CN116383752B - Motor locked rotor analysis method and system - Google Patents
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Abstract
The invention relates to the field of data processing, and discloses a motor locked rotor analysis method and a motor locked rotor analysis system, which are used for improving the accuracy of motor locked rotor analysis. The method comprises the following steps: inputting the target current matrix into a motor working state analysis model to analyze the motor working state to obtain a plurality of pieces of motor working state information, and classifying a data set of target current data according to the plurality of pieces of motor working state information to obtain sub-current data; constructing a first working state curve according to the sub-current data; data ordering is carried out on the first working state curve, and a second working state curve is generated; extracting characteristic points of the second working state curve to obtain a plurality of curve characteristic points, and calculating a linear slope value; and comparing the linear slope values to obtain a maximum slope value between two adjacent curve characteristic points, and carrying out motor stall warning on the target motor according to the maximum slope value to generate motor stall response information.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a motor locked rotor analysis method and system.
Background
When the rotating speed of the motor is 0 by a machine or man, and torque is still output, the motor load is overlarge, the bearing is damaged, the chamber is swept, the motor cannot be started or stopped to rotate, the power factor is extremely low when the motor is blocked, the current during blocking can be up to 7 times of rated current, the motor is burnt out after a little time, and the utilization rate of the motor is reduced.
The existing scheme is that the overcurrent protection is added by determining the current of the motor in normal operation, and the overcurrent protection can be a thermal relay, an air switch and an external overcurrent protection; after the overcurrent protection action, the motor is immediately inspected, the problem is determined, and then the motor is overhauled, but the load current changes at any time, and the voltage drop of the resistor also changes, so that the whole overcurrent protection system is unstable. The other is to add a limit switch at the position needing to stop rotation, and stop the rotation of the motor after the gear of the motor output shaft rotates to touch the limit switch; the defects of the existing scheme are that the gear of the motor output shaft cannot rotate at any angle, the use scene is limited, and the existing scheme has limitations, namely, the safety and the accuracy of the existing scheme are low.
Disclosure of Invention
In view of the above, the embodiment of the invention provides a motor locked rotor analysis method and a motor locked rotor analysis system, which solve the technical problem of lower accuracy in motor locked rotor analysis.
The invention provides a motor locked rotor analysis method, which comprises the following steps: setting a first sampling resistor and a second sampling resistor of a target motor based on a preset current sampling strategy, and calculating first current data corresponding to the first sampling resistor and second current data corresponding to the second sampling resistor; performing average value operation on the first current data and the second current data to obtain target current data, and performing matrix conversion on the target current data to generate a target current matrix; inputting the target current matrix into a preset motor working state analysis model to analyze the motor working state, obtaining a plurality of pieces of motor working state information, and classifying the target current data according to the pieces of motor working state information to obtain sub-current data corresponding to each piece of motor working state information; constructing a first working state curve of the target motor according to sub-current data corresponding to the working state information of each motor; data ordering is carried out on the first working state curve to obtain a target ordered number group, and a corresponding second working state curve is generated according to the target ordered number group; extracting characteristic points of the second working state curve to obtain a plurality of curve characteristic points, and calculating linear slope values corresponding to the two adjacent curve characteristic points; and comparing the linear slope values to obtain a maximum slope value between two adjacent curve characteristic points, and carrying out motor stall warning on the target motor according to the maximum slope value to generate motor stall response information.
In the present invention, the setting of the first sampling resistor and the second sampling resistor of the target motor based on the preset current sampling strategy, and calculating the first current data corresponding to the first sampling resistor and the second current data corresponding to the second sampling resistor includes: determining a motor sampling period and resistance position information of the target motor based on a preset current sampling strategy; setting a first sampling resistor and a second sampling resistor of a target motor according to the resistance position information, and acquiring first resistance data of the first sampling resistor and second resistance data of the second sampling resistor; acquiring a first input voltage and a first output voltage of the first sampling resistor according to the motor sampling period, and calculating first current data corresponding to the first sampling resistor according to the first input voltage, the first output voltage and the first resistance data; and acquiring a second input voltage and a second output voltage of the second sampling resistor according to the motor sampling period, and calculating second current data corresponding to the second sampling resistor according to the second input voltage, the second output voltage and the second resistance data.
In the present invention, the performing a mean value operation on the first current data and the second current data to obtain target current data, and performing a matrix conversion on the target current data to generate a target current matrix, includes: acquiring first timestamp data corresponding to the first current data and second timestamp data corresponding to the second current data according to the motor sampling period; according to the first timestamp data and the second timestamp data, carrying out data alignment and data matching on the first current data and the second current data to obtain a plurality of current value pairs; respectively calculating the average value of each current value pair to obtain a plurality of current average values, and generating target current data according to the plurality of current average values; and performing matrix conversion on the target current data to generate a target current matrix.
In the invention, the target current matrix is input into a preset motor working state analysis model to analyze the motor working state, so as to obtain a plurality of pieces of motor working state information, and the target current data is classified according to the plurality of pieces of motor working state information to obtain sub-current data corresponding to each piece of motor working state information, comprising: inputting the target current matrix into a preset motor working state analysis model, wherein the motor working state analysis model comprises the following components: a first threshold cycle network, a second threshold cycle network, and a two-layer fully connected network; and carrying out motor working state analysis on the target current matrix through the motor working state analysis model to obtain a plurality of pieces of motor working state information, wherein the motor working state information comprises: a start state, an idle rotation state, a load rotation state and a locked rotation state; and classifying the data set of the target current data according to the plurality of motor working state information to obtain sub-current data corresponding to each motor working state information.
In the present invention, the data sorting is performed on the first working state curve to obtain a target ordered number group, and a corresponding second working state curve is generated according to the target ordered number group, including: data ordering is carried out on the first working state curve to obtain an initial ordered number group; performing multi-round sequencing on the initial ordered number group to generate a target ordered number group; performing discrete distribution on the target ordered number groups to obtain discrete ordered number groups; and performing curve fitting on the discretization ordered number groups to generate a corresponding second working state curve.
In the present invention, the feature point extraction is performed on the second working state curve to obtain a plurality of curve feature points, and a linear slope value corresponding to two adjacent curve feature points is calculated, including: extracting characteristic points of the second working state curve to obtain a plurality of curve characteristic points; acquiring a characteristic point sequence of the plurality of curve characteristic points, and sequentially extracting two adjacent curve characteristic points according to the characteristic point sequence; calculating target circle centers corresponding to the two adjacent curve characteristic points, and respectively connecting the target circle centers with the two adjacent curve characteristic points to perform slope calculation to obtain linear slope values corresponding to the two adjacent curve characteristic points.
In the invention, the linear slope values are compared to obtain the maximum slope value between two adjacent curve characteristic points, and the motor stall warning is carried out on the target motor according to the maximum slope value, so as to generate motor stall response information, which comprises the following steps: comparing the linear slope values to obtain a slope value comparison result; selecting a maximum slope value between two adjacent curve characteristic points according to the slope value comparison result; determining a corresponding target sampling time according to the maximum slope value, and taking the target sampling time as a motor stall occurrence time of the target motor; and carrying out motor stall warning on the target motor according to the motor stall occurrence time to generate motor stall response information.
The invention also provides a motor locked rotor analysis system, which comprises:
the acquisition module is used for setting a first sampling resistor and a second sampling resistor of the target motor based on a preset current sampling strategy, and calculating first current data corresponding to the first sampling resistor and second current data corresponding to the second sampling resistor;
the conversion module is used for carrying out average value operation on the first current data and the second current data to obtain target current data, and carrying out matrix conversion on the target current data to generate a target current matrix;
The classification module is used for inputting the target current matrix into a preset motor working state analysis model to analyze the motor working state, obtaining a plurality of pieces of motor working state information, classifying the target current data according to the pieces of motor working state information, and obtaining sub-current data corresponding to each piece of motor working state information;
the construction module is used for constructing a first working state curve of the target motor according to the sub-current data corresponding to the working state information of each motor;
the sequencing module is used for sequencing the data of the first working state curve to obtain a target ordered number group, and generating a corresponding second working state curve according to the target ordered number group;
the calculating module is used for extracting the characteristic points of the second working state curve to obtain a plurality of curve characteristic points, and calculating the linear slope values corresponding to the two adjacent curve characteristic points;
the generation module is used for comparing the linear slope values to obtain a maximum slope value between two adjacent curve characteristic points, and carrying out motor stall warning on the target motor according to the maximum slope value to generate motor stall response information.
According to the technical scheme provided by the invention, a target current matrix is input into a motor working state analysis model to analyze the motor working state, so that a plurality of pieces of motor working state information are obtained, and a data set is classified according to the plurality of pieces of motor working state information to obtain sub-current data; constructing a first working state curve according to the sub-current data; data ordering is carried out on the first working state curve, and a second working state curve is generated; extracting characteristic points of the second working state curve to obtain a plurality of curve characteristic points, and calculating a linear slope value; the method comprises the steps of comparing linear slope values to obtain a maximum slope value between two adjacent curve characteristic points, and carrying out motor stall warning on a target motor according to the maximum slope value to generate motor stall response information.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the present invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a motor stall analysis method in an embodiment of the present invention.
FIG. 2 is a flow chart of data sorting for a first operating state curve according to an embodiment of the present invention.
Fig. 3 is a schematic diagram of a motor stall analysis system in an embodiment of the present invention.
Reference numerals:
301. an acquisition module; 302. a conversion module; 303. a classification module; 304. constructing a module; 305. a sequencing module; 306. a computing module; 307. and generating a module.
Detailed Description
The following description of the embodiments of the present invention will be made apparent and fully in view of the accompanying drawings, in which some, but not all embodiments of the invention are shown. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. are based on the directions or positional relationships shown in the drawings, are merely for convenience of describing the present invention and simplifying the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and thus should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In addition, the technical features of the different embodiments of the present invention described below may be combined with each other as long as they do not collide with each other.
For easy understanding, the following describes a specific flow of an embodiment of the present invention, referring to fig. 1, fig. 1 is a flow chart of a motor stall analysis method according to an embodiment of the present invention, as shown in fig. 1, including the following steps:
s101, setting a first sampling resistor and a second sampling resistor of a target motor based on a preset current sampling strategy, and calculating first current data corresponding to the first sampling resistor and second current data corresponding to the second sampling resistor;
Specifically, a first sampling resistor and a second sampling resistor of a target motor are set based on a preset current sampling strategy, and first current data corresponding to the first sampling resistor and second current data corresponding to the second sampling resistor are calculated, wherein a resistance value range of a current sampling circuit is determined according to rated current of the motor and characteristics of a driving chip. First current data = drive chip output voltage/first sampling resistor; second current data = drive chip output voltage/second sampling resistor. Specifically, in the embodiment of the present invention, the output current of the motor driving IC may be connected to an ADC pin of the MCU, and the ADC sampling frequency and accuracy may be configured in the program, and the ADC conversion may be started to obtain the ADC conversion result, and calculate the corresponding motor current value, and in the subsequent process, the server determines the rotation condition of the motor according to the magnitude of the motor current value, for example, when the motor current value is 0, it indicates that the motor stops rotating; and when the current value of the motor exceeds a certain threshold value, the motor is indicated to normally operate.
S102, carrying out average value operation on the first current data and the second current data to obtain target current data, and carrying out matrix conversion on the target current data to generate a target current matrix;
in particular, in motor control systems, it is often necessary to smooth the collected current data to obtain more stable and accurate control results. In the embodiment of the invention, the server performs average value operation on the first current data and the second current data and generates a target current matrix. And the first current data and the second current data are sampled for a plurality of times, the sampling results are summed, and the sampling times n are recorded. The average value of the first current data and the second current data is calculated, and the target current data= (sum of the first current data + sum of the second current data)/(2 n), it should be noted that, in the motor control system, the current matrix is a vector composed of current data and is used to represent the real-time state and the control command of the motor, wherein, firstly, the number of current directions to be controlled by the motor controller is determined, and in the motor control system, the current of the motor in multiple directions, for example, the current in three directions of U, V, W, needs to be controlled by the three-phase alternating current motor, is generally required to be controlled. After the current directions are determined, the dimension of the current matrix is determined, and target current data are grouped according to the current directions to obtain current values corresponding to each current direction. And finally, performing matrix conversion on the current value of each current direction, and finally generating a target current matrix.
S103, inputting a target current matrix into a preset motor working state analysis model to analyze the motor working state, obtaining a plurality of pieces of motor working state information, and classifying a data set of target current data according to the pieces of motor working state information to obtain sub-current data corresponding to each piece of motor working state information;
specifically, in this step, the server first determines the type and parameters of the motor working state analysis model, and then inputs the target current matrix into the motor working state analysis model for analysis, and inputs the target current matrix as the input of the model into the preset motor working state analysis model for analysis, thereby obtaining a plurality of motor working state information. It should be noted that the motor operation state information may include an operation state, a load condition, a power factor, etc. of the motor, for evaluating the operation state and performance of the motor. And classifying the target current data according to the plurality of motor working state information, wherein the server takes the plurality of motor working state information as a label, classifies the target current data according to the corresponding motor working state information, and generates a plurality of sub-current data sets.
It should be noted that in the embodiment of the present invention, statistical analysis and feature extraction may also be performed on each sub-current data set, and specifically, the server performs statistical analysis and feature extraction on each sub-current data set, so as to further analyze the working state and performance of the motor. For example, statistical indexes such as a mean value, a variance, spectrum energy and the like of each sub-current data set can be calculated, or characteristic parameters such as a main component, entropy, a wavelet coefficient and the like of each sub-current data set can be extracted, and further, according to the statistical analysis and the characteristic extraction result, the working state and the performance of the motor can be further judged, for example, if the mean value and the spectrum energy of a certain sub-current data set are large, the motor can be possibly indicated to be in a high-load state; if the waveform of a certain sub-current data set shows obvious change, the motor is indicated to have a fault or abnormal condition.
S104, constructing a first working state curve of the target motor according to sub-current data corresponding to the working state information of each motor;
specifically, the server performs preprocessing on sub-current data corresponding to each piece of motor working state information, in this step, the server performs noise removal processing on the sub-current data corresponding to each piece of motor working state information to obtain preprocessed sub-current data, and further calculates an average current value and a variance in the working state according to the preprocessed sub-current data, and simultaneously calculates statistical indexes such as the average current value and the variance in the working state for each processed sub-current data set, so as to describe actual conditions and performance of the motor in the working state. And the server generates a group of points by taking the average current value and the variance as the abscissa, represents the motor working state in the working state, connects the points to form a motor working state curve in the working state, is used for representing the actual performance and performance characteristics of the motor under different loads, and finally generates a first working state curve of the target motor.
S105, data sequencing is carried out on the first working state curve to obtain a target ordered number group, and a corresponding second working state curve is generated according to the target ordered number group;
specifically, the data of the first working state curve is ordered, specifically, the data of the first working state curve is ordered from small to large according to the average current value, a target ordered number group is obtained, then the server takes the target ordered number group as an abscissa, the variance and the average current value under each load are calculated again, and a corresponding second working state curve is generated. For example, the maximum sustainable current and power of the motor under different loads may be calculated, or the motor's behavior and response in overload or fault conditions may be predicted.
S106, extracting characteristic points of the second working state curve to obtain a plurality of curve characteristic points, and calculating straight line slope values corresponding to the two adjacent curve characteristic points;
in the embodiment of the present invention, in order to avoid the influence of noise and interference on the feature point extraction, the second operating state curve needs to be smoothed. The method can effectively eliminate noise and retain the main characteristics of the curve, and carry out extremum detection on the smoothed second working state curve, and extract all maximum value and minimum value points as characteristic points. The characteristic points can reflect important turning points and characteristic changes of the curve, are favorable for further analyzing and predicting the performance of the motor, and calculate slope values between two adjacent characteristic points according to the extracted characteristic points. This slope value can be used to evaluate the rate of change and the trend of the curve, for example, if the slope between two feature points is large, it indicates that the motor is responding faster in this load range, and if the slope is zero or near zero, it indicates that the motor is behaving steadily in this load range. And further analyzing the working state and performance of the motor according to the characteristic points and the slope values of the second working state curve. For example, cluster analysis may be performed for different feature points and slope values to find specific areas and performance features on the curve; or adopting regression analysis, a support vector machine and other methods to predict the performance and response of the motor under unknown load.
In the step, feature point extraction is performed on the second working state curve to obtain a plurality of curve feature points, and linear slope values corresponding to two adjacent curve feature points are calculated, so that the method is very important in a motor control system. By comprehensively and accurately analyzing and processing the main characteristics and the change trend of the curve, detailed and fine characteristic points and slope information reflecting the working state and the performance of the motor can be constructed, and important support and guidance are provided for the optimization and the control of the motor.
And S107, comparing the linear slope values to obtain a maximum slope value between two adjacent curve characteristic points, and carrying out motor stall warning on the target motor according to the maximum slope value to generate motor stall response information.
Specifically, the server sorts the slope values of the straight lines between all the two adjacent curve characteristic points according to the size, and determines the maximum value in the sorted slope values of the straight lines, namely the maximum slope value between the two adjacent curve characteristic points. The maximum slope value can represent the response speed and the trend of the motor in the load range. And in the step, comparing the linear slope values to obtain the maximum slope value between two adjacent curve characteristic points, and carrying out motor stall warning on the target motor according to the maximum slope value to generate motor stall response information, which is very important in a motor control system. Through monitoring and early warning to the real-time state and the performance of motor, can improve reliability and the stability of motor, ensure the normal work of motor in long-term operation.
By executing the steps, inputting the target current matrix into a motor working state analysis model to analyze the motor working state, obtaining a plurality of pieces of motor working state information, and classifying the target current data according to the plurality of pieces of motor working state information to obtain sub-current data; constructing a first working state curve according to the sub-current data; data ordering is carried out on the first working state curve, and a second working state curve is generated; extracting characteristic points of the second working state curve to obtain a plurality of curve characteristic points, and calculating a linear slope value; the method comprises the steps of comparing linear slope values to obtain a maximum slope value between two adjacent curve characteristic points, and carrying out motor stall warning on a target motor according to the maximum slope value to generate motor stall response information.
In a specific embodiment, the process of executing step S101 may specifically include the following steps:
(1) Determining a motor sampling period and resistance position information of a target motor based on a preset current sampling strategy;
(2) Setting a first sampling resistor and a second sampling resistor of a target motor according to the resistance position information, and acquiring first resistance data of the first sampling resistor and second resistance data of the second sampling resistor;
(3) Acquiring a first input voltage and a first output voltage of a first sampling resistor according to a motor sampling period, and calculating first current data corresponding to the first sampling resistor according to the first input voltage, the first output voltage and the first resistor data;
(4) And obtaining a second input voltage and a second output voltage of the second sampling resistor according to the motor sampling period, and calculating second current data corresponding to the second sampling resistor according to the second input voltage, the second output voltage and the second resistor data.
In particular, the server determines the period of time that the motor is sampled, for example, it may be configured to sample the motor at intervals (e.g., once per second), or at a particular load, etc. Resistance position information is determined based on the structure and characteristics of the target motor. It should be noted that there are multiple resistors inside the motor, including the sampling resistor required for measuring the current. Therefore, it is necessary to determine the position of the sampling resistor in order to acquire current data inside the motor. And setting a sampling period and a sampling frequency according to the motor sampling period and the resistance position information. The sampling period should be matched to the operating conditions and operational requirements of the motor to obtain accurate and efficient current data. And configuring corresponding sampling equipment and sampling parameters according to the motor sampling time period and the resistance position information. The sampling device may include an analog signal processor, a data acquisition card, a real-time controller, etc. for acquiring current data inside the motor. The sampling parameters include sampling frequency, sampling time, sampling channel and the like, and the resistance position for current sampling is determined according to the internal structure and characteristics of the motor. Typically, there will be multiple resistors within the motor, including the sampling resistor required to measure current. The first sampling resistor and the second sampling resistor are set. And setting a first sampling resistor and a second sampling resistor of the target motor according to the resistance position information. The first sampling resistor and the second sampling resistor should be located at different positions inside the motor, respectively, to ensure that different current characteristics are sampled. For example, it may be selected to place a first sampling resistor on the motor rotor circuit, a second sampling resistor on the motor stator circuit, and so on. And acquiring first resistance data of the first sampling resistor and second resistance data of the second sampling resistor by connecting tools such as a testing instrument (such as a universal meter) and the like. Acquiring a first input voltage and a first output voltage of a first sampling resistor according to a motor sampling period, and calculating first current data corresponding to the first sampling resistor according to the first input voltage, the first output voltage and the first resistor data; and meanwhile, acquiring a second input voltage and a second output voltage of a second sampling resistor according to the motor sampling period, and calculating second current data corresponding to the second sampling resistor according to the second input voltage, the second output voltage and the second resistor data. In this step, this process can be divided into the following steps: and acquiring a first input voltage and a first output voltage of the first sampling resistor, and calculating first current data corresponding to the first sampling resistor. And in the motor sampling period, acquiring a first input voltage and a first output voltage of a first sampling resistor and first resistance data, and calculating first current data corresponding to the first sampling resistor by using ohm's law. Specifically, the current value is equal to the input voltage minus the output voltage divided by the resistance value, i.e.: first current data= (first input voltage-first output voltage)/first resistance data, second input voltage and second output voltage of the second sampling resistor are obtained, and second current data corresponding to the second sampling resistor are calculated. And in the motor sampling period, acquiring a second input voltage and a second output voltage of a second sampling resistor and second resistance data, and calculating second current data corresponding to the second sampling resistor by using ohm's law. Specifically, the current value is equal to the input voltage minus the output voltage divided by the resistance value, i.e.: second current data= (second input voltage-second output voltage)/second resistance data.
According to the invention, based on a preset current sampling strategy, a first input voltage and a first output voltage of a first sampling resistor are obtained according to a motor sampling period, and first current data corresponding to the first sampling resistor are calculated; and simultaneously, acquiring a second input voltage and a second output voltage of a second sampling resistor, and calculating second current data corresponding to the second sampling resistor.
In a specific embodiment, the process of executing step S102 may specifically include the following steps:
(1) According to the motor sampling period, acquiring first timestamp data corresponding to the first current data and second timestamp data corresponding to the second current data;
(2) According to the first timestamp data and the second timestamp data, carrying out data alignment and data matching on the first current data and the second current data to obtain a plurality of current value pairs;
(3) Respectively calculating the average value of each current value pair to obtain a plurality of current average values, and generating target current data according to the plurality of current average values;
(4) And performing matrix conversion on the target current data to generate a target current matrix.
Specifically, the server determines a time stamp mode of current sampling, for example, the current can be set to be sampled in each period of time, and the time stamp of each sampling is recorded; or sampling in a specific state of the motor and recording the start and end time of each state, etc. Recording first timestamp data corresponding to the first current data and second timestamp data corresponding to the second current data, and recording the first timestamp data corresponding to the first current data and the second timestamp data corresponding to the second current data according to the sampling period and the timestamp mode. Specifically, according to the current sampling time stamping mode, the time stamp of each sampling is recorded, and then the time stamp and corresponding current data are stored in a data warehouse. And further determining the data alignment mode. For example, in a real-time control system, accurate data alignment may be performed by means of hardware synchronous sampling; in offline data processing, data alignment may be performed using time stamps. And determining the corresponding relation between the first current data and the second current data according to the first time stamp data and the second time stamp data. Specifically, the first time stamp data and the second time stamp data are compared, and the corresponding relation closest to the time stamp is found. And carrying out data matching and alignment according to the corresponding relation between the first current data and the second current data. Specifically, the first current data and the second current data are aligned according to the time stamp, and a plurality of current value pairs are obtained. And analyzing and processing according to the obtained current value pair. For example, spectral analysis, time domain analysis, statistical analysis, etc. may be performed to understand the performance and characteristics of the motor. And averaging each current value pair by using the plurality of current value pairs (comprising the first current data and the second current data) acquired before to obtain a plurality of current average values. Specifically, the two current values in each current value pair are averaged. Target current data is generated from the plurality of current averages. Specifically, a plurality of current averages are arranged in a certain order into one current vector, i.e., a target current vector. And performing matrix conversion on the target current vector to generate a target current matrix. Specifically, the target current vectors are packaged into a matrix according to a certain dimension so as to facilitate the processing and application of the subsequent controllers.
In a specific embodiment, the process of executing step S103 may specifically include the following steps:
(1) Inputting a target current matrix into a preset motor working state analysis model, wherein the motor working state analysis model comprises: a first threshold cycle network, a second threshold cycle network, and a two-layer fully connected network;
(2) And carrying out motor working state analysis on the target current matrix through a motor working state analysis model to obtain a plurality of pieces of motor working state information, wherein the motor working state information comprises: a start state, an idle rotation state, a load rotation state and a locked rotation state;
(3) And classifying the data set of the target current data according to the working state information of the plurality of motors to obtain sub-current data corresponding to the working state information of each motor.
Specifically, the server inputs the target current matrix into a preset motor working state analysis model, wherein the motor working state analysis model comprises: the first threshold circulation network, the second threshold circulation network and the two-layer full-connection network are used for analyzing the target current matrix through the motor working state analysis model to obtain a plurality of motor working state information, such as a starting state, an idle rotation state, a loaded rotation state, a locked rotation state and the like. Specifically, the current working state of the motor is determined by comparing the difference between the target current data and a preset value or a standard value. And classifying the data set of the target current data according to the working state information of the plurality of motors to obtain a sub-current data set corresponding to the working state information of each motor. Specifically, the target current data are classified according to the working state of the motor, so that a plurality of sub-current data sets are obtained, and each sub-current data set corresponds to one working state of the motor.
In the embodiment of the invention, for each sub-current data set, analysis and processing can be further performed, for example, characteristic parameters such as current average value, variance, power spectrum and the like in different states are respectively calculated, so that the performance and characteristics of the motor in different states can be known more deeply.
In a specific embodiment, as shown in fig. 2, the process of performing step S105 may specifically include the following steps:
s201, data sequencing is carried out on the first working state curve to obtain an initial ordered number group;
s202, carrying out multi-round sequencing on the initial ordered number group to generate a target ordered number group;
s203, performing discrete distribution on the target ordered number groups to obtain discrete ordered number groups;
s204, performing curve fitting on the discretized ordered number groups to generate a corresponding second working state curve.
Specifically, the server determines the sorting manner in advance, for example, sorting according to the current magnitude, sorting according to the time stamp, or the like. And acquiring first working state curve data from the motor control system, and extracting required data information. And sequencing the first working state curve data according to the sequencing mode determined before to obtain an initial ordered number group. In particular, the data may be sorted using algorithms such as fast sorting, merge sorting, and the like. And verifying whether the result of the data ordering meets the expectations. The method can use a visualization method, for example, drawing a sequenced graph, checking whether the graph is sequenced according to a specified mode, and further extracting target sequenced group data, further, in the embodiment of the invention, the server performs self-adaptive discretization according to the data distribution condition, further, a discretized sequenced group is obtained, and meanwhile, whether the discretized result meets the expectations is verified. Specifically, by drawing a graph or a scatter diagram after discretization, it is checked whether the data has been discretized in a specified manner. The server then determines the fitting function, for example, polynomial fitting, exponential fitting, logarithmic fitting, etc. methods may be employed. And acquiring discretized ordered group data from the motor control system, extracting required data information, performing curve fitting operation on the discretized ordered group data according to the fitting function determined in the prior art to obtain a second working state curve, and verifying whether a curve fitting result meets expectations. A visualization method, such as drawing a fitted graph or scatter plot, may be used to check whether the curve has been fitted in a specified manner, ultimately generating a corresponding second operating state curve.
In a specific embodiment, the process of executing step S106 may specifically include the following steps:
(1) Extracting characteristic points of the second working state curve to obtain a plurality of curve characteristic points;
(2) Acquiring a characteristic point sequence of a plurality of curve characteristic points, and sequentially extracting two adjacent curve characteristic points according to the characteristic point sequence;
(3) And calculating the target circle centers corresponding to the two adjacent curve characteristic points, and respectively connecting the target circle centers with the two adjacent curve characteristic points to perform slope calculation to obtain the linear slope values corresponding to the two adjacent curve characteristic points.
Specifically, the server first determines the type of the feature point, in the embodiment of the present invention, takes the extreme point, the inflection point, the slope change point and the like as the feature point, acquires the second working state curve data from the motor control system, extracts the required data information, and performs the feature point extraction operation on the second working state curve data according to the type of the feature point determined in the foregoing, so as to obtain a plurality of curve feature points. Specifically, the method of differential equation, filter, least square method and the like may be used to perform feature point extraction processing, so as to obtain a plurality of curve feature points, and then the server determines the feature point arrangement mode according to the time sequence, and sorts the plurality of curve feature points, so as to obtain the feature point sequence. The sorting may be performed using an algorithm, such as bubbling sorting, insertion sorting, or the like, and two adjacent curve feature points are sequentially extracted according to the feature point order and processed. Specifically, information such as distance and time interval between the two curve feature points can be calculated, curve change trend is analyzed, whether abnormal conditions exist or not is judged, finally, the target circle centers corresponding to the two adjacent curve feature points are calculated, the target circle centers and the two adjacent curve feature points are connected respectively, slope calculation is conducted, and the linear slope values corresponding to the two adjacent curve feature points are obtained.
In a specific embodiment, the process of executing step S107 may specifically include the following steps:
(1) Comparing the linear slope values to obtain a slope value comparison result;
(2) Selecting a maximum slope value between two adjacent curve characteristic points according to the slope value comparison result;
(3) Determining a corresponding target sampling time according to the maximum slope value, and taking the target sampling time as a motor stall occurrence time of a target motor;
(4) And carrying out motor stall warning on the target motor through the motor stall occurrence time to generate motor stall response information.
Specifically, the server sets a comparison threshold, and then divides the slope value into three conditions of ascending, descending and unchanged through the comparison threshold, calculates slope values of two straight lines according to two adjacent curve characteristic points and corresponding target circle centers, compares the calculated slope values to obtain a slope value comparison result, selects a maximum slope value between the two adjacent curve characteristic points, and selects the maximum slope value between the two adjacent curve characteristic points according to the slope value comparison result. And determining the corresponding target sampling time according to the maximum slope value. The time stamp of the feature point may be used to determine the sampling instant. And taking the target sampling time as the motor stall occurrence time of the target motor. A time stamp may be used to indicate the motor stall occurrence time. And carrying out motor locked rotor early warning operation according to the motor locked rotor occurrence time, and generating corresponding locked rotor response information. The early warning operation can be performed by means of sound, optical fibers and the like, and the response information can be output by means of screen prompt, log record and the like.
The embodiment of the invention also provides a motor locked rotor analysis system, as shown in fig. 3, which specifically comprises:
the acquiring module 301 is configured to set a first sampling resistor and a second sampling resistor of a target motor based on a preset current sampling strategy, and calculate first current data corresponding to the first sampling resistor and second current data corresponding to the second sampling resistor;
the conversion module 302 is configured to perform a mean value operation on the first current data and the second current data to obtain target current data, and perform matrix conversion on the target current data to generate a target current matrix;
the classification module 303 is configured to input the target current matrix into a preset motor working state analysis model to perform motor working state analysis, obtain a plurality of pieces of motor working state information, and classify the target current data according to the plurality of pieces of motor working state information to obtain sub-current data corresponding to each piece of motor working state information;
the construction module 304 is configured to construct a first working state curve of the target motor according to sub-current data corresponding to the working state information of each motor;
The sorting module 305 is configured to sort the data of the first working state curve to obtain a target ordered group, and generate a corresponding second working state curve according to the target ordered group;
the calculating module 306 is configured to extract feature points of the second working state curve, obtain a plurality of curve feature points, and calculate linear slope values corresponding to two adjacent curve feature points;
and the generating module 307 is configured to compare the linear slope values to obtain a maximum slope value between two adjacent curve feature points, and perform motor stall warning on the target motor according to the maximum slope value, so as to generate motor stall response information.
Optionally, the acquiring module 301 is specifically configured to: determining a motor sampling period and resistance position information of the target motor based on a preset current sampling strategy;
setting a first sampling resistor and a second sampling resistor of a target motor according to the resistance position information, and acquiring first resistance data of the first sampling resistor and second resistance data of the second sampling resistor;
acquiring a first input voltage and a first output voltage of the first sampling resistor according to the motor sampling period, and calculating first current data corresponding to the first sampling resistor according to the first input voltage, the first output voltage and the first resistance data;
And acquiring a second input voltage and a second output voltage of the second sampling resistor according to the motor sampling period, and calculating second current data corresponding to the second sampling resistor according to the second input voltage, the second output voltage and the second resistance data.
Optionally, the conversion module 302 is specifically configured to: acquiring first timestamp data corresponding to the first current data and second timestamp data corresponding to the second current data according to the motor sampling period; according to the first timestamp data and the second timestamp data, carrying out data alignment and data matching on the first current data and the second current data to obtain a plurality of current value pairs; respectively calculating the average value of each current value pair to obtain a plurality of current average values, and generating target current data according to the plurality of current average values; and performing matrix conversion on the target current data to generate a target current matrix.
Optionally, the classification module 303 is specifically configured to: inputting the target current matrix into a preset motor working state analysis model, wherein the motor working state analysis model comprises the following components: a first threshold cycle network, a second threshold cycle network, and a two-layer fully connected network; and carrying out motor working state analysis on the target current matrix through the motor working state analysis model to obtain a plurality of pieces of motor working state information, wherein the motor working state information comprises: a start state, an idle rotation state, a load rotation state and a locked rotation state; and classifying the data set of the target current data according to the plurality of motor working state information to obtain sub-current data corresponding to each motor working state information.
Optionally, the sorting module 305 is specifically configured to: data ordering is carried out on the first working state curve to obtain an initial ordered number group; performing multi-round sequencing on the initial ordered number group to generate a target ordered number group; performing discrete distribution on the target ordered number groups to obtain discrete ordered number groups; and performing curve fitting on the discretization ordered number groups to generate a corresponding second working state curve.
Optionally, the computing module 306 is specifically configured to: extracting characteristic points of the second working state curve to obtain a plurality of curve characteristic points; acquiring a characteristic point sequence of the plurality of curve characteristic points, and sequentially extracting two adjacent curve characteristic points according to the characteristic point sequence; calculating target circle centers corresponding to the two adjacent curve characteristic points, and respectively connecting the target circle centers with the two adjacent curve characteristic points to perform slope calculation to obtain linear slope values corresponding to the two adjacent curve characteristic points.
Optionally, the generating module 307 is specifically configured to: comparing the linear slope values to obtain a slope value comparison result; selecting a maximum slope value between two adjacent curve characteristic points according to the slope value comparison result; determining a corresponding target sampling time according to the maximum slope value, and taking the target sampling time as a motor stall occurrence time of the target motor; and carrying out motor stall warning on the target motor according to the motor stall occurrence time to generate motor stall response information.
Through the cooperation of the modules, a target current matrix is input into a motor working state analysis model to analyze the motor working state, so that a plurality of pieces of motor working state information are obtained, and data sets of target current data are classified according to the plurality of pieces of motor working state information, so that sub-current data are obtained; constructing a first working state curve according to the sub-current data; data ordering is carried out on the first working state curve, and a second working state curve is generated; extracting characteristic points of the second working state curve to obtain a plurality of curve characteristic points, and calculating a linear slope value; the method comprises the steps of comparing linear slope values to obtain a maximum slope value between two adjacent curve characteristic points, and carrying out motor stall warning on a target motor according to the maximum slope value to generate motor stall response information.
The above embodiments are only for illustrating the technical aspects of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the embodiments, it should be understood by those of ordinary skill in the art that: modifications and equivalents may be made to the specific embodiments of the invention without departing from the spirit and scope of the invention, which is intended to be covered by the scope of the claims.
Claims (7)
1. The motor locked rotor analysis method is characterized by comprising the following steps of:
setting a first sampling resistor and a second sampling resistor of a target motor based on a preset current sampling strategy, and calculating first current data corresponding to the first sampling resistor and second current data corresponding to the second sampling resistor;
performing average value operation on the first current data and the second current data to obtain target current data, and performing matrix conversion on the target current data to generate a target current matrix;
inputting the target current matrix into a preset motor working state analysis model to analyze the motor working state, obtaining a plurality of pieces of motor working state information, and classifying the target current data according to the pieces of motor working state information to obtain sub-current data corresponding to each piece of motor working state information;
Constructing a first working state curve of the target motor according to sub-current data corresponding to the working state information of each motor;
data ordering is carried out on the first working state curve to obtain a target ordered number group, and a corresponding second working state curve is generated according to the target ordered number group;
extracting characteristic points of the second working state curve to obtain a plurality of curve characteristic points, and calculating linear slope values corresponding to the two adjacent curve characteristic points;
comparing the linear slope values to obtain a maximum slope value between two adjacent curve characteristic points, and carrying out motor stall warning on the target motor according to the maximum slope value to generate motor stall response information, wherein the comparing the linear slope values to obtain the maximum slope value between two adjacent curve characteristic points, and carrying out motor stall warning on the target motor according to the maximum slope value to generate motor stall response information, and the method comprises the following steps: comparing the linear slope values to obtain a slope value comparison result; selecting a maximum slope value between two adjacent curve characteristic points according to the slope value comparison result; determining a corresponding target sampling time according to the maximum slope value, and taking the target sampling time as a motor stall occurrence time of the target motor; and carrying out motor stall warning on the target motor according to the motor stall occurrence time to generate motor stall response information.
2. The motor stall analysis method of claim 1, wherein the setting the first sampling resistor and the second sampling resistor of the target motor based on the preset current sampling strategy, and calculating the first current data corresponding to the first sampling resistor and the second current data corresponding to the second sampling resistor, comprises:
determining a motor sampling period and resistance position information of the target motor based on a preset current sampling strategy;
setting a first sampling resistor and a second sampling resistor of a target motor according to the resistance position information, and acquiring first resistance data of the first sampling resistor and second resistance data of the second sampling resistor;
acquiring a first input voltage and a first output voltage of the first sampling resistor according to the motor sampling period, and calculating first current data corresponding to the first sampling resistor according to the first input voltage, the first output voltage and the first resistance data;
and acquiring a second input voltage and a second output voltage of the second sampling resistor according to the motor sampling period, and calculating second current data corresponding to the second sampling resistor according to the second input voltage, the second output voltage and the second resistance data.
3. The method of claim 2, wherein the performing a mean value operation on the first current data and the second current data to obtain target current data, and performing a matrix conversion on the target current data to generate a target current matrix, includes:
acquiring first timestamp data corresponding to the first current data and second timestamp data corresponding to the second current data according to the motor sampling period;
according to the first timestamp data and the second timestamp data, carrying out data alignment and data matching on the first current data and the second current data to obtain a plurality of current value pairs;
respectively calculating the average value of each current value pair to obtain a plurality of current average values, and generating target current data according to the plurality of current average values;
and performing matrix conversion on the target current data to generate a target current matrix.
4. The method for analyzing motor stalling according to claim 1, wherein inputting the target current matrix into a preset motor operation state analysis model to analyze motor operation states to obtain a plurality of pieces of motor operation state information, and classifying the target current data according to the plurality of pieces of motor operation state information to obtain sub-current data corresponding to each piece of motor operation state information, comprises:
Inputting the target current matrix into a preset motor working state analysis model, wherein the motor working state analysis model comprises the following components: a first threshold cycle network, a second threshold cycle network, and a two-layer fully connected network;
and carrying out motor working state analysis on the target current matrix through the motor working state analysis model to obtain a plurality of pieces of motor working state information, wherein the motor working state information comprises: a start state, an idle rotation state, a load rotation state and a locked rotation state;
and classifying the data set of the target current data according to the plurality of motor working state information to obtain sub-current data corresponding to each motor working state information.
5. The method of claim 1, wherein the step of sorting the first operating state curves to obtain target ordered groups, and generating corresponding second operating state curves according to the target ordered groups comprises:
data ordering is carried out on the first working state curve to obtain an initial ordered number group;
performing multi-round sequencing on the initial ordered number group to generate a target ordered number group;
Performing discrete distribution on the target ordered number groups to obtain discrete ordered number groups;
and performing curve fitting on the discretization ordered number groups to generate a corresponding second working state curve.
6. The method for analyzing motor stalling according to claim 1, wherein the extracting the feature points of the second working state curve to obtain a plurality of curve feature points, and calculating the linear slope values corresponding to two adjacent curve feature points, includes:
extracting characteristic points of the second working state curve to obtain a plurality of curve characteristic points;
acquiring a characteristic point sequence of the plurality of curve characteristic points, and sequentially extracting two adjacent curve characteristic points according to the characteristic point sequence;
calculating target circle centers corresponding to the two adjacent curve characteristic points, and respectively connecting the target circle centers with the two adjacent curve characteristic points to perform slope calculation to obtain linear slope values corresponding to the two adjacent curve characteristic points.
7. A motor stall analysis system for performing the motor stall analysis method of any of claims 1 to 6, comprising:
the acquisition module is used for setting a first sampling resistor and a second sampling resistor of the target motor based on a preset current sampling strategy, and calculating first current data corresponding to the first sampling resistor and second current data corresponding to the second sampling resistor;
The conversion module is used for carrying out average value operation on the first current data and the second current data to obtain target current data, and carrying out matrix conversion on the target current data to generate a target current matrix;
the classification module is used for inputting the target current matrix into a preset motor working state analysis model to analyze the motor working state, obtaining a plurality of pieces of motor working state information, classifying the target current data according to the pieces of motor working state information, and obtaining sub-current data corresponding to each piece of motor working state information;
the construction module is used for constructing a first working state curve of the target motor according to the sub-current data corresponding to the working state information of each motor;
the sequencing module is used for sequencing the data of the first working state curve to obtain a target ordered number group, and generating a corresponding second working state curve according to the target ordered number group;
the calculating module is used for extracting the characteristic points of the second working state curve to obtain a plurality of curve characteristic points, and calculating the linear slope values corresponding to the two adjacent curve characteristic points;
the generation module is used for comparing the linear slope values to obtain a maximum slope value between two adjacent curve characteristic points, carrying out motor stall warning on the target motor according to the maximum slope value, and generating motor stall response information, wherein the comparison of the linear slope values to obtain the maximum slope value between two adjacent curve characteristic points, carrying out motor stall warning on the target motor according to the maximum slope value, and generating motor stall response information, and the generation module comprises the following steps: comparing the linear slope values to obtain a slope value comparison result; selecting a maximum slope value between two adjacent curve characteristic points according to the slope value comparison result; determining a corresponding target sampling time according to the maximum slope value, and taking the target sampling time as a motor stall occurrence time of the target motor; and carrying out motor stall warning on the target motor according to the motor stall occurrence time to generate motor stall response information.
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