Intelligent control system for wire tension of wire drawing machine and control method thereof

Abstract

The invention discloses an intelligent control system for wire tension of a wire drawing machine and a control method thereof, and relates to the technical field of industrial automation. During operation of the system, a data preprocessing module, a multi-parameter analysis module, a fault prediction and early warning module and an intelligent control and optimization module are integrated; the method comprises the following steps: acquiring and analyzing running state information and environmental data of a wire drawing machine in real time, establishing a tension dynamic model, a thermodynamic tension distribution model and a dynamic humidity influence model of the wire drawing machine, and obtaining a fault prediction index value PF (t) of the wire drawing machine; the wire drawing machine is regulated and controlled according to the preliminary assessment risk value SF (t) and the secondary fault risk value SF2 (t) obtained through assessment, the running state information of the regulated wire drawing machine is fed back, potential equipment faults are predicted, running parameters are automatically adjusted to ensure continuous and stable running of equipment, the wire tension can be accurately monitored and regulated, and the production efficiency is improved. And equipment faults caused by tension fluctuation are reduced.

Description

Technical Field

[0001] The invention relates to the technical field of wire production control, and in particular to an intelligent control system for wire tension of a wire drawing machine and a control method thereof. Background Art

[0002] A wire drawing machine is a machine that draws coils into thin iron wires. As a key equipment in wire manufacturing, its main function is to draw materials into wires of required specifications through a certain tension. With the development of industry, wire drawing machines are gradually transforming towards automation and intelligence. Especially in metal processing and wire production, how to intelligently control wire production to improve product quality and production efficiency has gradually become the focus of many manufacturing companies. In the high-precision manufacturing process of wire production, tension control is crucial because it directly affects the diameter, surface quality and mechanical properties of the wire.

[0003] However, in the current wire drawing machine wire production, although some automated control methods have been introduced, there are still many problems; for example, traditional tension control systems mostly rely on the preset of fixed parameters, and cannot respond to the complex and changeable production environment in real time. When the tension or speed fluctuates abnormally, it is often impossible to detect and deal with it in time, resulting in wire breakage, surface defects, uneven diameter and other quality problems; at the same time, equipment failures are often discovered only after the problem occurs, and no early warning is given, which not only leads to production interruptions, but may also cause more serious equipment damage and safety hazards.

[0004] Therefore, how to intelligently control the tension in the wire production process is a technical problem that technicians currently need to solve. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides an intelligent control system for wire tension of a wire drawing machine and a control method thereof, which solve the problems mentioned in the background technology.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions. In a first aspect, the present application provides an intelligent control system for wire tension of a wire drawing machine, including a data preprocessing module, a multi-parameter analysis module, a fault prediction and early warning module, and an intelligent control and optimization module;

[0007] The data preprocessing module collects the running status information and running area environment information of the wire drawing machine in real time through the sensor network to form the wire drawing machine collection data set X raw (t), and preprocess the wire drawing machine data set, including denoising preprocessing, data normalization preprocessing and outlier detection preprocessing, to form the processed data feature vector set X feat (t);

[0008] The multi-parameter analysis module is based on the data feature vector set X feat (t) Establish the wire drawing machine tension dynamic model, thermodynamic tension distribution model and dynamic humidity influence model, and obtain the dynamic tension value T through training and analysis dyn (t), tension gradient distribution value ▽T(x, t) and humidity influence value △H(t);

[0009] The fault prediction and warning module calculates the dynamic tension value T dyn (t), tension gradient distribution value ▽T(x, t) and humidity influence value △H(t) are fitted to obtain the wire drawing machine failure prediction index value P F (t), and the wire drawing machine failure prediction index value P F (t) Conduct a preliminary assessment and obtain a preliminary assessment risk value S F (t) To judge and regulate the operation status of the wire drawing machine, and to perform a secondary evaluation on the regulated wire drawing machine to obtain the secondary failure risk value S F2 (t);

[0010] The intelligent control and optimization module is based on the preliminary assessment of the risk value S F (t) and the secondary failure risk value S F2 (t) Regulate the wire drawing machine and provide feedback on the operating status of the adjusted wire drawing machine.

[0011] Preferably, the data preprocessing module includes a data acquisition unit and a data processing unit;

[0012] The data acquisition unit is connected to the Internet network through the Internet of Things sensor to collect the operating status information of the wire drawing machine and the operating area environment information in real time, including the tension applied by the wire drawing machine on the wire, the wire moving speed, the vibration frequency of the equipment, the temperature in the operating environment and the humidity in the operating environment, forming the wire drawing machine collection data set X raw (t), including the length of the output material L(t), tension T(t), speed V(t), vibration frequency F v (t), temperature T env and humidity S(t);

[0013] Among them, IoT sensors include tension sensors, speed sensors, vibration sensors, temperature sensors and humidity sensors.

[0014] Preferably, the data processing unit collects data set X from the wire drawing machine. raw (t) Perform preprocessing, including denoising preprocessing, data normalization preprocessing and outlier detection preprocessing, to form the processed data feature vector set X feat (t);

[0015] Among them, the denoising preprocessing includes using wavelet transform and Kalman filter to collect data set X of the wire drawing machine raw (t) Perform denoising to obtain the denoised data set X after denoising at time t denoise (t);

[0016] Data normalization preprocessing includes using principal component analysis PCA to denoise the denoised dataset X after time t denoise (t) Perform normalization processing to obtain the normalized data set Xnorm(t) normalized at time t;

[0017] The outlier detection preprocessing includes using the isolation forest algorithm to perform outlier detection on the normalized data set Xnorm(t) normalized at time t, and propose extremely abnormal data points to form the data feature vector set X at time t. feat (t).

[0018] Preferably, the multi-parameter analysis module comprises a modeling unit;

[0019] The modeling unit is based on the data feature vector set X feat (t) combining the inherent characteristic information of the material and the initial state information of the wire drawing machine as the input data set to establish the wire drawing machine tension dynamic model, thermodynamic tension distribution model and dynamic humidity influence model;

[0020] Among them, the inherent characteristic information of the material includes elastic modulus E, stress relaxation rate SR(t), thermal conductivity k(T), density p and specific heat capacity C p ; The initial state information of the wire drawing machine includes the initial tension T of the equipment 0 and the initial length L of the material 0 ;

[0021] Through the training and analysis of the wire drawing machine tension dynamic model, the tension of the material during the wire drawing process changes with time, and the dynamic tension value T is obtained. dyn (t);

[0022] Through the training and analysis of the thermodynamic tension distribution model, the changing trend of tension at different locations is predicted, and it is used as the basis for adjusting the equipment operating parameters to avoid equipment damage or material breakage caused by local tension anomalies, and obtain the tension gradient distribution value ▽T(x, t);

[0023] By accumulating the long-term impact of humidity changes on equipment operation, the corresponding maintenance and adjustment plans are formulated to achieve a stable operating state of the equipment under different environmental conditions and obtain the humidity impact value △H(t).

[0024] Preferably, the dynamic tension value T dyn (t) is obtained by the following calculation formula:

[0025]

[0026] Where, E represents the elastic modulus, specifically the stiffness of the material, L(t) represents the length of the material at time t, specifically the length of the material processed by the wire drawing machine, and L 0 represents the initial length of the material, T 0 represents the initial tension, SR(t) represents the stress relaxation rate, K F Indicates the vibration frequency F v The preset influence weight value of (t), represents the integral term from time 0 to time t, and τ represents the time τ from time 0 to time t;

[0027] The tension gradient distribution value Obtained through the following calculation formula:

[0028]

[0029] In the formula, k(T) represents the thermal conductivity, which specifically indicates the thermal conductivity of the material at temperature T env Thermal conductivity, T env represents the temperature of the material, p represents the density of the material, C p represents the specific heat capacity, represents the first-order partial derivative of the tension T with respect to the position x, specifically the rate of change of the tension T along the spatial coordinate x, It represents the second-order partial derivative of tension T with respect to time t, and represents the acceleration change state of tension T at position x with respect to time t.

[0030] The humidity impact value △H(t) is obtained by the following calculation formula:

[0031]

[0032] Where V(t) represents the speed, specifically the speed of the wire when it is stretched. represents the rate of change of humidity S(t) at time τ, S(τ, T env (τ)) represents the humidity influence function, specifically the material temperature T at time τ env Impact on changes in humidity S(t).

[0033] Preferably, the fault prediction and early warning module includes a fault prediction unit and a risk assessment unit;

[0034] The fault prediction unit calculates the dynamic tension value T dyn (t), tension gradient distribution value The humidity influence value △H(t) is fitted, including the use of multiple regression and machine learning algorithms to obtain the wire drawing machine failure prediction index value P F (t);

[0035] The wire drawing machine fault prediction index value P F (t) is fitted by the following calculation formula:

[0036]

[0037] In the formula, It represents the square integral of the tension gradient distribution value, specifically the cumulative effect of the distribution of tension T along the material length L of the wire;

[0038] The risk assessment unit also calculates the wire drawing machine failure prediction index value P F (t) Conduct a preliminary assessment and obtain a preliminary assessment risk value S F (t) To judge and regulate the operation status of the wire drawing machine, and to perform a secondary evaluation on the regulated wire drawing machine to obtain the secondary failure risk value S F2 (t).

[0039] Preferably, the preliminary risk assessment value S F (t) is obtained by the following calculation formula:

[0040]

[0041] In the formula, Indicates the wire drawing machine failure prediction index value P F The derivative of (t) with respect to time τ, specifically the rate of change over time and the value of P F (τ) The changing trend over time τ;

[0042] The secondary failure risk value S F2 (t) is obtained by the following calculation formula:

[0043]

[0044] In the formula, It represents the derivative of the rate of change of tension T with respect to the rate of change of velocity V at time τ, It represents the first-order partial derivative of the tension T with respect to the position x, specifically the rate of change of the tension T along the spatial coordinate x.

[0045] Preferably, the intelligent control and optimization module includes a regulation execution unit and a feedback optimization unit;

[0046] The control execution unit is based on the preliminary assessment risk value S F(t) Matching with the preset preliminary control evaluation threshold CZ, obtaining the preliminary control evaluation result, and generating the wire drawing machine control strategy plan according to the result, and executing the wire drawing machine control strategy plan content to perform a fixed period of secondary failure risk value S F2 (t) matching with the preset post-regulation secondary evaluation threshold EZ, obtaining the post-regulation secondary evaluation result, and obtaining the secondary regulation evaluation strategy scheme for executing the wire drawing machine regulation strategy scheme according to the result;

[0047] The feedback optimization unit triggers a fixed period to detect the parameters of the wire drawing machine after the operating status is regulated according to the execution of the wire drawing machine regulation strategy, and specifically executes the generated secondary regulation evaluation strategy content to provide feedback on the operating status information of the wire drawing machine after regulation.

[0048] Preferably, the preliminary control evaluation result is obtained by the following matching method:

[0049] When the risk value S is initially assessed F When (t) is less than the initial control evaluation threshold CZ, the result of the initial control evaluation is qualified, and a strategy plan for not controlling the wire drawing machine is generated;

[0050] When the risk value S is initially assessed F When (t) ≥ the initial control evaluation threshold CZ, the result of the initial control evaluation is unqualified, and a strategy for controlling the wire drawing machine is generated. The control includes controlling the tension T(t) and speed V(t) of the wire drawing machine;

[0051] The secondary evaluation results after regulation are obtained through the following matching method:

[0052] When the secondary failure risk value S F2 When (t) is less than the secondary evaluation threshold EZ after control, the secondary evaluation result after control is obtained to be qualified, and a secondary non-control evaluation strategy plan for the wire drawing machine control is generated;

[0053] When the secondary failure risk value S F2 When (t)≥the secondary evaluation threshold value EZ after control, the secondary evaluation result after control is obtained as unqualified, and a secondary control evaluation strategy plan for the wire drawing machine control is generated, including control and notification. The control includes controlling the tension T(t) and speed V(t) of the wire drawing machine; the notification includes a visualization page prompt notification of abnormal operating status of the wire drawing machine.

[0054] The second aspect of the present application provides an intelligent control method for wire tension of a wire drawing machine, comprising the following steps:

[0055] Step 1: The data preprocessing module collects the operating status information and operating area environment information of the wire drawing machine in real time through the sensor network to form the wire drawing machine collection data set X raw(t), and preprocess the wire drawing machine data set, including denoising preprocessing, data normalization preprocessing and outlier detection preprocessing, to form the processed data feature vector set X feat (t);

[0056] Step 2: The multi-parameter analysis module is based on the data feature vector set X feat (t) Establish the wire drawing machine tension dynamic model, thermodynamic tension distribution model and dynamic humidity influence model, and obtain the dynamic tension value T through training and analysis dyn (t), tension gradient distribution value ▽T(x, t) and humidity influence value △H(t);

[0057] Step 3: The fault prediction and warning module calculates the dynamic tension value T dyn (t), tension gradient distribution value ▽T(x, t) and humidity influence value △H(t) are fitted to obtain the wire drawing machine failure prediction index value P F (t), and the wire drawing machine failure prediction index value P F (t) Conduct a preliminary assessment and obtain a preliminary assessment risk value S F (t) To judge and regulate the operation status of the wire drawing machine, and to perform a secondary evaluation on the regulated wire drawing machine to obtain the secondary failure risk value S F2 (t);

[0058] Step 4: The intelligent control and optimization module evaluates the risk value S based on the preliminary assessment. F (t) and the secondary failure risk value S F2 (t) Regulate the wire drawing machine and provide feedback on the operating status of the adjusted wire drawing machine.

[0059] The present invention provides an intelligent control system and control method for wire tension of a wire drawing machine, which has the following beneficial effects:

[0060] (1) When the system is running, the data preprocessing module, multi-parameter analysis module, fault prediction and warning module, and intelligent control and optimization module are integrated to collect and analyze the operating status information and environmental data of the wire drawing machine in real time. The wire drawing machine tension dynamic model, thermodynamic tension distribution model, and dynamic humidity influence model are constructed. The dynamic tension value T is obtained through training and analysis. dyn (t), tension gradient distribution value ▽T(x, t) and humidity influence value △H(t), and obtain the wire drawing machine failure prediction index value P through fitting. F (t), and obtain the risk value S according to the preliminary assessment through evaluation F (t) and the secondary failure risk value S F2(t) Regulate the wire drawing machine and provide feedback on the operating status of the adjusted wire drawing machine, predict potential equipment failures, and automatically adjust operating parameters to ensure the continued stable operation of the equipment. It can accurately monitor and regulate wire tension, reduce equipment failures caused by tension fluctuations, and significantly reduce downtime, thereby improving the overall safety and efficiency of the production line.

[0061] (2) By obtaining the dynamic tension value Tdyn(t), the tension gradient distribution value ▽T(x, t) and the humidity influence value △H(t), the accuracy and predictability of the tension control of the wire drawing machine are achieved. By combining the inherent characteristic information of the material and the initial state of the equipment, the wire drawing machine tension dynamic model, thermodynamic tension distribution model and dynamic humidity influence model are established, which can carefully reflect the tension changes of the wire during the wire drawing process, and can also predict possible equipment failures through fitting analysis, and adjust the operating parameters in real time to avoid equipment damage caused by local tension abnormalities or environmental changes. At the same time, it can focus on the analysis of the humidity accumulation effect during long-term operation to ensure that the equipment always maintains stability under different environmental conditions.

[0062] (3) Based on the preliminary assessment of risk value S F (t) Matching with the preset preliminary control evaluation threshold CZ, obtaining the preliminary control evaluation result, and generating the wire drawing machine control strategy plan according to the result, and executing the wire drawing machine control strategy plan content to perform a fixed period of secondary failure risk value S F2 (t) Match with the preset post-regulation secondary evaluation threshold EZ, obtain the post-regulation secondary evaluation result, and obtain the secondary regulation evaluation strategy for the wire drawing machine regulation strategy according to the result, so as to judge the effectiveness of the preliminary regulation in real time and optimize the operation status of the equipment. Detect and analyze the operating parameters of the wire drawing machine after regulation in each cycle to ensure that the execution effect of the regulation strategy reaches the expected result. Carry out continuous and closed-loop regulation and feedback according to the evaluation results, accurately adjust the tension and speed of the wire drawing machine, reduce the equipment failure rate, and provide safer and more reliable production guarantee by notifying the system of abnormal conditions in real time, thereby significantly improving the operation efficiency and product quality of the wire drawing machine. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 It is a schematic diagram of a block diagram of an intelligent control system for wire tension of a wire drawing machine according to the present invention;

[0064] Figure 2 The present invention is a schematic diagram of the steps of an intelligent control method for wire tension of a wire drawing machine. DETAILED DESCRIPTION

[0065] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0066] Example 1

[0067] The present invention provides an intelligent control system for the wire tension of a wire drawing machine. Figure 1 , including data preprocessing module, multi-parameter analysis module, fault prediction and warning module and intelligent control and optimization module;

[0068] The data preprocessing module collects the running status information and running area environment information of the wire drawing machine in real time through the sensor network to form the wire drawing machine collection data set X raw (t), and preprocess the wire drawing machine data set, including denoising preprocessing, data normalization preprocessing and outlier detection preprocessing, to form the processed data feature vector set X feat (t);

[0069] The multi-parameter analysis module is based on the data feature vector set X feat (t) Establish the wire drawing machine tension dynamic model, thermodynamic tension distribution model and dynamic humidity influence model, and obtain the dynamic tension value T through training and analysis dyn (t), tension gradient distribution value ▽T(x, t) and humidity influence value △H(t);

[0070] The fault prediction and early warning module calculates the dynamic tension value T dyn (t), tension gradient distribution value ▽T(x, t) and humidity influence value △H(t) are fitted to obtain the wire drawing machine failure prediction index value P F (t), and the wire drawing machine failure prediction index value P F (t) Conduct a preliminary assessment and obtain a preliminary assessment risk value S F (t) To judge and regulate the operation status of the wire drawing machine, and to perform a secondary evaluation on the regulated wire drawing machine to obtain the secondary failure risk value S F2 (t);

[0071] The intelligent control and optimization module is based on the preliminary assessment of the risk value S F (t) and the secondary failure risk value S F2 (t) Regulate the wire drawing machine and provide feedback on the operating status of the adjusted wire drawing machine.

[0072] In this embodiment, by integrating the data preprocessing module, the multi-parameter analysis module, the fault prediction and warning module, and the intelligent control and optimization module, the operating status information and environmental data of the wire drawing machine are collected and analyzed in real time, and a wire drawing machine tension dynamic model, a thermodynamic tension distribution model, and a dynamic humidity influence model are constructed to obtain the dynamic tension value T through training and analysis. dyn (t), tension gradient distribution value ▽T(x, t) and humidity influence value △H(t), and obtain the wire drawing machine failure prediction index value P through fitting. F (t), and obtain the risk value S according to the preliminary assessment through evaluation F (t) and the secondary failure risk value S F2 (t) Regulate the wire drawing machine and provide feedback on the operating status of the adjusted wire drawing machine, predict potential equipment failures, and automatically adjust operating parameters to ensure the continued stable operation of the equipment. It can accurately monitor and regulate wire tension, reduce equipment failures caused by tension fluctuations, and significantly reduce downtime, thereby improving the overall safety and efficiency of the production line.

[0073] Example 2

[0074] This embodiment is explained in Example 1, please refer to Figure 1 ,Specifically: the data preprocessing module includes a data acquisition unit and a data processing unit;

[0075] The data acquisition unit is connected to the Internet network through the Internet of Things sensor to collect the operating status information of the wire drawing machine and the operating area environment information in real time, including the tension applied by the wire drawing machine on the wire, the wire moving speed, the vibration frequency of the equipment, the temperature in the operating environment and the humidity in the operating environment, forming the wire drawing machine collection data set X raw (t), including the length of the output material L(t), tension T(t), speed V(t), vibration frequency F v (t), temperature T env and humidity S(t);

[0076] Among them, IoT sensors include tension sensors, speed sensors, vibration sensors, temperature sensors and humidity sensors.

[0077] The data processing unit collects data set X from the wire drawing machine raw (t) Perform preprocessing, including denoising preprocessing, data normalization preprocessing and outlier detection preprocessing, to form the processed data feature vector set X feat (t);

[0078] Among them, the denoising preprocessing includes using wavelet transform and Kalman filter to collect data set X of the wire drawing machine raw(t) Perform denoising to obtain the denoised data set X after denoising at time t denoise (t);

[0079] Data normalization preprocessing includes using principal component analysis PCA to denoise the denoised dataset X after time t denoise (t) Perform normalization processing to obtain the normalized data set Xnorm(t) normalized at time t;

[0080] The outlier detection preprocessing includes using the isolation forest algorithm to perform outlier detection on the normalized data set Xnorm(t) normalized at time t, and propose extremely abnormal data points to form the data feature vector set X at time t. feat (t).

[0081] Example 3

[0082] This embodiment is explained in Example 1, please refer to Figure 1 ,Specifically: the multi-parameter analysis module includes a modeling unit;

[0083] The modeling unit is based on the data feature vector set X feat (t) combining the inherent characteristic information of the material and the initial state information of the wire drawing machine as the input data set to establish the wire drawing machine tension dynamic model, thermodynamic tension distribution model and dynamic humidity influence model;

[0084] Among them, the inherent characteristic information of the material includes elastic modulus E, stress relaxation rate SR(t), thermal conductivity k(T), density p and specific heat capacity C p ; The initial state information of the wire drawing machine includes the initial tension T of the equipment 0 and the initial length L of the material 0 ;

[0085] Through the training and analysis of the wire drawing machine tension dynamic model, the tension of the material during the wire drawing process changes with time, and the dynamic tension value T is obtained. dyn (t);

[0086] Through the training and analysis of the thermodynamic tension distribution model, the changing trend of tension at different locations is predicted, and it is used as the basis for adjusting the equipment operating parameters to avoid equipment damage or material breakage caused by local tension anomalies, and obtain the tension gradient distribution value ▽T(x, t);

[0087] By accumulating the long-term impact of humidity changes on equipment operation, the humidity impact value △H(t) is obtained as the basis for formulating corresponding maintenance and adjustment plans, so as to achieve a stable operation state of the equipment under different environmental conditions.

[0088] The dynamic tension value T dyn(t) is obtained by the following calculation formula:

[0089]

[0090] Where, E represents the elastic modulus, specifically the stiffness of the material, L(t) represents the length of the material at time t, specifically the length of the material processed by the wire drawing machine, and L 0 represents the initial length of the material, T 0 represents the initial tension, SR(t) represents the stress relaxation rate, K F Indicates the vibration frequency F v The preset influence weight value of (t), represents the integral term from time 0 to time t, and τ represents the time τ from time 0 to time t;

[0091] The tension gradient distribution value Obtained through the following calculation formula:

[0092]

[0093] In the formula, k(T) represents the thermal conductivity, which specifically indicates the thermal conductivity of the material at temperature T env Thermal conductivity, T env represents the temperature of the material, p represents the density of the material, C p represents the specific heat capacity, represents the first-order partial derivative of the tension T with respect to the position x, specifically the rate of change of the tension T along the spatial coordinate x, It represents the second-order partial derivative of tension T with respect to time t, and represents the acceleration change state of tension T at position x with respect to time t.

[0094] The humidity impact value △H(t) is obtained by the following calculation formula:

[0095]

[0096] Where V(t) represents the speed, specifically the speed of the wire when it is stretched. represents the rate of change of humidity S(t) at time τ, S(τ, T env (τ)) represents the humidity influence function, specifically the material temperature T at time τ env Impact on changes in humidity S(t).

[0097] The fault prediction and early warning module includes a fault prediction unit and a risk assessment unit;

[0098] The fault prediction unit calculates the dynamic tension value T dyn (t), tension gradient distribution value The humidity influence value △H(t) is fitted, including the use of multiple regression and machine learning algorithms to obtain the wire drawing machine failure prediction index value P F (t);

[0099] The wire drawing machine fault prediction index value P F (t) is fitted by the following calculation formula:

[0100]

[0101] In the formula, It represents the square integral of the tension gradient distribution value, specifically the cumulative effect of the distribution of tension T along the material length L of the wire;

[0102] The risk assessment unit also calculates the wire drawing machine failure prediction index value P F (t) Conduct a preliminary assessment and obtain a preliminary assessment risk value S F (t) To judge and regulate the operation status of the wire drawing machine, and to perform a secondary evaluation on the regulated wire drawing machine to obtain the secondary failure risk value S F2 (t).

[0103] The initial risk assessment value S F (t) is obtained by the following calculation formula:

[0104]

[0105] In the formula, Indicates the wire drawing machine failure prediction index value P F The derivative of (t) with respect to time τ, specifically the rate of change over time and the value of P F (τ) The changing trend over time τ;

[0106] The secondary failure risk value S F2 (t) is obtained by the following calculation formula:

[0107]

[0108] In the formula, It represents the derivative of the rate of change of tension T with respect to the rate of change of velocity V at time τ, It represents the first-order partial derivative of the tension T with respect to the position x, specifically the rate of change of the tension T along the spatial coordinate x.

[0109] In this embodiment, by obtaining the dynamic tension value T dyn(t), tension gradient distribution value ▽T(x, t) and humidity influence value △H(t), realize the accuracy and predictability of wire drawing machine tension control, and establish wire drawing machine tension dynamic model, thermodynamic tension distribution model and dynamic humidity influence model by combining the inherent characteristic information of the material and the initial state of the equipment, which can carefully reflect the tension change of the wire in the wire drawing process, and can also predict possible equipment failures through fitting analysis, and adjust the operating parameters in real time to avoid equipment damage caused by local tension abnormalities or environmental changes. At the same time, it can focus on the analysis of humidity accumulation effect during long-term operation to ensure that the equipment always maintains stability under different environmental conditions.

[0110] Example 4

[0111] This embodiment is explained in Example 1, please refer to Figure 1 ,Specifically: the intelligent control and optimization module includes a regulation execution unit and a feedback optimization unit;

[0112] The control execution unit is based on the preliminary assessment risk value S F (t) Matching with the preset preliminary control evaluation threshold CZ, obtaining the preliminary control evaluation result, and generating the wire drawing machine control strategy plan according to the result, and executing the wire drawing machine control strategy plan content to perform a fixed period of secondary failure risk value S F2 (t) matching with the preset post-regulation secondary evaluation threshold EZ, obtaining the post-regulation secondary evaluation result, and obtaining the secondary regulation evaluation strategy scheme for executing the wire drawing machine regulation strategy scheme according to the result;

[0113] The feedback optimization unit triggers a fixed period to detect the parameters of the wire drawing machine after the operating status is regulated according to the execution of the wire drawing machine regulation strategy, and specifically executes the generated secondary regulation evaluation strategy content to provide feedback on the operating status information of the wire drawing machine after regulation.

[0114] The preliminary control assessment results are obtained through the following matching method:

[0115] When the risk value S is initially assessed F When (t) is less than the initial control evaluation threshold CZ, the result of the initial control evaluation is qualified, and a strategy plan for not controlling the wire drawing machine is generated;

[0116] When the risk value S is initially assessed F When (t) ≥ the initial control evaluation threshold CZ, the result of the initial control evaluation is unqualified, and a strategy for controlling the wire drawing machine is generated. The control includes controlling the tension T(t) and speed V(t) of the wire drawing machine;

[0117] The secondary evaluation results after regulation are obtained through the following matching method:

[0118] When the secondary failure risk value S F2 When (t) is less than the secondary evaluation threshold EZ after control, the secondary evaluation result after control is obtained to be qualified, and a secondary non-control evaluation strategy plan for the wire drawing machine control is generated;

[0119] When the secondary failure risk value S F2 When (t)≥the secondary evaluation threshold value EZ after control, the secondary evaluation result after control is obtained as unqualified, and a secondary control evaluation strategy plan for the wire drawing machine control is generated, including control and notification. The control includes controlling the tension T(t) and speed V(t) of the wire drawing machine; the notification includes a visualization page prompt notification of abnormal operating status of the wire drawing machine.

[0120] In this embodiment, according to the preliminary assessment risk value S F (t) Matching with the preset preliminary control evaluation threshold CZ, obtaining the preliminary control evaluation result, and generating the wire drawing machine control strategy plan according to the result, and executing the wire drawing machine control strategy plan content to perform a fixed period of secondary failure risk value S F2 (t) Match with the preset post-regulation secondary evaluation threshold EZ, obtain the post-regulation secondary evaluation result, and obtain the secondary regulation evaluation strategy for the wire drawing machine regulation strategy according to the result, so as to judge the effectiveness of the preliminary regulation in real time and optimize the operation status of the equipment. Detect and analyze the operating parameters of the wire drawing machine after regulation in each cycle to ensure that the execution effect of the regulation strategy reaches the expected result. Carry out continuous and closed-loop regulation and feedback according to the evaluation results, accurately adjust the tension and speed of the wire drawing machine, reduce the equipment failure rate, and provide safer and more reliable production guarantee by notifying the system of abnormal conditions in real time, thereby significantly improving the operation efficiency and product quality of the wire drawing machine.

[0121] Example 5

[0122] An intelligent control method for wire tension of wire drawing machine, please refer to Figure 2 , specifically: including the following steps:

[0123] Step 1: The data preprocessing module collects the operating status information and operating area environment information of the wire drawing machine in real time through the sensor network to form the wire drawing machine collection data set X raw (t), and preprocess the wire drawing machine data set, including denoising preprocessing, data normalization preprocessing and outlier detection preprocessing, to form the processed data feature vector set X feat (t);

[0124] Step 2: The multi-parameter analysis module is based on the data feature vector set X feat (t) Establish the wire drawing machine tension dynamic model, thermodynamic tension distribution model and dynamic humidity influence model, and obtain the dynamic tension value T through training and analysisdyn (t), tension gradient distribution value ▽T(x, t) and humidity influence value △H(t);

[0125] Step 3: The fault prediction and warning module calculates the dynamic tension value T dyn (t), tension gradient distribution value ▽T(x, t) and humidity influence value △H(t) are fitted to obtain the wire drawing machine failure prediction index value P F (t), and the wire drawing machine failure prediction index value P F (t) Conduct a preliminary assessment and obtain a preliminary assessment risk value S F (t) To judge and regulate the operation status of the wire drawing machine, and to perform a secondary evaluation on the regulated wire drawing machine to obtain the secondary failure risk value S F2 (t);

[0126] Step 4: The intelligent control and optimization module evaluates the risk value S based on the preliminary assessment. F (t) and the secondary failure risk value S F2 (t) Regulate the wire drawing machine and provide feedback on the operating status of the adjusted wire drawing machine.

[0127] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent control system for wire tension of a wire drawing machine, characterized in that: It includes data preprocessing module, multi-parameter analysis module, fault prediction and early warning module and intelligent control and optimization module; The data preprocessing module collects the running status information and running area environment information of the wire drawing machine in real time through the sensor network to form the wire drawing machine collection data set X raw (t), and preprocess the wire drawing machine data set, including denoising preprocessing, data normalization preprocessing and outlier detection preprocessing, to form the processed data feature vector set X feat (t); The multi-parameter analysis module is based on the data feature vector set X feat (t) Establish the wire drawing machine tension dynamic model, thermodynamic tension distribution model and dynamic humidity influence model, and obtain the dynamic tension value T through training and analysis dyn (t), tension gradient distribution value ▽T(x, t) and humidity influence value △H(t); The fault prediction and early warning module calculates the dynamic tension value T dyn (t), tension gradient distribution value ▽T(x, t) and humidity influence value △H(t) are fitted to obtain the wire drawing machine failure prediction index value P F (t), and the wire drawing machine failure prediction index value P F (t) Conduct a preliminary assessment and obtain a preliminary assessment risk value S F (t) To judge and regulate the operation status of the wire drawing machine, and to perform a secondary evaluation on the regulated wire drawing machine to obtain the secondary failure risk value S F2 (t); The intelligent control and optimization module is based on the preliminary assessment of the risk value S F (t) and the secondary failure risk value S F2 (t) Regulate the wire drawing machine and provide feedback on the operating status of the adjusted wire drawing machine.

2. The intelligent control system for wire tension of a wire drawing machine according to claim 1, characterized in that: The data preprocessing module includes a data acquisition unit and a data processing unit; The data acquisition unit is connected to the Internet network through the Internet of Things sensor to collect the operating status information of the wire drawing machine and the operating area environment information in real time, including the tension applied by the wire drawing machine on the wire, the wire moving speed, the vibration frequency of the equipment, the temperature in the operating environment and the humidity in the operating environment, forming the wire drawing machine collection data set X raw (t), including the output material length L(t), tension T(t), speed V(t), vibration frequency F v (t), temperature T env and humidity S(t); Among them, IoT sensors include tension sensors, speed sensors, vibration sensors, temperature sensors and humidity sensors.

3. The intelligent control system for wire tension of a wire drawing machine according to claim 2, characterized in that: The data processing unit collects data set X from the wire drawing machine raw (t) Perform preprocessing, including denoising preprocessing, data normalization preprocessing and outlier detection preprocessing, to form the processed data feature vector set X feat (t); Among them, the denoising preprocessing includes using wavelet transform and Kalman filter to collect data set X of the wire drawing machine raw (t) Perform denoising to obtain the denoised data set X after denoising at time t denoise (t); Data normalization preprocessing includes using principal component analysis PCA to denoise the denoised dataset X after time t denoise (t) Perform normalization processing to obtain the normalized data set Xnorm(t) normalized at time t; The outlier detection preprocessing includes using the isolation forest algorithm to perform outlier detection on the normalized data set Xnorm(t) normalized at time t, and propose extremely abnormal data points to form the data feature vector set X at time t. feat (t).

4. The intelligent control system for wire tension of a wire drawing machine according to claim 1, characterized in that: The multi-parameter analysis module includes a modeling unit; The modeling unit is based on the data feature vector set X feat (t) combining the inherent characteristic information of the material and the initial state information of the wire drawing machine as the input data set to establish the wire drawing machine tension dynamic model, thermodynamic tension distribution model and dynamic humidity influence model; Among them, the inherent characteristic information of the material includes elastic modulus E, stress relaxation rate SR(t), thermal conductivity k(T), density p and specific heat capacity C p ; The initial state information of the wire drawing machine equipment includes the initial tension T0 of the equipment and the initial length L0 of the material; Through the training and analysis of the wire drawing machine tension dynamic model, the tension of the material during the wire drawing process changes with time, and the dynamic tension value T is obtained. dyn (t); Through the training and analysis of the thermodynamic tension distribution model, the changing trend of tension at different positions is predicted and the tension gradient distribution value is obtained. By accumulating the long-term impact of humidity changes on equipment operation, the humidity impact value △H(t) is obtained as the basis for formulating corresponding maintenance and adjustment plans.

5. The intelligent control system for wire tension of a wire drawing machine according to claim 4, characterized in that: The dynamic tension value T dyn (t) is obtained by the following calculation formula: Where, E represents the elastic modulus, specifically the stiffness of the material, L(t) represents the length of the material at time t, specifically the length of the material processed by the wire drawing machine, L0 represents the initial length of the material, T0 represents the initial tension, SR(t) represents the stress relaxation rate, and K F Indicates the vibration frequency F v The preset influence weight value of (t), represents the integral term from time 0 to time t, and τ represents the time τ from time 0 to time t; The tension gradient distribution value Obtained through the following calculation formula: In the formula, k(T) represents the thermal conductivity, which specifically indicates the thermal conductivity of the material at temperature T env Thermal conductivity, T env represents the temperature of the material, p represents the density of the material, C p represents the specific heat capacity, represents the first-order partial derivative of the tension T with respect to the position x, specifically the rate of change of the tension T along the spatial coordinate x, It represents the second-order partial derivative of tension T with respect to time t, and represents the acceleration change state of tension T at position x with respect to time t. The humidity impact value △H(t) is obtained by the following calculation formula: Where V(t) represents the speed, specifically the speed of the wire when it is stretched. represents the rate of change of humidity S(t) at time τ, S(τ, T env (τ)) represents the humidity influence function, specifically the material temperature T at time τ env Impact on changes in humidity S(t).

6. The intelligent control system for wire tension of a wire drawing machine according to claim 5, characterized in that: The fault prediction and early warning module includes a fault prediction unit and a risk assessment unit; The fault prediction unit calculates the dynamic tension value T dyn (t), tension gradient distribution value ▽T(x, t) and humidity influence value △H(t) are fitted, including using multiple regression and machine learning algorithms to obtain the wire drawing machine failure prediction index value P F (t); The wire drawing machine fault prediction index value P F (t) is fitted by the following calculation formula: In the formula, It represents the square integral of the tension gradient distribution value, specifically the cumulative effect of the distribution of tension T along the material length L of the wire; The risk assessment unit also calculates the wire drawing machine failure prediction index value P F (t) Conduct a preliminary assessment and obtain a preliminary assessment risk value S F (t) To judge and regulate the operation status of the wire drawing machine, and to perform a secondary evaluation on the regulated wire drawing machine to obtain the secondary failure risk value S F2 (t).

7. The intelligent control system for wire tension of a wire drawing machine according to claim 6, characterized in that: The initial risk assessment value S F (t) is obtained by the following calculation formula: In the formula, Indicates the wire drawing machine failure prediction index value P F The derivative of (t) with respect to time τ, specifically the rate of change over time and the value of P F (τ) The changing trend over time τ; The secondary failure risk value S F2 (t) is obtained by the following calculation formula: In the formula, It represents the derivative of the rate of change of tension T with respect to the rate of change of velocity V at time τ, It represents the first-order partial derivative of the tension T with respect to the position x, specifically the rate of change of the tension T along the spatial coordinate x.

8. The intelligent control system for wire tension of a wire drawing machine according to claim 1, characterized in that: The intelligent control and optimization module includes a control execution unit and a feedback optimization unit; The control execution unit is based on the preliminary assessment risk value S F (t) Matching with the preset preliminary control evaluation threshold CZ, obtaining the preliminary control evaluation result, and generating the wire drawing machine control strategy plan according to the result, and executing the wire drawing machine control strategy plan content to perform a fixed period of secondary failure risk value S F2 (t) matching with the preset post-regulation secondary evaluation threshold EZ, obtaining the post-regulation secondary evaluation result, and obtaining the secondary regulation evaluation strategy scheme for executing the wire drawing machine regulation strategy scheme according to the result; The feedback optimization unit triggers a fixed period to detect the parameters of the wire drawing machine after the operating status is regulated according to the execution of the wire drawing machine regulation strategy, and specifically executes the generated secondary regulation evaluation strategy content to provide feedback on the operating status information of the wire drawing machine after regulation.

9. The intelligent control system for wire tension of a wire drawing machine according to claim 8, characterized in that: The preliminary control evaluation results are obtained by the following matching method: When the risk value S is initially assessed F When (t) is less than the initial control evaluation threshold CZ, the result of the initial control evaluation is qualified, and a strategy plan for not controlling the wire drawing machine is generated; When the risk value S is initially assessed F When (t) ≥ the initial control evaluation threshold CZ, the result of the initial control evaluation is unqualified, and a strategy plan for wire drawing machine control is generated, and the control includes controlling the tension T(t) and speed V(t) of the wire drawing machine; The secondary evaluation results after regulation are obtained through the following matching method: When the secondary failure risk value S F2 When (t) < the secondary evaluation threshold value EZ after control, the secondary evaluation result after control is determined to be qualified, and a secondary non-control evaluation strategy scheme for the wire drawing machine control is generated; When the secondary failure risk value S F2 When (t)≥the secondary evaluation threshold EZ after control, it is determined that the secondary evaluation result after control is unqualified, and a secondary control evaluation strategy scheme for the wire drawing machine control is generated. The secondary control evaluation strategy scheme includes control and notification. The control includes controlling the tension T(t) and speed V(t) of the wire drawing machine. The notification includes a visualization page prompt notification of abnormal operating status of the wire drawing machine.

10. An intelligent control method for wire tension of a wire drawing machine, used for applying an intelligent control system for wire tension of a wire drawing machine as described in any one of claims 1 to 9, characterized in that: The following steps are involved: Step 1: The data preprocessing module collects the operating status information and operating area environment information of the wire drawing machine in real time through the sensor network to form the wire drawing machine collection data set X raw (t), and preprocess the wire drawing machine data set, including denoising preprocessing, data normalization preprocessing and outlier detection preprocessing, to form the processed data feature vector set X feat (t); Step 2: The multi-parameter analysis module is based on the data feature vector set X feat (t) Establish the wire drawing machine tension dynamic model, thermodynamic tension distribution model and dynamic humidity influence model, and obtain the dynamic tension value T through training and analysis dyn (t), tension gradient distribution value ▽T(x, t) and humidity influence value △H(t); Step 3: The fault prediction and warning module calculates the dynamic tension value T dyn (t), tension gradient distribution value ▽T(x, t) and humidity influence value △H(t) are fitted to obtain the wire drawing machine failure prediction index value P F (t), and the wire drawing machine failure prediction index value P F (t) Conduct a preliminary assessment and obtain a preliminary assessment risk value S F (t) To judge and regulate the operation status of the wire drawing machine, and to perform a secondary evaluation on the regulated wire drawing machine to obtain the secondary failure risk value S F2 (t); Step 4: The intelligent control and optimization module evaluates the risk value S based on the preliminary assessment. F (t) and the secondary failure risk value S F2 (t) Regulate the wire drawing machine and provide feedback on the operating status of the adjusted wire drawing machine.

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