Low-voltage High-power Multi-branch Motor Fault Testing Method Based on Data Analysis

Abstract

This application relates to the technical field of multi-branch motor fault testing, specifically to a low-voltage high-power multi-branch motor fault testing method based on data analysis. The method includes: collecting AC impedance data and current data; obtaining the AC impedance anomaly coefficient of each branch motor at each acquisition moment; obtaining the principal component direction of the current data for each acquisition time period; obtaining the current turbulence coefficient of each branch motor; obtaining the data correlation coefficient between each branch motor and other branch motors; obtaining the motor anomaly coefficient of each branch motor based on the data correlation coefficient; and performing fault testing on the multi-branch motor based on the motor anomaly coefficient. This application improves the accuracy of multi-branch motor fault testing.

Description

Technical Field

[0001] This application relates to the technical field of multi-branch motor fault testing, and specifically to a low-voltage high-power multi-branch motor fault testing method based on data analysis. Background Art

[0002] With the continuous development of industrial automation, low-voltage high-power multi-branch motors have been widely used in various industrial applications, such as fans, water pumps, compressors, etc. However, with the increase in usage time and the change of load conditions, these motors are prone to various faults during operation, such as winding short circuits, insulation aging, mechanical wear, etc., seriously affecting the reliability and economy of the equipment. Traditional fault detection methods mainly rely on experience and regular maintenance, and often cannot real-time monitor the operating state of the motor, resulting in lagging fault discovery and increased maintenance costs. With the development of data acquisition technology and data analysis methods, by real-time monitoring and analyzing various data such as current, voltage, temperature, and vibration during the operation of the motor, the operating state and fault characteristics of the motor can be effectively identified.

[0003] In the prior art, an AC impedance tester is usually used to detect winding short circuit faults. The AC impedance tester applies an AC signal with a known frequency and amplitude to the device under test and measures the response of the device to this signal. If the motor has multiple windings, the AC impedance between two windings is measured to detect whether there is a short circuit. However, because the performance of the motor is different under different load conditions, if the measurement is carried out during the operation of the motor, it will be affected by the load change, thus affecting the accuracy of the measurement result. Summary of the Invention

[0004] In order to solve the above technical problems, this application provides a low-voltage high-power multi-branch motor fault testing method based on data analysis to solve the existing problems.

[0005] The low-voltage high-power multi-branch motor fault testing method based on data analysis of this application adopts the following technical solutions:

[0006] An embodiment of this application provides a low-voltage high-power multi-branch motor fault testing method based on data analysis, and this method includes the following steps:

[0007] Collect the AC impedance data and current data of each branch motor at each acquisition moment;

[0008] Based on the average of the differences between the AC impedance data of each branch motor and the AC impedance data of other branch motors, obtain the AC impedance anomaly coefficient of each branch motor at each acquisition moment;

[0009] Obtain the principal component direction of the current data in each acquisition time period;

[0010] Obtain the current fluctuation coefficient of each branch motor based on the principal component direction of the current data of each branch motor and the difference between the current data and the average of all current data;

[0011] Divide the current data sequence of each branch motor;

[0012] Obtain the data correlation coefficient between each branch motor and other branch motors based on the difference between the average values of the AC impedance anomaly coefficients, the difference between the current fluctuation coefficients, and the correlation between the current data;

[0013] Obtain the motor anomaly coefficient of each branch motor based on the data correlation coefficient;

[0014] Conduct a fault test on the multi-branch motor based on the motor anomaly coefficient.

[0015] Furthermore, the calculation formula for the AC impedance anomaly coefficient is: ; where represents the AC impedance anomaly coefficient of the th branch motor at each acquisition moment; represents the AC impedance data of the th motor at each acquisition moment, represents the AC impedance data of the th branch motor except the th branch motor at each acquisition moment,

[0016] Furthermore, the acquisition time period is a time period composed of a preset number of consecutive acquisition moments.

[0017] Furthermore, the method for obtaining the principal component direction is: for the current data of each acquisition time period, use the principal component analysis algorithm to process the current data of the acquisition time period to obtain the principal component direction of the current data of each acquisition time period.

[0018] Furthermore, the calculation formula for the current fluctuation coefficient is: ; where represents the current fluctuation coefficient of the th branch motor; represents the principal component direction of the current data of the th branch motor in the th acquisition time period, is 180 degrees, represents the exponential function with the natural constant as the base, and m represents the total number of acquisition time periods; represents the th current data of the th branch motor, represents the The mean value of all current data of each branch motor, represents the total number of current data.

[0019] Furthermore, the current data sequence is a sequence obtained by sorting all current data of the branch motor in ascending order of time.

[0020] Furthermore, the calculation formula of the data correlation coefficient is: ; where represents the data correlation coefficient between the th branch motor and the th branch motor; e is the natural constant, represents the mean value of all AC impedance anomaly coefficients of the th branch motor, represents the mean value of all AC impedance anomaly coefficients of the th branch motor, represents the current fluctuation coefficient of the th branch motor, represents the current fluctuation coefficient of the th branch motor, represents the correlation coefficient between the current data sequences of the th branch motor and the th branch motor.

[0021] Furthermore, the method for obtaining the motor anomaly coefficient is:

[0022] Obtain each clustering cluster based on the data correlation coefficient;

[0023] Calculate the difference between the mean value of all data correlation coefficients and the minimum value of all data correlation coefficients, calculate the product of the difference and the number of times each branch motor appears in the clustering cluster with the minimum mean value of the data correlation coefficient, and use the normalized value of the product as the motor anomaly coefficient of each branch motor.

[0024] Furthermore, obtaining each clustering cluster based on the data correlation coefficient includes: for all branch motors, clustering all branch motors using a clustering algorithm, using the reciprocal of the data correlation coefficient between any two branch motors as the metric distance between any two branch motors, and obtaining each clustering cluster.

[0025] Furthermore, the method for performing a fault test on a multi-branch motor based on the motor anomaly coefficient includes: when the motor anomaly coefficient of a branch motor is greater than a preset motor anomaly threshold, regarding the branch motor as a faulty branch motor.

[0026] This application has at least the following beneficial effects:

[0027] This application collects the AC impedance data and current data of each branch motor at each collection moment. In order to reflect the abnormal conditions of the AC impedance of each branch motor, an AC impedance anomaly coefficient is constructed based on the AC impedance data. In order to reflect the stability of the current data of each branch motor, a current turbulence coefficient is constructed based on the current data. Furthermore, a data correlation coefficient is constructed to reflect the correlation between the data of each branch motor and the data of other branch motors, and the motor anomaly coefficient of each branch motor is obtained according to the data correlation coefficient, so as to conduct a fault test on the multi-branch motor and improve the accuracy of the multi-branch motor fault test. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0029] Figure 1 It is a flowchart of a fault test method for low-voltage high-power multi-branch motors based on data analysis provided by an embodiment of the present application;

[0030] Figure 2 It is a flowchart for obtaining the motor anomaly coefficient provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0031] In order to further elaborate on the technical means and effects adopted by the present application to achieve the intended invention purpose, the following will, in conjunction with the accompanying drawings and preferred embodiments, detail the specific implementation manners, structures, features, and effects of the fault test method for low-voltage high-power multi-branch motors based on data analysis proposed in the present application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0032] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which this application belongs.

[0033] The following will specifically describe the specific solution of the fault test method for low-voltage high-power multi-branch motors based on data analysis provided by the present application with reference to the accompanying drawings.

[0034] A fault test method for low-voltage high-power multi-branch motors based on data analysis provided by an embodiment of the present application. Specifically, the following fault test method for low-voltage high-power multi-branch motors based on data analysis is provided. Please refer to Figure 1 and the method includes the following steps:

[0035] Step S1, collect the AC impedance data and current data of each branch motor at each collection moment.

[0036] The multi-branch permanent magnet synchronous motor has branch motor modules, and the traditional complete circumferential stator is divided into (n≥3) branch motor modules with exactly the same motors. In this embodiment, the value of n is 5, and the implementer can select other values according to the actual situation. Moreover, each branch module shares a rotor, and each branch is independently powered by a general low-voltage frequency converter. Among them, each branch module is independent of each other, and different combinations of branches can be arbitrarily put into operation.

[0037] Furthermore, among them, there is one winding in each branch motor. In this application, an AC impedance tester is used to obtain the AC impedance data of each branch motor at each collection moment; an ammeter is used to obtain the current data of each branch motor at each collection moment. In this embodiment, the interval between each collection moment is 1 s, and the implementer can select other values according to the actual situation.

[0038] Step S2, based on the average of the differences between the AC impedance data of each branch motor and the AC impedance data of other branch motors, obtain the AC impedance anomaly coefficient of each branch motor at each collection moment; obtain the principal component direction of the current data of each collection time period; based on the principal component direction of the current data of each branch motor and the difference between the current data and the average of all current data, obtain the current fluctuation coefficient of each branch motor.

[0039] Low-voltage high-power multi-branch motors usually adopt a three-phase power supply system and have multiple winding branches, which can be configured into different wiring methods according to needs, such as star connection or delta connection. Due to their high efficiency and good starting characteristics, low-voltage high-power multi-branch motors are widely used in fields such as manufacturing, mining, transportation, and water treatment to drive various mechanical equipment, such as pumps, fans, conveyor belts, and compressors. Due to their high-power and multi-branch characteristics, special attention needs to be paid to the electrical performance and maintenance management of such motors during operation to ensure their safe and reliable operation and avoid production stagnation and economic losses caused by failures.

[0040] Because it adopts a modular combined stator structure, it has a major change compared with traditional integral motors. The annular iron core is spliced by a certain number of sector-shaped cake-shaped module motors. According to the actual needs of technology, materials, and performance, the distance between adjacent unit motors can be appropriately adjusted. Due to the adoption of a modular winding structure and a reverse embedding method, mechanical decoupling can be achieved between adjacent stator units, thus greatly enhancing the flexibility of motor assembly and maintenance and improving the overall reliability of the motor system.

[0041] A short - circuit fault in the winding will not only lead to a decline in the performance of the motor, but may also cause overheating and damage of the equipment, and even result in safety accidents, posing a serious threat to the stability and continuity of the production line. In addition, the occurrence of a short - circuit fault may also cause current surges in the power grid, affecting the normal operation of other equipment. Therefore, by regularly monitoring the electrical characteristics of the winding, such as impedance changes and temperature rises, potential faults can be detected in a timely manner, and the service life of the motor can be extended.

[0042] According to the above analysis, in order to reflect the abnormal conditions of the AC impedance of each branch motor, based on the average of the differences between the AC impedance data of each branch motor and that of other branch motors, the AC impedance anomaly coefficient of each branch motor is calculated, and the calculation formula is as follows: ; In the formula, represents the AC impedance anomaly coefficient of the th branch motor at each acquisition moment; represents the AC impedance data of the th motor at each acquisition moment, represents the AC impedance data of the th branch motor except the th branch motor at each acquisition moment,

[0043] It should be noted that when a short - circuit problem occurs in a branch motor, it will cause a decrease in the AC impedance of the branch motor, and the difference between the AC impedance of each branch motor and that of other branch motors is relatively large. At this time, has a large value, and the AC impedance anomaly coefficient obtained by the branch motor at each acquisition moment is relatively large, and the greater the possibility of a short - circuit anomaly; conversely, the AC impedance anomaly coefficient obtained by the branch motor at each acquisition moment is relatively small, and the smaller the possibility of a short - circuit anomaly.

[0044] Furthermore, during the long - term operation of the motor winding, the change of the motor load will affect its working state. When the load is too large, the iron core of the motor may enter the magnetic saturation state. In the magnetic saturation state, the inductive reactance of the motor will decrease, resulting in a change in the AC impedance; when the load increases, the current required by the motor will also increase; the increase in current will directly affect the resistance and reactance of the motor winding, thus causing a change in the total AC impedance. For inductive loads, the increase in current will also affect the response time and phase angle of the motor, thus changing the impedance characteristics.

[0045] Therefore, when identifying a short - circuit fault in the motor winding, because the change of the motor load will cause a change in the AC impedance, it is inaccurate to judge whether the motor has a short - circuit fault only based on the AC impedance. Therefore, it is also necessary to analyze the change of the motor current and identify the abnormal conditions according to the change of the current data.

[0046] Take the time period composed of a continuous preset number of acquisition moments as each acquisition time period. In this embodiment, the value of the preset number is 10, and the implementer can select other values according to the actual situation. For the current data of each acquisition time period, use the principal component analysis (PCA) algorithm to process the current data of the acquisition time period to obtain the principal component direction of the current data of each acquisition time period. Among them, the principal component analysis (PCA) algorithm is a well-known technology and will not be elaborated in this embodiment.

[0047] Furthermore, in order to reflect the stability of the current data of each branch motor, based on the principal component direction of the current data of each branch motor and the difference between the current data and the average situation of all current data, obtain the current turbulence coefficient of each branch motor. The calculation formula is: ; In the formula, represents the current turbulence coefficient of the th branch motor; represents the principal component direction of the current data of the th branch motor in the th acquisition time period, is 180 degrees, represents the exponential function with the natural constant as the base, and m represents the total number of acquisition time periods; represents the th current data of the th branch motor, represents the mean value of all current data of the th branch motor,

[0048] It should be noted that represents the change amount of the principal component direction of each acquisition time period. When the principal component direction is positive, the smaller the difference, the more it indicates that the current data is in a positive upward state at this time, so the load of the motor is greater; if the principal component direction is negative, the greater the difference, the more it indicates that the data is in a downward state at this time, so the load of the motor is smaller; represents the angle conversion to radian value; represents the change trend of each segment in the entire current data sequence. The larger its value, the greater the change degree of the current data, so the more unstable it is; represents the difference between the amplitude of the rd data point in the current data of the th branch motor and the mean value of the current data, represents the change degree of the current data. The greater the change degree, the more unstable the current data is, and the greater the current turbulence coefficient obtained at this time; on the contrary, the smaller the current turbulence coefficient obtained.

[0049] Based on the stability of the motor current data changes obtained from the above calculations, and then comparing the correlation of the current data changes between different motors. Because under normal circumstances, the motors on different branches work simultaneously, and their states are stable and similar. When a problem occurs in one of the motors, its current and AC impedance will change; however, when the load on the motor increases, the current of each motor will change, and the changes are relatively uniform.

[0050] Step S3: Divide the current data sequences of each branch motor; based on the differences between the average values of the AC impedance anomaly coefficients, the differences between the current fluctuation coefficients, and the correlation between the current data, obtain the data correlation coefficients between each branch motor and other branch motors; based on the data correlation coefficients, obtain the motor anomaly coefficients of each branch motor.

[0051] For each branch motor, sort all the current data of the branch motor in ascending order of time to obtain the current data sequence of each branch motor.

[0052] Furthermore, based on the above analysis, in order to reflect the correlation between the data of each branch motor and other branch motors, calculate the data correlation coefficients between any two branch motors based on the differences between the average values of the AC impedance anomaly coefficients, the differences between the current fluctuation coefficients, and the correlation between the current data. The calculation formula is: ; where represents the data correlation coefficient between the th branch motor and the th branch motor; e is the natural constant, represents the mean value of all the AC impedance anomaly coefficients of the th branch motor, represents the mean value of all the AC impedance anomaly coefficients of the th branch motor, represents the current fluctuation coefficient of the th branch motor, represents the current fluctuation coefficient of the th branch motor, represents the correlation coefficient between the current data sequences of the th branch motor and the th branch motor; among them, in this embodiment, the Pearson correlation coefficient is selected as the measurement method for the correlation coefficient, and the implementer can select other correlation coefficients according to the actual situation.

[0053] It should be noted that Represents the difference in the AC impedance of the two branch motors. If there is a large difference between the AC impedances of the two branch motors, then there are significant differences in the operating states of the motors. Therefore, their correlation is very small. Thus, here is used to represent the difference in states and is amplified by an exponential factor; Represents the difference in the current stability between the two branch motors. The greater the degree of difference, the greater the difference in the operating states between different motors. At this time, the correlation between the two branch motors is relatively small; represents the th branch motor and the th branch motor's Pearson correlation coefficient of the current data, which describes the correlation between the current data sequences of the two branch motors. The smaller the correlation, the less similar the changes in the current data sequences of the two branch motors. At this time, the value of the obtained data correlation coefficient is small; conversely, the value of the obtained data correlation coefficient is large.

[0054] Furthermore, when the data correlation coefficient between any two branch motors is small, there are significant differences between the state of the branch motor and other motors. The reason for the difference is that there are significant differences between the current data and the AC impedance. When the difference is relatively large, it indicates that the current motor has a short - circuit fault. Therefore, the abnormal branch is located based on the correlation relationship between the states of each motor obtained from the above calculations.

[0055] Because when a certain motor fails, its correlation with other motors will be relatively small. Therefore, based on the above analysis, for all branch motors, the clustering algorithm is used to cluster all branch motors. The reciprocal of the data correlation coefficient between any two branch motors is used as the measurement distance between any two branch motors to obtain each clustering cluster. Among them, the clustering algorithm selected in this embodiment is the DBSCAN clustering algorithm, and the implementer can select other clustering algorithms according to the actual situation.

[0056] Furthermore, according to the division result of the clustering cluster, calculate the motor abnormality coefficient of each branch motor. The calculation formula is: ; In the formula, represents the th branch motor's motor abnormality coefficient, represents the mean value of all data correlation coefficients, represents the minimum value of all data correlation coefficients, represents the number of occurrences of the th motor in the clustering cluster with the minimum mean value of the data correlation coefficient, is the normalization function. Among them, the flowchart for obtaining the motor abnormality coefficient is as Figure 2 shown.

[0057] It should be noted that for each branch motor, when the number of times the branch motor appears in the clustering cluster with the smallest mean of the data correlation coefficients is relatively large, it indicates that the correlation between this motor and other motors is smaller, and thus the branch motor is more abnormal and the probability of failure is greater; when the difference between the mean of all data correlation coefficients and the minimum value of all data correlation coefficients is relatively large, the probability of the branch motor with a failure is greater, and at this time, the motor abnormality coefficient of each motor is greater; conversely, the motor abnormality coefficient is smaller.

[0058] Step S4, perform a fault test on the multi-branch motor based on the motor abnormality coefficient.

[0059] Based on the abnormality of the motor obtained from the above calculation, a preset motor abnormality threshold is set. In this embodiment, the value of the preset motor abnormality threshold is 0.7, and the implementer can select other values according to the actual situation; specifically, when the motor abnormality coefficient of the branch motor is greater than the preset motor abnormality threshold, it indicates that there is a relatively large difference in the current and AC impedance between the current motor and other motors, and thus it is a fault caused by a winding short circuit, and this branch motor is regarded as the faulty branch motor.

[0060] It should be noted that: the above order of the embodiments of the present application is only for description and does not represent the superiority or inferiority of the embodiments. And the above specific embodiments of the present application have been described. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0061] Each embodiment in the present application is described in a progressive manner. The same or similar parts between each embodiment can be referred to each other, and the key points of each embodiment are the differences from other embodiments.

[0062] The above embodiments are only used to illustrate the technical solutions of the present application and are not intended to limit them; modifying the technical solutions recorded in the foregoing embodiments or equivalently replacing some of the technical features does not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of each embodiment of the present application, and all should be included within the protection scope of the present application.

Claims

1. A low-voltage, high-power, multi-branch motor fault testing method based on data analysis, characterized in that: The method comprises the following steps: Collect the AC impedance data and current data of each branch motor at each collection time; Based on the average of the differences between the AC impedance data of each branch motor and other branch motors, the AC impedance abnormality coefficient of each branch motor at each acquisition time is obtained; Obtaining the principal component direction of the current data in each acquisition time period; Based on the main component direction of the current data of each branch motor and the difference between the current data and the average of all current data, the current turbulence coefficient of each branch motor is obtained; Divide the current data sequence of each branch motor; Based on the difference between the average conditions of the AC impedance abnormality coefficient, the difference between the current turbulence coefficients and the correlation between the current data, the data correlation coefficient between each branch motor and other branch motors is obtained; Obtain the motor abnormality coefficient of each branch motor based on the data correlation coefficient; Perform fault testing on multi-branch motors based on motor abnormality coefficients.

2. The low-voltage, high-power, multi-branch motor fault testing method based on data analysis as claimed in claim 1, characterized in that: The calculation formula of the AC impedance anomaly coefficient is: ; In the formula, Indicates the number of The AC impedance anomaly coefficient of each branch motor; Indicates the number of AC impedance data of each motor, It represents the AC impedance data of the rth branch motor except the jth branch motor at each acquisition time. Indicates the number of branch motors, Represents the normalization function.

3. The low-voltage, high-power, multi-branch motor fault testing method based on data analysis as claimed in claim 1, characterized in that: The collection time period is a time period consisting of a preset number of consecutive collection moments.

4. The low-voltage, high-power, multi-branch motor fault testing method based on data analysis as claimed in claim 1, characterized in that: The method for acquiring the principal component direction is: for the current data in each acquisition time period, a principal component analysis algorithm is used to process the current data in the acquisition time period to acquire the principal component direction of the current data in each acquisition time period.

5. The low-voltage, high-power, multi-branch motor fault testing method based on data analysis as claimed in claim 1, characterized in that: The calculation formula of the current turbulence coefficient is: ; In the formula, Indicates The current turbulence coefficient of each branch motor; Indicates The first branch motor The principal component direction of the current data for each acquisition period, is 180 degrees, represents an exponential function with a natural constant as the base, and m represents the total number of acquisition time periods; Indicates The i-th current data of the branch motor, Indicates The average value of all current data of the branch motors, Indicates the total number of current data.

6. The low-voltage, high-power, multi-branch motor fault testing method based on data analysis as claimed in claim 1, characterized in that: The current data sequence is a sequence obtained by sorting all current data of the branch motors in positive time sequence.

7. The low-voltage, high-power, multi-branch motor fault testing method based on data analysis as claimed in claim 1, characterized in that: The calculation formula of the data correlation coefficient is: ; In the formula, Indicates The branch motor and the The data correlation coefficient between the branch motors; e is a natural constant, Indicates The average value of all AC impedance anomaly coefficients of the branch motors, Indicates The average value of all AC impedance anomaly coefficients of the branch motors, Indicates The current turbulence coefficient of each branch motor, Indicates The current turbulence coefficient of each branch motor, Indicates The branch motor and the The correlation coefficient between the current data series of the branch motors.

8. The low-voltage, high-power, multi-branch motor fault testing method based on data analysis as claimed in claim 1, characterized in that: The method for obtaining the motor abnormality coefficient is: Obtain each cluster based on the data correlation coefficient; Calculate the difference between the mean of all data correlation coefficients and the minimum of all data correlation coefficients, calculate the product of the difference and the number of times each branch motor appears in the cluster with the minimum mean of the data correlation coefficient, and use the normalized value of the product as the motor abnormality coefficient of each branch motor.

9. The low-voltage, high-power, multi-branch motor fault testing method based on data analysis as claimed in claim 8, characterized in that: The method of obtaining each clustering cluster based on the data correlation coefficient includes: for all branch motors, using a clustering algorithm to cluster all branch motors, taking the inverse of the data correlation coefficient between any two branch motors as the metric distance between any two branch motors, and obtaining each clustering cluster.

10. The low-voltage, high-power, multi-branch motor fault testing method based on data analysis as claimed in claim 1, characterized in that: The method of performing fault testing on multiple branch motors based on motor abnormality coefficients includes: when the motor abnormality coefficient of a branch motor is greater than a preset motor abnormality threshold, treating the branch motor as a faulty branch motor.

Images