Textile mill driving motor control method based on data analysis

Abstract

The invention discloses a textile mill driving motor control method based on data analysis, and particularly relates to the technical field of textile mill driving motor control. The operation hidden danger monitoring model of the textile mill driving motor is constructed by obtaining cooling efficiency fluctuation information, bearing vibration rising information, motor phase angle abnormal information and insulation resistance decreasing information of a motor winding in the operation process of the textile mill driving motor, and operation hidden danger analysis is carried out on a main motor. Potential operation hidden dangers existing in the main motor are sensed in time, the situation that equipment is replaced only after faults occur is avoided, operation hidden danger analysis is conducted on the slave motors, the stably-operating slave motors are selected from the slave motors, the time difference of the operation hidden dangers existing in the main motor is obtained, and the task complexity of the current production task is combined. A main motor dynamic adjustment model is constructed, whether the main motor is replaced immediately or not is flexibly judged according to changes of the production environment, production scheduling and resource configuration are optimized, continuity and stability of the production process are ensured, and dynamic selection of the main motor is optimized.

Description

Technical Field

[0001] The present invention relates to the technical field of textile mill drive motor control, and more specifically, to a textile mill drive motor control method based on data analysis. Background Art

[0002] In the modern textile industry, drive motors play a vital role as the core power source of production equipment. With the expansion of production scale and the improvement of automation level, textile factories often need multiple motors to work together to ensure efficient synchronization of various production links. However, existing multi-motor control systems are usually based on a fixed master-slave configuration mode, that is, a master motor is selected as the speed reference, and the remaining motors follow according to the output of the master motor. This method usually reselects only when the main motor has an obvious fault, resulting in potential operating hazards not being identified in time, thereby affecting the continuity and stability of the production process, and may even cause equipment downtime and further economic losses. However, how to identify the potential operating hazards before the main motor has an obvious fault and complete the dynamic selection of the main motor without interrupting the critical production stage is still an important challenge that needs to be solved. Summary of the invention

[0003] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a textile mill drive motor control method based on data analysis to solve the problems raised in the above-mentioned background technology.

[0004] To achieve the above object, the present invention provides the following technical solutions:

[0005] The textile mill drive motor control method based on data analysis includes the following steps:

[0006] Step S1, obtaining cooling efficiency fluctuation information, bearing vibration increase information, motor phase angle abnormality information, and motor winding insulation resistance decrease information during the operation of the textile factory drive motor;

[0007] Step S2, constructing a hidden danger monitoring model for the operation of the drive motor of the textile factory, analyzing the abnormal hidden dangers of the operation of the drive motor of the textile factory, generating a hidden danger monitoring index for the drive motor, and comparing the hidden danger monitoring index for the drive motor with a preset hidden danger monitoring index threshold for the drive motor, so as to evaluate the hidden dangers of the operation of the drive motor;

[0008] Step S3, analyzing the operating hidden dangers of the main motor according to the operating hidden danger monitoring model of the drive motor of the textile factory to determine whether the main motor has operating hidden dangers;

[0009] Step S4, when the main motor has an operation hazard, the operation hazard analysis of the slave motor is performed according to the textile factory drive motor operation hazard monitoring model to obtain a main motor candidate ranking list;

[0010] Step S5, obtaining the time difference when the main motor has operating hazards and combining it with the task complexity of the current production task, constructing a main motor dynamic adjustment model to determine whether to replace the main motor immediately.

[0011] In a preferred embodiment, the cooling efficiency fluctuation information includes the cooling efficiency fluctuation coefficient, the bearing vibration rise information includes the bearing vibration rise coefficient, the motor phase angle abnormality information includes the motor phase angle abnormality coefficient, and the motor winding insulation resistance drop information includes the insulation resistance drop coefficient.

[0012] In a preferred embodiment, by acquiring cooling efficiency fluctuation information during the operation of the drive motor, analyzing the cooling efficiency fluctuation during the operation of the drive motor, and acquiring a cooling efficiency fluctuation coefficient, the degree of cooling efficiency fluctuation during the operation of the drive motor is measured;

[0013] The logic for obtaining the cooling efficiency fluctuation coefficient is as follows:

[0014] Preset a fixed time period T, obtain the cooling efficiency data in the adjacent time period, and mark the cooling efficiency data time series in the T time period as Q = {ql i,k}={ql 1,k ,ql 2,k ,...,ql I,k}, where ql i,k represents the kth cooling efficiency data feature at the i-th time in the time period, i = {1, 2, ..., I}, I is a positive integer; the cooling efficiency data time series in the T+1 time period is marked as C = {cl j,k}={cl 1,k ,cl 2,k ,...,cl J,k}, where cl j,k represents the kth cooling efficiency data feature at the jth moment in the time period, j={1,2,...,J}, J is a positive integer;

[0015] According to each cooling efficiency data feature, the Euclidean distance at each moment in the cooling efficiency data time series is calculated to obtain the distance matrix D, where each element D(i,j) in the distance matrix represents ql i,k and cl j,k The Euclidean distance is calculated as follows:

[0016] Construct a cumulative distance matrix ML to store the minimum cumulative distance from the starting point (1,1) to each point (i,j). The calculation rule of the cumulative distance is as follows: ML(i,j)=D(i,j)+min{ML(i-1,j),ML(i,j-1),ML(i-1,j-1)}, where ML(i,j) represents ql i,k and cl j,k The minimum cumulative distance of

[0017] Calculate the cooling efficiency fluctuation coefficient Lqbd, the expression is as follows Where YA represents the sum of the elements in the cumulative distance matrix.

[0018] In a preferred embodiment, by acquiring bearing vibration rise information during the operation of the drive motor, analyzing the abnormal rise of the bearing vibration during the operation of the drive motor, and acquiring the bearing vibration rise coefficient, the degree of abnormal rise of the bearing vibration during the operation of the drive motor is measured;

[0019] The logic for obtaining the bearing vibration rise coefficient is as follows:

[0020] Obtain the bearing vibration data signal x(t) during the operation of the drive motor, and use the EMD algorithm to decompose the bearing vibration data signal to obtain the inherent mode function and residual component. The decomposition formula is as follows Where x(t) is the original bearing vibration data signal, which represents the vibration data collected at time t, and IMF n (t) represents the value of the nth intrinsic mode function at time t, n = {1, 2, ..., N}, N is a positive integer, ca(t) represents the residual component at time t;

[0021] Calculating IMF n (t) The instantaneous energy EIMF n (t), the expression is as follows EIMF n (t) = IMF n (t) 2 ;

[0022] Preset the time window ΔT and calculate the energy change rate Nh. The expression is as follows EIMF n (t) represents the instantaneous energy of the nth intrinsic mode function at time t;

[0023] Calculate the average rate of energy change The expression is as follows Where Nh(t,ΔT m ) represents the energy change rate at time t and the mth time window, m = {1, 2, ..., M}, M is a positive integer;

[0024] Calculate IMF using Hilbert transform n The analytical signal JX of (t) n (t), the expression is as follows JX n (t) = IMF n (t)+xs*XB(IMF n (t)), where JX n (t) represents IMF n (t), xs represents the imaginary unit, XB(IMF n (t)) represents IMF n Hilbert transform of (t);

[0025] Calculating IMF n (t) is the instantaneous frequency

[0026] Calculate the bearing vibration rise coefficient Zcsx, the expression is as follows

[0027] In a preferred embodiment, by acquiring abnormal information of the motor phase angle during the operation of the drive motor, analyzing the abnormal fluctuation of the motor phase angle during the operation of the drive motor, and acquiring the abnormal coefficient of the motor phase angle, the abnormal fluctuation degree of the motor phase angle during the operation of the drive motor is measured;

[0028] The logic for obtaining the motor phase angle abnormality coefficient is as follows:

[0029] Get the current and voltage data of the drive motor when it is running, and calculate the motor phase angle. The expression is as follows where θ r Indicates the motor phase angle at the rth moment, DL r represents the current at the rth moment, DY r represents the voltage at the rth moment;

[0030] Calculate the average motor phase angle xpj, the expression is as follows

[0031] Calculate the standard deviation of the motor phase angle xbz, the expression is as follows

[0032] Calculate the motor phase angle abnormality coefficient Xwjy, the expression is as follows

[0033] In a preferred embodiment, by acquiring the insulation resistance drop information of the motor winding during the operation of the drive motor, analyzing the abnormal insulation resistance drop of the motor winding during the operation of the drive motor, and acquiring the insulation resistance drop coefficient, the abnormal insulation resistance drop degree of the motor winding during the operation of the drive motor is measured;

[0034] The logic for obtaining the insulation resistance reduction coefficient is as follows:

[0035] Preset multiple monitoring points to obtain the insulation resistance value dz of the drive motor winding b ,dz b represents the insulation resistance value of the b-th monitoring point, b={1,2,...,B}, B is a positive integer;

[0036] Calculate the average insulation resistance value The expression is as follows

[0037] The insulation resistance values ​​of the drive motor windings at different monitoring points are compared with the average insulation resistance values, and the insulation resistance values ​​that are less than the average insulation resistance values ​​are marked as insulation resistance abnormal values, and the insulation resistance abnormal values ​​are added to the insulation resistance abnormal set, and the insulation resistance abnormal set is marked as BJH={dzc y}={dzc1,dzc2,...,dzc Y}, where y = {1, 2, ..., Y}, Y is a positive integer;

[0038] The insulation resistance reduction coefficient Jydx is calculated based on the insulation resistance abnormal value in the insulation resistance abnormal set, and is expressed as follows

[0039] In a preferred embodiment, a textile mill drive motor operation hidden danger monitoring model is constructed according to the cooling efficiency fluctuation coefficient, the bearing vibration rise coefficient, the motor phase angle abnormality coefficient, and the insulation resistance drop coefficient to generate a drive motor hidden danger monitoring index Qdyz. The formula based on the model is as follows: Wherein α, β, γ, and δ represent preset proportional coefficients of cooling efficiency fluctuation coefficient, bearing vibration rise coefficient, motor phase angle abnormality coefficient, and insulation resistance decrease coefficient, respectively, and α, β, γ, and δ are all greater than 0.

[0040] In a preferred embodiment, the operation hidden danger analysis of the main motor is performed according to the textile mill drive motor operation hidden danger monitoring model, and the drive motor hidden danger monitoring index generated by the textile mill drive motor operation hidden danger monitoring model is compared with a preset drive motor hidden danger monitoring index threshold;

[0041] If the drive motor hidden danger monitoring index is greater than the drive motor hidden danger monitoring index threshold, a main motor operation hidden danger signal is generated;

[0042] If the drive motor hidden danger monitoring index is less than or equal to the drive motor hidden danger monitoring index threshold, there is no need to generate a main motor operation hidden danger signal.

[0043] In a preferred embodiment, when the main motor operation hidden danger signal is generated, the operation hidden danger analysis of the slave motor is performed according to the textile factory drive motor operation hidden danger monitoring model, and the drive motor hidden danger monitoring index generated by the textile factory drive motor operation hidden danger monitoring model is compared with a preset drive motor hidden danger monitoring index threshold;

[0044] If the drive motor hidden danger monitoring index is greater than the drive motor hidden danger monitoring index threshold, a warning signal is generated;

[0045] If the drive motor hidden danger monitoring index is less than or equal to the drive motor hidden danger monitoring index threshold, a main motor candidate ranking table is generated for the slave motors from small to large according to the drive motor hidden danger monitoring index.

[0046] In a preferred embodiment, the time when the main motor operation hidden danger signal is generated is recorded, and the current time and the time when the main motor operation hidden danger signal is generated are calculated by difference to obtain the time difference Sjc when the main motor has the operation hidden danger;

[0047] The task complexity of the current production task refers to the complexity of the production task of the current link that the main motor is responsible for. The task complexity of the current production task is measured by the total number of processes Zgx of the production task of the current link;

[0048] According to the time difference of the main motor running hidden dangers and the total number of processes, the main motor dynamic adjustment model is constructed to generate the main motor dynamic adjustment index Tzds. The model is based on the following formula Tzds = ρ*Sjc+σ*Zgx, where ρ and σ represent the preset proportional coefficients of the time difference of the main motor running hidden dangers and the total number of processes, respectively, and ρ and σ are both greater than 0;

[0049] The main motor dynamic adjustment index is compared with a preset main motor dynamic adjustment index threshold value. If the main motor dynamic adjustment index is greater than the main motor dynamic adjustment index threshold value, a main motor immediate replacement signal is generated, and the main motor is replaced with the first motor in the main motor candidate ranking table;

[0050] If the main motor dynamic adjustment index is less than or equal to the main motor dynamic adjustment index threshold, there is no need to generate a main motor immediate replacement signal.

[0051] Technical effects and advantages of the present invention:

[0052] 1. The present invention constructs a textile factory drive motor operation hidden danger monitoring model by acquiring cooling efficiency fluctuation information and bearing vibration increase information, motor phase angle abnormality information and motor winding insulation resistance decrease information during the operation of the textile factory drive motor, generates a drive motor hidden danger monitoring index to analyze the operation hidden danger of the main motor, timely perceives the operation hidden danger of the main motor, avoids equipment replacement only after a fault occurs, thereby reducing downtime and production losses, and the textile factory drive motor operation hidden danger monitoring model analyzes the operation hidden danger of the slave motor, selects a stably operating slave motor from the slave motor, obtains a main motor candidate ranking list, and simultaneously obtains the time difference when the main motor has the operation hidden danger and combines the task complexity of the current production task to construct a main motor dynamic adjustment model, and flexibly determines whether to replace the main motor immediately according to changes in the production environment, thereby optimizing production scheduling and resource allocation, ensuring the continuity and stability of the production process, optimizing the dynamic selection of the main motor, and improving the production safety and efficiency of the textile factory. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to facilitate understanding by those skilled in the art, the present invention is further described below in conjunction with the accompanying drawings;

[0054] Figure 1 Schematic diagram of the structure of the method of the embodiment of the present invention. DETAILED DESCRIPTION

[0055] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0056] Example: Figure 1 The invention provides a method for controlling a driving motor of a textile mill based on data analysis, comprising the following steps:

[0057] Step S1, obtaining cooling efficiency fluctuation information, bearing vibration increase information, motor phase angle abnormality information, and motor winding insulation resistance decrease information during the operation of the textile factory drive motor;

[0058] Step S2, constructing a hidden danger monitoring model for the operation of the drive motor of the textile factory, analyzing the abnormal hidden dangers of the operation of the drive motor of the textile factory, generating a hidden danger monitoring index for the drive motor, and comparing the hidden danger monitoring index for the drive motor with a preset hidden danger monitoring index threshold for the drive motor, so as to evaluate the hidden dangers of the operation of the drive motor;

[0059] Step S3, analyzing the operating hidden dangers of the main motor according to the operating hidden danger monitoring model of the drive motor of the textile factory to determine whether the main motor has operating hidden dangers;

[0060] Step S4, when the main motor has an operation hazard, the operation hazard analysis of the slave motor is performed according to the textile factory drive motor operation hazard monitoring model to obtain a main motor candidate ranking list;

[0061] Step S5, obtaining the time difference when the main motor has operating hidden dangers and combining it with the task complexity of the current production task, constructing a main motor dynamic adjustment model to determine whether to replace the main motor immediately;

[0062] Step S1, obtaining cooling efficiency fluctuation information, bearing vibration increase information, motor phase angle abnormality information, and motor winding insulation resistance decrease information during the operation of the textile factory drive motor;

[0063] The cooling efficiency fluctuation information includes the cooling efficiency fluctuation coefficient, the bearing vibration increase information includes the bearing vibration increase coefficient, the motor phase angle abnormality information includes the motor phase angle abnormality coefficient, and the motor winding insulation resistance decrease information includes the insulation resistance decrease coefficient;

[0064] The cooling efficiency fluctuation coefficient is used to measure the degree of abnormal fluctuation in the cooling efficiency of the drive motor cooling system. When the cooling efficiency fluctuation coefficient is larger, it indicates that the stability of the cooling effect of the drive motor is worse. The instability of the cooling efficiency may cause the temperature of the drive motor to rise during operation, increasing the probability of operating hidden dangers. On the contrary, when the cooling efficiency fluctuation coefficient is smaller, it indicates that the cooling effect of the drive motor is more stable, and the probability of drive motor failure caused by poor cooling is lower.

[0065] Therefore, by obtaining the cooling efficiency fluctuation information during the operation of the drive motor, analyzing the cooling efficiency fluctuation during the operation of the drive motor, and obtaining the cooling efficiency fluctuation coefficient, the cooling efficiency fluctuation degree during the operation of the drive motor is measured;

[0066] The logic for obtaining the cooling efficiency fluctuation coefficient is as follows:

[0067] Preset a fixed time period T, obtain the cooling efficiency data in the adjacent time period, and mark the cooling efficiency data time series in the T time period as Q = {ql i,k}={ql 1,k ,ql 2,k ,...,ql I,k}, where ql i,k represents the kth cooling efficiency data feature at the i-th time in the time period, i = {1, 2, ..., I}, I is a positive integer; the cooling efficiency data time series in the T+1 time period is marked as C = {clj,k}={cl 1,k ,cl 2,k ,...,cl J,k}, where cl j,k represents the kth cooling efficiency data feature at the jth moment in the time period, j={1,2,...,J}, J is a positive integer;

[0068] It should be noted that the cooling efficiency data characteristics include coolant temperature, flow rate, pressure, etc.;

[0069] According to each cooling efficiency data feature, the Euclidean distance at each moment in the cooling efficiency data time series is calculated to obtain the distance matrix D, where each element D(i,j) in the distance matrix represents ql i,k and cl j,k The Euclidean distance is calculated as follows:

[0070] Construct a cumulative distance matrix ML to store the minimum cumulative distance from the starting point (1,1) to each point (i,j). The calculation rule of the cumulative distance is as follows: ML(i,j)=D(i,j)+min{ML(i-1,j),ML(i,j-1),ML(i-1,j-1)}, where ML(i,j) represents ql i,k and cl j,k The minimum cumulative distance of

[0071] It should be noted that the boundary condition of the cumulative distance calculation rule is ML(1,1)=D(1,1);

[0072] Calculate the cooling efficiency fluctuation coefficient Lqbd, the expression is as follows Where YA represents the sum of elements in the cumulative distance matrix;

[0073] The bearing vibration rise coefficient is used to measure the abnormal increase in the bearing vibration level during the operation of the drive motor. When the bearing vibration rise coefficient is larger, it indicates that the bearing vibration has increased significantly in a short period of time, which may indicate potential hidden dangers such as bearing wear, insufficient lubrication or mechanical imbalance, and increase the probability of motor failure; on the contrary, when the vibration rise coefficient is smaller, it means that the bearing vibration is stable, its working state is healthier, and has less impact on the normal operation of the motor;

[0074] Therefore, by obtaining the bearing vibration rise information during the operation of the drive motor, analyzing the abnormal rise of the bearing vibration during the operation of the drive motor, and obtaining the bearing vibration rise coefficient, the degree of abnormal rise of the bearing vibration during the operation of the drive motor is measured;

[0075] The logic for obtaining the bearing vibration rise coefficient is as follows:

[0076] Obtain the bearing vibration data signal x(t) during the operation of the drive motor, and use the EMD algorithm to decompose the bearing vibration data signal to obtain the inherent mode function and residual component. The decomposition formula is as follows Where x(t) is the original bearing vibration data signal, which represents the vibration data collected at time t, and IMF n (t) represents the value of the nth intrinsic mode function at time t, n = {1, 2, ..., N}, N is a positive integer, each intrinsic mode function represents a specific frequency component of the bearing vibration data signal, reflecting the vibration characteristics of different scales or frequencies in the bearing vibration data signal, ca(t) represents the residual component at time t, representing the remaining part of the bearing vibration data signal after decomposition, including the lower frequency components or noise in the bearing vibration data signal;

[0077] Calculating IMF n (t) The instantaneous energy EIMF n (t), the expression is as follows EIMF n (t) = IMF n (t) 2 , evaluate the vibration intensity at different time points;

[0078] Preset the time window ΔT and calculate the energy change rate Nh. The expression is as follows EIMF n (t) represents the instantaneous energy of the nth intrinsic mode function at time t;

[0079] Calculate the average rate of energy change The expression is as follows Where Nh(t,ΔT m ) represents the energy change rate at time t and the mth time window, m = {1, 2, ..., M}, M is a positive integer;

[0080] Calculate IMF using Hilbert transform n The analytical signal JX of (t) n (t), the expression is as follows JX n (t) = IMF n (t)+xs*XB(IMF n (t)), where JX n (t) represents IMF n (t), xs represents the imaginary unit, XB(IMF n (t)) represents IMF n Hilbert transform of (t);

[0081] Calculating IMF n (t) is the instantaneous frequency in express The derivative with respect to time t;

[0082] Calculate the bearing vibration rise coefficient Zcsx, the expression is as follows

[0083] The motor phase angle abnormality coefficient is used to measure the abnormal fluctuation degree of the phase angle of the drive motor during operation. The phase angle usually refers to the phase difference between the motor current and voltage, reflecting the load condition and operating status of the motor. Abnormal phase angle fluctuation may indicate that the motor has a fault, uneven load or other operating problems. The larger the motor phase angle abnormality coefficient, the greater the phase angle fluctuation degree of the drive motor, and the greater the probability that the drive motor has operating hidden dangers; conversely, when the motor phase angle abnormality coefficient is smaller, the smaller the abnormal fluctuation degree of the phase angle, and the smaller the probability that the drive motor has operating hidden dangers;

[0084] Therefore, by acquiring the abnormal information of the motor phase angle during the operation of the drive motor, analyzing the abnormal fluctuation of the motor phase angle during the operation of the drive motor, and acquiring the abnormal coefficient of the motor phase angle, the abnormal fluctuation degree of the motor phase angle during the operation of the drive motor is measured;

[0085] The logic for obtaining the motor phase angle abnormality coefficient is as follows:

[0086] Get the current and voltage data of the drive motor when it is running, and calculate the motor phase angle. The expression is as follows where θ r Indicates the motor phase angle at the rth moment, DL r represents the current at the rth moment, DY r represents the voltage at the rth moment;

[0087] Calculate the average motor phase angle xpj, the expression is as follows

[0088] Calculate the standard deviation of the motor phase angle xbz, the expression is as follows

[0089] Calculate the motor phase angle abnormality coefficient Xwjy, the expression is as follows

[0090] The insulation resistance drop coefficient is used to measure the abnormal drop in insulation resistance of the drive motor winding, reflecting the insulation performance and potential failure risk of the motor winding. The larger the insulation resistance drop coefficient, the more serious the abnormal drop in insulation resistance of the drive motor winding, which means that the insulation performance of the motor winding deteriorates, the potential failure risk increases, and the probability of the drive motor having operating hazards is greater. On the contrary, when the insulation resistance drop coefficient is smaller, it indicates that the insulation resistance of the drive motor winding remains stable or fluctuates slightly, the insulation performance is good, and the potential failure risk is low.

[0091] Therefore, by obtaining the insulation resistance drop information of the motor winding during the operation of the drive motor, the abnormal insulation resistance drop of the motor winding during the operation of the drive motor is analyzed, and the insulation resistance drop coefficient is obtained to measure the abnormal insulation resistance drop degree of the motor winding during the operation of the drive motor;

[0092] The logic for obtaining the insulation resistance reduction coefficient is as follows:

[0093] Preset multiple monitoring points to obtain the insulation resistance value dz of the drive motor winding b ,dz b represents the insulation resistance value of the b-th monitoring point, b={1,2,...,B}, B is a positive integer;

[0094] Calculate the average insulation resistance value The expression is as follows

[0095] The insulation resistance values ​​of the drive motor windings at different monitoring points are compared with the average insulation resistance values, and the insulation resistance values ​​that are less than the average insulation resistance values ​​are marked as insulation resistance abnormal values, and the insulation resistance abnormal values ​​are added to the insulation resistance abnormal set, and the insulation resistance abnormal set is marked as BJH={dzc y}={dzc1,dzc2,...,dzc Y}, where y = {1, 2, ..., Y}, Y is a positive integer;

[0096] The insulation resistance reduction coefficient Jydx is calculated based on the insulation resistance abnormal value in the insulation resistance abnormal set, and is expressed as follows

[0097] Step S2, constructing a hidden danger monitoring model for the operation of the drive motor of the textile factory, analyzing the abnormal hidden dangers of the operation of the drive motor of the textile factory, generating a hidden danger monitoring index for the drive motor, and comparing the hidden danger monitoring index for the drive motor with a preset hidden danger monitoring index threshold for the drive motor, so as to evaluate the hidden dangers of the operation of the drive motor;

[0098] According to the cooling efficiency fluctuation coefficient, bearing vibration rise coefficient, motor phase angle abnormality coefficient, and insulation resistance drop coefficient, a textile mill drive motor operation hidden danger monitoring model is constructed to generate the drive motor hidden danger monitoring index Qdyz. The model is based on the following formula Wherein α, β, γ, and δ represent the preset proportional coefficients of the cooling efficiency fluctuation coefficient, the bearing vibration rise coefficient, the motor phase angle abnormality coefficient, and the insulation resistance drop coefficient, respectively, and α, β, γ, and δ are all greater than 0;

[0099] It should be noted that before constructing the monitoring model for hidden dangers in the operation of drive motors in textile mills, it is necessary to ensure that the cooling efficiency fluctuation coefficient, bearing vibration rise coefficient, motor phase angle abnormality coefficient, and insulation resistance drop coefficient are all normalized. Common normalization methods include Min-Max normalization and Z-Score standardization. α, β, γ, and δ are set according to actual conditions. For example, the expert empowerment method is adopted, that is, experts in related fields are invited to determine the preset proportional coefficients of various indicators through professional opinion surveys and comprehensive evaluations.

[0100] It can be seen from the above calculation expression that the larger the cooling efficiency fluctuation coefficient, the larger the bearing vibration rise coefficient, the larger the motor phase angle abnormality coefficient, and the larger the insulation resistance drop coefficient, the larger the drive motor hidden danger monitoring index is, indicating that the probability of the drive motor operation hidden danger is greater. Conversely, the smaller the cooling efficiency fluctuation coefficient, the smaller the bearing vibration rise coefficient, the smaller the motor phase angle abnormality coefficient, and the smaller the insulation resistance drop coefficient, the smaller the drive motor hidden danger monitoring index is, indicating that the probability of the drive motor operation hidden danger is smaller.

[0101] Step S3, analyzing the operating hidden dangers of the main motor according to the operating hidden danger monitoring model of the drive motor of the textile factory to determine whether the main motor has operating hidden dangers;

[0102] The operation hidden dangers of the main motor are analyzed according to the operation hidden danger monitoring model of the drive motor of the textile factory, and the drive motor hidden danger monitoring index generated by the operation hidden danger monitoring model of the drive motor of the textile factory is compared with the preset drive motor hidden danger monitoring index threshold;

[0103] If the drive motor hidden danger monitoring index is greater than the drive motor hidden danger monitoring index threshold, it indicates that the main motor has an operation hidden danger, and a main motor operation hidden danger signal is generated;

[0104] If the drive motor hidden danger monitoring index is less than or equal to the drive motor hidden danger monitoring index threshold, it indicates that the main motor has no operation hidden danger, and there is no need to generate a main motor operation hidden danger signal;

[0105] Step S4, when the main motor has an operation hazard, the operation hazard analysis of the slave motor is performed according to the textile factory drive motor operation hazard monitoring model to obtain a main motor candidate ranking list;

[0106] When the main motor operation hidden danger signal is generated, the operation hidden danger analysis of the slave motor is performed according to the textile mill drive motor operation hidden danger monitoring model, and the drive motor hidden danger monitoring index generated by the textile mill drive motor operation hidden danger monitoring model is compared with the preset drive motor hidden danger monitoring index threshold;

[0107] If the drive motor hidden danger monitoring index is greater than the drive motor hidden danger monitoring index threshold, it indicates that the slave motor has an operation hidden danger, and an early warning signal is generated to indicate the operation hidden danger of the slave motor and prompt relevant personnel to perform inspection and maintenance;

[0108] If the drive motor hidden danger monitoring index is less than or equal to the drive motor hidden danger monitoring index threshold, it indicates that the slave motor has no operation hidden danger, and a main motor candidate ranking table is generated for the slave motors from small to large according to the drive motor hidden danger monitoring index;

[0109] Step S5, obtaining the time difference when the main motor has operating hidden dangers and combining it with the task complexity of the current production task, constructing a main motor dynamic adjustment model to determine whether to replace the main motor immediately;

[0110] Record the time when the main motor operation hidden danger signal is generated, and perform a difference operation between the current time and the time when the main motor operation hidden danger signal is generated to obtain the time difference Sjc when the main motor has an operation hidden danger;

[0111] The task complexity of the current production task refers to the complexity of the production task of the current link that the main motor is responsible for. The task complexity of the current production task is measured by the total number of processes Zgx of the production task of the current link;

[0112] According to the time difference of the main motor running hidden dangers and the total number of processes, the main motor dynamic adjustment model is constructed to generate the main motor dynamic adjustment index Tzds. The model is based on the following formula Tzds = ρ*Sjc+σ*Zgx, where ρ and σ represent the preset proportional coefficients of the time difference of the main motor running hidden dangers and the total number of processes, respectively, and ρ and σ are both greater than 0;

[0113] It should be noted that before constructing the dynamic adjustment model of the main motor, it is necessary to ensure that the time difference and the total number of processes of the main motor with hidden dangers in operation are normalized. Common normalization methods include Min-Max normalization and Z-Score standardization. ρ and σ are set according to the actual situation. For example, the expert empowerment method is adopted, that is, experts in related fields are invited to determine the preset proportional coefficients of various indicators through professional opinion surveys and comprehensive evaluations.

[0114] It can be seen from the above calculation expression that the greater the time difference of the main motor's operation hidden danger and the greater the total number of processes, the greater the main motor's dynamic adjustment index, indicating that the longer the main motor's operation hidden danger is and the more complex the current production task is, the greater the probability of immediately replacing the main motor; conversely, the smaller the time difference of the main motor's operation hidden danger and the smaller the total number of processes, the smaller the main motor's dynamic adjustment index, indicating that the shorter the main motor's operation hidden danger is and the simpler the current production task is, the smaller the probability of immediately replacing the main motor is;

[0115] The main motor dynamic adjustment index is compared with a preset main motor dynamic adjustment index threshold value. If the main motor dynamic adjustment index is greater than the main motor dynamic adjustment index threshold value, a main motor immediate replacement signal is generated, and the main motor is replaced with the first motor in the main motor candidate ranking table;

[0116] If the main motor dynamic adjustment index is less than or equal to the main motor dynamic adjustment index threshold, there is no need to generate a main motor immediate replacement signal;

[0117] The present invention constructs a textile factory drive motor operation hidden danger monitoring model by acquiring cooling efficiency fluctuation information and bearing vibration rise information, motor phase angle abnormality information and motor winding insulation resistance drop information during the operation process of the textile factory drive motor, generates a drive motor hidden danger monitoring index to analyze the operation hidden danger of the main motor, timely perceives the operation hidden danger of the main motor, avoids equipment replacement only after the failure occurs, thereby reducing downtime and production losses, and the textile factory drive motor operation hidden danger monitoring model analyzes the operation hidden danger of the slave motor, selects the slave motor with stable operation from the slave motor, obtains the main motor candidate ranking list, and simultaneously obtains the time difference of the main motor having the operation hidden danger and combines the task complexity of the current production task to construct the main motor dynamic adjustment model, according to the changes in the production environment, flexibly judges whether to replace the main motor immediately, thereby optimizing production scheduling and resource allocation, ensuring the continuity and stability of the production process, optimizing the dynamic selection of the main motor, and improving the production safety and efficiency of the textile factory;

[0118] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0119] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0120] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A textile mill drive motor control method based on data analysis, characterized in that: The steps include: Step S1, obtaining cooling efficiency fluctuation information, bearing vibration increase information, motor phase angle abnormality information, and motor winding insulation resistance decrease information during the operation of the textile factory drive motor; Step S2, constructing a hidden danger monitoring model for the operation of the drive motor of the textile factory, analyzing the abnormal hidden dangers of the operation of the drive motor of the textile factory, generating a hidden danger monitoring index for the drive motor, and comparing the hidden danger monitoring index for the drive motor with a preset hidden danger monitoring index threshold for the drive motor, so as to evaluate the hidden dangers of the operation of the drive motor; Step S3, analyzing the operating hidden dangers of the main motor according to the operating hidden danger monitoring model of the drive motor of the textile factory to determine whether the main motor has operating hidden dangers; Step S4, when the main motor has an operation hazard, the operation hazard analysis of the slave motor is performed according to the textile factory drive motor operation hazard monitoring model to obtain a main motor candidate ranking list; Step S5, obtaining the time difference when the main motor has operating hazards and combining it with the task complexity of the current production task, constructing a main motor dynamic adjustment model to determine whether to replace the main motor immediately.

2. The method for controlling a textile mill drive motor based on data analysis according to claim 1, characterized in that: The cooling efficiency fluctuation information includes the cooling efficiency fluctuation coefficient, the bearing vibration increase information includes the bearing vibration increase coefficient, the motor phase angle abnormality information includes the motor phase angle abnormality coefficient, and the motor winding insulation resistance decrease information includes the insulation resistance decrease coefficient.

3. The textile mill drive motor control method based on data analysis according to claim 2 is characterized in that: By acquiring cooling efficiency fluctuation information during the operation of the drive motor, analyzing the cooling efficiency fluctuation during the operation of the drive motor, and acquiring a cooling efficiency fluctuation coefficient, the degree of cooling efficiency fluctuation during the operation of the drive motor is measured; The logic for obtaining the cooling efficiency fluctuation coefficient is as follows: Preset a fixed time period T, obtain the cooling efficiency data in the adjacent time period, and mark the cooling efficiency data time series in the T time period as Q = {ql i,k }={ql 1,k ,ql 2,k ,...,ql I,k }, where ql i,k represents the kth cooling efficiency data feature at the i-th time in the time period, i = {1, 2, ..., I}, I is a positive integer; the cooling efficiency data time series in the T+1 time period is marked as C = {cl j,k }={cl 1,k ,cl 2,k ,...,cl J,k }, where cl j,k represents the kth cooling efficiency data feature at the jth moment in the time period, j={1,2,...,J}, J is a positive integer; According to each cooling efficiency data feature, the Euclidean distance at each moment in the cooling efficiency data time series is calculated to obtain the distance matrix D, where each element D(i,j) in the distance matrix represents ql i,k and cl j,k The Euclidean distance is calculated as follows: Construct a cumulative distance matrix ML to store the minimum cumulative distance from the starting point (1,1) to each point (i,j). The calculation rule of the cumulative distance is as follows: ML(i,j)=D(i,j)+min{ML(i-1,j),ML(i,j-1),ML(i-1,j-1)}, where ML(i,j) represents ql i,k and cl j,k The minimum cumulative distance of Calculate the cooling efficiency fluctuation coefficient Lqbd, the expression is as follows Where YA represents the sum of the elements in the cumulative distance matrix.

4. The textile mill drive motor control method based on data analysis according to claim 2 is characterized in that: By acquiring the bearing vibration rise information during the operation of the drive motor, analyzing the abnormal rise of the bearing vibration during the operation of the drive motor, and acquiring the bearing vibration rise coefficient, the degree of abnormal rise of the bearing vibration during the operation of the drive motor is measured; The logic for obtaining the bearing vibration rise coefficient is as follows: Obtain the bearing vibration data signal x(t) during the operation of the drive motor, and use the EMD algorithm to decompose the bearing vibration data signal to obtain the inherent mode function and residual component. The decomposition formula is as follows Where x(t) is the original bearing vibration data signal, which represents the vibration data collected at time t, and IMF n (t) represents the value of the nth intrinsic mode function at time t, n = {1, 2, ..., N}, N is a positive integer, ca(t) represents the residual component at time t; Calculating IMF n (t) The instantaneous energy EIMF n (t), the expression is as follows EIMF n (t) = IMF n (t) 2 ; Preset the time window ΔT and calculate the energy change rate Nh. The expression is as follows EIMF n (t) represents the instantaneous energy of the nth intrinsic mode function at time t; Calculate the average energy change rate The expression is as follows Where Nh(t,ΔT m ) represents the energy change rate at time t and the mth time window, m = {1, 2, ..., M}, M is a positive integer; Calculate IMF using Hilbert transform n The analytical signal JX of (t) n (t), the expression is as follows JX n (t) = IMF n (t)+xs*XB(IMF n (t)), where JX n (t) represents IMF n (t), xs represents the imaginary unit, XB(IMF n (t)) represents IMF n Hilbert transform of (t); Calculating IMF n (t) is the instantaneous frequency Calculate the bearing vibration rise coefficient Zcsx, the expression is as follows 5. The textile mill drive motor control method based on data analysis according to claim 2 is characterized in that: By acquiring abnormal information of the motor phase angle during the operation of the drive motor, analyzing the abnormal fluctuation of the motor phase angle during the operation of the drive motor, and acquiring the abnormal coefficient of the motor phase angle, the abnormal fluctuation degree of the motor phase angle during the operation of the drive motor is measured; The logic for obtaining the motor phase angle abnormality coefficient is as follows: Get the current and voltage data of the drive motor when it is running, and calculate the motor phase angle. The expression is as follows where θ r Indicates the motor phase angle at the rth moment, DL r represents the current at the rth moment, DY r represents the voltage at the rth moment; Calculate the average motor phase angle xpj, the expression is as follows Calculate the standard deviation of the motor phase angle xbz, the expression is as follows Calculate the motor phase angle abnormality coefficient Xwjy, the expression is as follows 6. The textile mill drive motor control method based on data analysis according to claim 2 is characterized in that: By acquiring the insulation resistance drop information of the motor winding during the operation of the drive motor, analyzing the abnormal insulation resistance drop of the motor winding during the operation of the drive motor, and acquiring the insulation resistance drop coefficient, the degree of abnormal insulation resistance drop of the motor winding during the operation of the drive motor is measured; The logic for obtaining the insulation resistance reduction coefficient is as follows: Preset multiple monitoring points to obtain the insulation resistance value dz of the drive motor winding b ,dz b represents the insulation resistance value of the b-th monitoring point, b={1,2,...,B}, B is a positive integer; Calculate the average insulation resistance value The expression is as follows The insulation resistance values ​​of the drive motor windings at different monitoring points are compared with the average insulation resistance values, and the insulation resistance values ​​that are less than the average insulation resistance values ​​are marked as insulation resistance abnormal values, and the insulation resistance abnormal values ​​are added to the insulation resistance abnormal set, and the insulation resistance abnormal set is marked as BJH={dzc y }={dzc1,dzc2,...,dzc Y }, where y = {1, 2, ..., Y}, Y is a positive integer; The insulation resistance reduction coefficient Jydx is calculated based on the insulation resistance abnormal value in the insulation resistance abnormal set, and is expressed as follows 7. The textile mill drive motor control method based on data analysis according to claim 2 is characterized in that: According to the cooling efficiency fluctuation coefficient, bearing vibration rise coefficient, motor phase angle abnormality coefficient, and insulation resistance drop coefficient, a textile mill drive motor operation hidden danger monitoring model is constructed to generate the drive motor hidden danger monitoring index Qdyz. The model is based on the following formula Wherein α, β, γ, and δ represent preset proportional coefficients of cooling efficiency fluctuation coefficient, bearing vibration rise coefficient, motor phase angle abnormality coefficient, and insulation resistance decrease coefficient, respectively, and α, β, γ, and δ are all greater than 0.

8. The method for controlling a textile mill drive motor based on data analysis according to claim 7 is characterized in that: The operation hidden dangers of the main motor are analyzed according to the operation hidden danger monitoring model of the drive motor of the textile factory, and the drive motor hidden danger monitoring index generated by the operation hidden danger monitoring model of the drive motor of the textile factory is compared with the preset drive motor hidden danger monitoring index threshold; If the drive motor hidden danger monitoring index is greater than the drive motor hidden danger monitoring index threshold, a main motor operation hidden danger signal is generated; If the drive motor hidden danger monitoring index is less than or equal to the drive motor hidden danger monitoring index threshold, there is no need to generate a main motor operation hidden danger signal.

9. The textile mill drive motor control method based on data analysis according to claim 8 is characterized in that: When the main motor operation hidden danger signal is generated, the operation hidden danger analysis of the slave motor is performed according to the textile mill drive motor operation hidden danger monitoring model, and the drive motor hidden danger monitoring index generated by the textile mill drive motor operation hidden danger monitoring model is compared with the preset drive motor hidden danger monitoring index threshold; If the drive motor hidden danger monitoring index is greater than the drive motor hidden danger monitoring index threshold, a warning signal is generated; If the drive motor hidden danger monitoring index is less than or equal to the drive motor hidden danger monitoring index threshold, a main motor candidate ranking table is generated for the slave motors from small to large according to the drive motor hidden danger monitoring index.

10. The textile mill drive motor control method based on data analysis according to claim 9, characterized in that: Record the time when the main motor operation hidden danger signal is generated, and perform a difference operation between the current time and the time when the main motor operation hidden danger signal is generated to obtain the time difference Sjc when the main motor has an operation hidden danger; The task complexity of the current production task refers to the complexity of the production task of the current link that the main motor is responsible for. The task complexity of the current production task is measured by the total number of processes Zgx of the production task of the current link; According to the time difference of the main motor running hidden dangers and the total number of processes, the main motor dynamic adjustment model is constructed to generate the main motor dynamic adjustment index Tzds. The model is based on the following formula Tzds = ρ*Sjc+σ*Zgx, where ρ and σ represent the preset proportional coefficients of the time difference of the main motor running hidden dangers and the total number of processes, respectively, and ρ and σ are both greater than 0; The main motor dynamic adjustment index is compared with a preset main motor dynamic adjustment index threshold value. If the main motor dynamic adjustment index is greater than the main motor dynamic adjustment index threshold value, a main motor immediate replacement signal is generated, and the main motor is replaced with the first motor in the main motor candidate ranking table; If the main motor dynamic adjustment index is less than or equal to the main motor dynamic adjustment index threshold, there is no need to generate a main motor immediate replacement signal.

Images