the micro-discharge intensity initial performance value is determined as the micro-discharge intensity target performance value.

6. The method of claim 1, wherein the method further comprises: determining the temperature rise current coupling effect value according to the temperature difference change amount sequence, the current change amount sequence and the target change index sequence, comprising: a target change straight line is obtained by linear fitting the target change index sequence; Determine a temperature rise current coupling effect value according to the mean of the temperature difference change amount sequence, the mean of the current change amount sequence, and the slope of the target change straight line.

7. The method of claim 1, wherein the method further comprises: The method comprises the following steps: normalizing the multiplication values between all elements in the potential abnormal wear feature vector and the wear failure appearance possible feature vector to obtain a target abnormal state index of the target carbon brush at the current time; if the target abnormal state index is greater than a preset abnormal threshold, it is determined that the target carbon brush has potential abnormality or real abnormality at the current time; if the target abnormal state index is less than or equal to the preset abnormal threshold, it is determined that the health state of the target carbon brush is good at the current time.

8. An online monitoring system for carbon brush health status of an electric machine, characterized in that, The method comprises a processor and a memory, and the processor is used for processing instructions stored in the memory to realize the online monitoring method of the health state of the carbon brush of the motor.

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