A monitoring and warning method and system for rolling stability

Abstract

The present invention relates to the field of rolling monitoring, and discloses a monitoring and early warning method for rolling stability. Each finishing rolling area of a finishing mill is disassembled, and each area is disassembled into different individual equipment. Data of each individual equipment during the rolling process are collected in real time. First, the data of each individual equipment are analyzed, and then the data of all individual equipment are combined to analyze the health of the finishing mill. At the same time, product data rolled out are collected in real time, and the collected product data are analyzed to determine whether the product meets the requirements. If the requirements are met, the product qualification rate is calculated. Finally, the rolling stability is calculated according to the health of the finishing mill and the product qualification rate.

Description

Technical Field

[0001] The present invention relates to the field of rolling monitoring, and in particular to a method and system for monitoring and warning of rolling stability. Background Art

[0002] Hot rolling focuses on the production of high-strength steel, grain-oriented silicon steel, non-grain-oriented silicon steel, etc. Due to the narrow processing window and high production difficulty in the production process, production interruption accidents often occur. There is an urgent need to develop new technologies to improve the intelligence level of production management and keep the production in the best stable state. The difficulty is that there are many factors affecting production stability. The post-treatment and experience-based working methods can no longer meet the requirements of advanced production management. It is necessary to explore a highly efficient intelligent production method to get rid of the old way of information separation, knowledge fragmentation, and reliance on expert experience. Utilizing industrial big data, integrating production knowledge, and establishing a rolling process warning system provide a new way for assisting production and improving work efficiency. Developing production anomaly diagnosis and process warning technologies is also the key technology research and development direction recommended by the iron and steel industry at present.

[0003] The control ability of the rolling stability state in the finishing area, the stable control of the finishing quality, and the prevention of equipment operation accidents not only rely on effective equipment inspection and maintenance, TPM management, and operation skill improvement, but also more rely on the collation, mining, and analysis of regional operation and maintenance data; the solution of many problems such as rolling stability and production quality has also proved the "golden" value of these data: the comprehensive diagnosis and analysis of the functional accuracy and reliability of various equipment in the production process, the rolling stability of the finishing mill, the rationality of process settings, and the accuracy of operation control are also increasingly valued by everyone; at present, the detection and diagnosis technologies of many related enterprises at home and abroad are only limited to the corresponding operation state analysis of a specific equipment (or subsystem) of the rolling mill, and there is no relevant system and function for the overall and comprehensive diagnosis of the rolling mill.

[0004] Moreover, most of the existing systems only issue warnings when unqualified products and process data exceed the critical value, and do not consider that qualified products and process data may be close to the critical value, but do not issue warnings because they exceed the critical value. However, when the products and process data are in this state, it indicates that the rolling stability is also low, and continuing processing may cause losses. Summary of the Invention

[0005] The purpose of the present invention is to provide a method and system for monitoring and warning of rolling stability to solve the above-mentioned problems.

[0006] The purpose of the present invention can be achieved by the following technical solutions:

[0007] A method for monitoring and warning of rolling stability, the method comprising the following steps:

[0008] Step S1: Disassemble the finishing mill into different functional areas, and each functional area is disassembled into different individual equipment;

[0009] Step S2: Real-time collect the data of individual equipment in each functional area during the rolling process, analyze the collected equipment data to determine whether the finishing mill is healthy. If it is healthy, calculate the health degree of the finishing mill;

[0010] Step S3: Real-time collect the product data of the rolled products, analyze the collected product data to determine whether the products meet the requirements. If they meet the requirements, calculate the product qualification rate;

[0011] Step S4: Calculate the rolling stability based on the health degree of the finishing mill and the product qualification rate.

[0012] Through the above technical solution, the present invention disassembles each finishing area of the finishing mill, and each area is disassembled into different individual equipment. Real-time collect the data of each individual equipment during the rolling process, first analyze the data of each individual equipment, and then combine the data of all individual equipment to analyze the health degree of the finishing mill. At the same time, real-time collect the product data of the rolled products, analyze the collected product data to determine whether the products meet the requirements. If they meet the requirements, calculate the product qualification rate. Finally, calculate the rolling stability based on the health degree of the finishing mill and the product qualification rate.

[0013] As a further description of the solution of the present invention, the specific process of step S2 includes:

[0014] Step S21: Sequentially number the individual equipment in each functional area as: 1, 2, 3…n;

[0015] Step S22: Sequentially obtain the data of the pressure change with time during the finishing process of each individual equipment, and fit it into a curve f n (t);

[0016] Step S23: Calculate the health degree HC of the finishing mill through the formula:

[0017]

[0018] In the formula, A n is the actual score of the individual equipment in each functional area, A 0n is the full score of the individual equipment in each functional area, and k n is the weight coefficient;

[0019] Step S24: Compare the actual score A n of the individual equipment in each functional area with the corresponding threshold respectively:

[0020] If there are parameter items that do not meet the threshold conditions, adjust the individual equipment in this functional area;

[0021] If all meet the corresponding threshold conditions, then compare the health degree HC with the preset threshold HC th as follows:

[0022] If HC ≥ HC th , it is determined that the device has potential processing risks;

[0023] If HC < HC th , it is determined that the device has no potential processing risks.

[0024] Through the above technical solution, the single devices in each functional area of the present invention are numbered in sequence as: 1, 2, 3... n. First, determine whether each single device has parameter items that do not meet the threshold conditions. If so, adjust the device separately. If not, combine the data of all single devices and calculate the health degree of the device according to the formula Then compare the health degree HC with the preset threshold HC th as follows: If HC ≥ HC th , it is determined that the device has potential processing risks; if HC < HC th , it is determined that the device has no potential processing risks.

[0025] As a further description of the solution of the present invention, the calculation process of the actual score A n of the single device in each functional area includes:

[0026] Calculate the actual score index σ n of the single device in each functional area:

[0027]

[0028] In the formula, t1 to t2 are the time periods of the finishing rolling process of the finishing mill, f n (t) is the curve of the pressure changing with time during the finishing rolling process of each single device, f n0 (t) is the curve of the standard pressure changing with time during the finishing rolling process of each single device, Δt is the duration of the finishing rolling process of the finishing mill, and Δt = t2 - t1;

[0029] Substitute σ n into the preset threshold interval [σ 1n , σ 2n to calculate the actual score A n of the single device in each functional area:

[0030]

[0031] As a further description of the solution of the present invention, when σ n > σ2n When the actual score A of the individual device in each functional area is n 0 points, it indicates that the individual device in the current area has a fault; when σ 1n ≤σ n ≤σ 2n When, the actual score A of the individual device in each functional area is n A / 2 points, indicating that there is a potential possibility of a fault in the individual device in the current area; when σ 0n <σ n <σ 1n When, the actual score A of the individual device in each functional area is n A 0n points, indicating that the individual device in the current area is normal.

[0032] Through the above technical solution, according to the formula Calculate the actual score index σ of the individual device in each functional area n , and then calculate the actual score A of the individual device in each functional area according to the actual score index σ of the individual device in each functional area n , and judge the current state of the individual device according to the actual score A of the individual device. n n n n

[0033] As a further description of the solution of the present invention, the specific process of step S3 includes:

[0034] Step S31, obtain each mechanical property parameter of the product after finish rolling: MP1, MP2...MP m ;

[0035] Step S32, compare each mechanical property parameter with the corresponding mechanical property parameter standard value. If the mechanical property parameter standard value is not reached, it means the product is unqualified. If the mechanical property parameter standard value is reached, the product is qualified and enter step S33;

[0036] Step S33, calculate the product qualification degree Q according to each mechanical property parameter:

[0037]

[0038] In the formula, MP m0 is the standard value of the mth mechanical property parameter, and ρ m is the weight coefficient of the mth mechanical property parameter.

[0039] As a further description of the solution of the present invention, compare the product qualification degree Q with the preset threshold Q th :

[0040] If Q≥Q th, it is determined that the product has a relatively high qualification rate;

[0041] If Q < Q th , it is determined that the product has a relatively low qualification rate.

[0042] As a further description of the solution of the present invention, the specific process of step S4 includes:

[0043] Calculate the rolling stability S through the formula S = α * e HC + β * Q, where α and β are the parameter elimination coefficients of the health degree HC of the finishing mill and the product qualification rate Q respectively;

[0044] Compare the rolling stability S with the preset threshold S th for comparison:

[0045] If S ≥ S th , it is determined that the rolling stability is relatively high;

[0046] If S < S th , it is determined that the rolling stability is relatively low and an early warning needs to be issued immediately.

[0047] Through the above technical solution, each mechanical property parameter of the product after finishing rolling is collected in real time, and each mechanical property parameter is compared with the corresponding mechanical property parameter standard value. If the mechanical property parameter standard value is not reached, it means that the product is unqualified. If the mechanical property parameter standard value is reached, the product is qualified, and according to the formula calculate the qualification rate, and then calculate the rolling stability according to the formula S = α * e HC + β * Q, compare the rolling stability S with the preset threshold S th for comparison: If S ≥ S th , it is determined that the rolling stability is relatively high. If S < S th , it is determined that the rolling stability is relatively low and an early warning needs to be issued immediately.

[0048] A monitoring and early warning system for rolling stability, the system includes:

[0049] A rolling process monitoring module for collecting monomer equipment data of each functional area during the rolling process in real time;

[0050] A rolled product monitoring module for collecting product data rolled out in real time;

[0051] A data analysis module for analyzing the data collected by the rolling process monitoring module and the rolled product monitoring module;

[0052] An early warning module for issuing corresponding early warnings according to the analysis results of the data analysis module.

[0053] Beneficial effects: 1. In the present invention, the individual devices in each functional area are sequentially numbered as 1, 2, 3... n. First, it is determined whether each individual device has parameter items that do not meet the threshold conditions. If so, the device is adjusted individually. If not, the data of all individual devices are combined and the health degree of the device is calculated according to the formula The health degree HC is compared with the preset threshold HC th as follows: If HC≥HC th , it is determined that the device has potential processing risks; if HC<HC th , it is determined that the device does not have potential processing risks.

[0054] 2. The present invention collects various mechanical property parameters of the product after finish rolling, and compares each mechanical property parameter with the corresponding standard value of the mechanical property parameter. If the mechanical property parameter standard value is not reached, it indicates that the product is unqualified. If the mechanical property parameter standard value is reached, the product is qualified, and the qualification degree is calculated according to the formula Then, according to the formula S = α*e HC +β*Q, the rolling stability is calculated. The rolling stability S is compared with the preset threshold S th as follows: If S≥S th , it is determined that the rolling stability is relatively high; if S<S th , it is determined that the rolling stability is relatively low and an early warning needs to be given immediately.

[0055] 3. The present invention comprehensively analyzes the qualified products and process data, calculates the rolling stability, and determines the state of the current rolling process according to the stability, so as to avoid losses that may be caused by continuing processing when the rolling stability is relatively low.

[0056] Of course, any product implementing the present invention does not necessarily need to achieve all the above-mentioned advantages simultaneously. Description of the drawings

[0057] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for describing the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0058] Figure 1 It is a partial flow diagram of the method for monitoring and early warning of rolling stability provided by the present invention;

[0059] Figure 2 It is a structural diagram of the system for monitoring and early warning of rolling stability provided by the present invention. Detailed implementation manners

[0060] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0061] Please refer to Figure 1 As shown, the present invention is 1. A method for monitoring and warning rolling stability, characterized in that the method includes the following steps:

[0062] Step S1: Disassemble the finishing mill into different functional areas, and each functional area is disassembled into different individual equipment;

[0063] Step S2: Real-time collect the data of individual equipment in each functional area during the rolling process, analyze the collected equipment data, and judge whether the finishing mill is healthy. If it is healthy, calculate the health degree of the finishing mill;

[0064] The specific process of step S2 includes:

[0065] Step S21: Number the individual equipment in each functional area in sequence as: 1, 2, 3... n;

[0066] Step S22: Sequentially obtain the data of the pressure change with time of each individual equipment during the finishing process and fit it into a curve f n (t);

[0067] Step S23: Calculate the health degree HC of the finishing mill through the formula:

[0068]

[0069] In the formula, A n is the actual score of the individual equipment in each functional area, A 0n is the full score of the individual equipment in each functional area, and k n is the weight coefficient;

[0070] Step S24: Compare the actual score A n of the individual equipment in each functional area with the corresponding threshold respectively:

[0071] If there are parameter items that do not meet the threshold conditions, adjust the individual equipment in this functional area;

[0072] If all meet the corresponding threshold conditions, compare the health degree HC with the preset threshold HC th for comparison:

[0073] If HC≥HC th, it is determined that the equipment has potential processing risks;

[0074] If HC < HC th , it is determined that the equipment has no potential processing risks.

[0075] The actual score A of the single equipment in each functional area n The calculation process includes:

[0076] Calculate the actual score index σ of the single equipment in each functional area n :

[0077]

[0078] In the formula, t1 to t2 are the finishing process time periods of the finishing mill, and f n (t) is the curve of pressure changing with time during the finishing process of each single equipment, and f n0 (t) is the curve of standard pressure changing with time during the finishing process of each single equipment, Δt is the duration of the finishing process of the finishing mill, and Δt = t2 - t1;

[0079] Substitute σ n into the preset threshold interval [σ 1n , σ 2n to calculate the actual score A of the single equipment in each functional area n :

[0080]

[0081] It can be seen from the above formula that when σ n > σ 2n , the actual score A of the single equipment in each functional area n is 0 points, indicating that the single equipment in the current area has a fault; when σ 1n ≤σ n ≤σ 2n , the actual score A of the single equipment in each functional area n is A 0n / 2 points, indicating that the single equipment in the current area has a potential possibility of malfunction; when σ n <σ 1n , the actual score A of the single equipment in each functional area n is A 0n points, indicating that the single equipment in the current area is normal.

[0082] Through the above technical solution, the single devices in each functional area in this embodiment are numbered in sequence as: 1, 2, 3... n. First, it is determined whether each single device has a parameter item that does not meet the threshold condition. If so, the device is adjusted separately. If not, the data of all single devices are combined and calculated according to the formula to calculate the health degree of the device. The health degree HC is compared with the preset threshold HC th : If HC ≥ HC th , it is determined that the device has a potential processing risk; if HC < HC th , it is determined that the device does not have a potential processing risk.

[0083] Moreover, in this embodiment, the actual scoring index σ of the single device in each functional area is calculated according to the formula , and then the actual score A of the single device in each functional area is calculated according to the actual scoring index σ n of the single device in each functional area. The current state of the single device is judged according to the actual score A n of the single device. n It should be noted that f n (t) is set in advance according to the material characteristics and processing requirements, and k

[0084] is empirical data and is set selectively according to the rolling process. n0 (t) is set in advance according to the material characteristics and processing requirements, and k n is empirical data and is set selectively according to the rolling process.

[0085] Step S3: Real-time collect the product data rolled out, analyze the collected product data, and judge whether the product meets the requirements. If it meets the requirements, calculate the product qualification rate;

[0086] The specific process of the said step S3 includes:

[0087] Step S31: Obtain each mechanical property parameter of the product after finish rolling: MP1, MP2... MP m ;

[0088] Step S32: Compare each mechanical property parameter with the corresponding standard value of the mechanical property parameter. If the standard value of the mechanical property parameter is not reached, it means that the product is unqualified. If the standard value of the mechanical property parameter is reached, the product is qualified and enters step S33;

[0089] Step S33: Calculate the product qualification rate Q according to each mechanical property parameter:

[0090]

[0091] In the formula, MP m0 is the standard value of the m-th mechanical property parameter, and ρ mis the weight coefficient of the m-th mechanical property parameter.

[0092] Compare the product qualification Q with the preset threshold Q th as follows:

[0093] If Q ≥ Q th , it is determined that the product qualification is high;

[0094] If Q < Q th , it is determined that the product qualification is low.

[0095] Step S4: Calculate the rolling stability according to the health of the finishing mill and the product qualification.

[0096] The specific process of step S4 includes:

[0097] Calculate the rolling stability S through the formula S = α * e HC + β * Q, where α and β are the de-parameterization coefficients of the health HC of the finishing mill and the product qualification Q respectively;

[0098] Compare the rolling stability S with the preset threshold S th as follows:

[0099] If S ≥ S th , it is determined that the rolling stability is high;

[0100] If S < S th , it is determined that the rolling stability is low and an early warning needs to be issued immediately.

[0101] In this embodiment, each mechanical property parameter of the product after finishing is collected in real time, and each mechanical property parameter is compared with the corresponding mechanical property parameter standard value. If the mechanical property parameter standard value is not reached, it means the product is unqualified. If the mechanical property parameter standard value is reached, the product is qualified, and the qualification is calculated according to the formula Then, calculate the rolling stability according to the formula S = α * e HC + β * Q, and compare the rolling stability S with the preset threshold S th as follows: If S ≥ S th , it is determined that the rolling stability is high. If S < S th , it is determined that the rolling stability is low and an early warning needs to be issued immediately.

[0102] It should be noted that the mechanical property MP m is selectively set according to the material characteristics of the processed product and the use of the processed product. Compressive property, tensile property, torsional property, etc. can be selected. ρ m is empirical data and is also selectively set according to the current relevance between mechanical properties and the product.

[0103] A monitoring and early warning system for rolling stability, the system includes:

[0104] A rolling process monitoring module, used to collect the data of each individual device in each functional area during the rolling process in real time;

[0105] A rolled product monitoring module, used to collect the data of the rolled products in real time;

[0106] A data analysis module, used to analyze the data collected by the rolling process monitoring module and the rolled product monitoring module:

[0107] An early warning module, used to issue corresponding early warnings according to the analysis results of the data analysis module.

[0108] The system further includes a device monitoring module, used to monitor the static data of the device, the dynamic data of the device, etc.;

[0109] It also includes a fault monitoring module, used to monitor equipment failures.

[0110] Working principle: In this embodiment, each finishing area of the finishing mill is disassembled, and each area is disassembled into different individual devices. The data of each individual device during the rolling process is collected in real time. First, the data of each individual device is analyzed, and then the data of all individual devices are combined to analyze the health of the finishing mill. At the same time, the data of the rolled products are collected in real time, and the collected product data is analyzed to determine whether the products meet the requirements. If they meet the requirements, the product qualification rate is calculated. Finally, the rolling stability is calculated based on the health of the finishing mill and the product qualification rate.

[0111] The above has described a detailed description of an embodiment of the present invention, but the content described is only a preferred embodiment of the present invention and cannot be considered as limiting the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the application of the present invention should still fall within the scope covered by the patent of the present invention.

Claims

1. A monitoring and early warning method for rolling stability, characterized in that The method includes the following steps: Step S1: Disassemble the finishing mill into different functional areas, and each functional area is disassembled into different individual equipment; Step S2: Real-time collect the data of individual equipment in each functional area during the rolling process, analyze the collected equipment data, judge whether the finishing mill is healthy, if it is healthy, calculate the health degree of the finishing mill; Step S3: Real-time collect the product data of the rolled products, analyze the collected product data, judge whether the products meet the requirements, if they meet the requirements, calculate the product qualification rate; Step S4: Calculate the rolling stability according to the health degree of the finishing mill and the product qualification rate; The specific process of step S2 includes: Step S21: Number the individual equipment in each functional area in sequence as: 1, 2, 3... n; Step S22: Sequentially obtain the data of the pressure variation with time during the finishing rolling process of each individual device, and fit it into a curve f n (t); Step S23: Calculate the health degree HC of the finishing mill through the formula: Where, A n is the actual score of the individual device in each functional area, A 0n is the full score of the individual device in each functional area, and k n is the weight coefficient; Step S24: Compare the actual score A of the single device in each functional area n with the corresponding threshold respectively: If there are parameter items that do not meet the threshold conditions, adjust the individual equipment in this functional area; If all meet the corresponding threshold conditions, the health degree HC is compared with the preset threshold HC th for comparison: If HC ≥ HC th , it is determined that the device has potential processing risks; If HC < HC th , it is determined that the device has no potential processing risks; The actual score A of the individual device in each functional area n The calculation process includes: Calculate the actual scoring index σ of the individual devices in each functional area n : Wherein, t1 to t2 are the time periods of the finishing rolling process of the finishing mill, and f n (t) is the curve of the pressure changing with time during the finishing rolling process of each individual equipment, and f n0 (t) is the curve of the standard pressure changing with time during the finishing rolling process of each individual equipment, Δt is the duration of the finishing rolling process of the finishing mill, and Δt = t2 - t1; Substitute σ n into the preset threshold interval [σ 1n , σ 2n to calculate the actual score A n of the single device in each functional area:

2. The monitoring and early warning method for rolling stability according to claim 1, wherein When σ n > σ 2n , the actual score A of the individual device in each functional area n is 0 points, indicating that the individual device in the current area has failed; when σ 1n ≤ σ n ≤ σ 2n , the actual score A of the individual device in each functional area n is A 0n / 2 points, indicating that there is a potential possibility of failure of the individual device in the current area; when σ n < σ 1n , the actual score A of the individual device in each functional area n is A 0n points, indicating that the individual device in the current area is normal.

3. The monitoring and early warning method for rolling stability according to claim 2, characterized in that, The specific process of step S3 includes: Step S31: Obtain each mechanical property parameter of the product after finish rolling: MP1, MP2... MP m ; Step S32: Compare each mechanical property parameter with the corresponding standard value of the mechanical property parameter. If the mechanical property parameter standard value is not reached, it means the product is unqualified. If the mechanical property parameter standard value is reached, the product is qualified and proceed to step S33; Step S33: Calculate the product qualification rate Q according to each mechanical property parameter; where, MP m0 is the standard value of the m-th mechanical property parameter, and ρ m is the weighting coefficient of the m-th mechanical property parameter.

4. The monitoring and early warning method for rolling stability according to claim 3, characterized in that Compare the product qualification Q with a preset threshold Q th for comparison: If Q ≥ Q th , it is determined that the product has a high degree of qualification; If Q < Q th , it is determined that the qualification rate of the product is low.

5. The monitoring and early warning method for rolling stability according to claim 4, characterized in that, The specific process of step S4 includes: Calculate the rolling stability S through the formula S = α * e HC + β * Q, where α and β are the parameter elimination coefficients of the health condition HC and the product qualification rate Q of the finishing mill respectively; Compare the rolling stability S with a preset threshold S th as follows: If S≥S th , it is determined that the rolling stability is relatively high; If S < S th , it is determined that the rolling stability is low and an immediate warning is required.

6. A monitoring and early warning system adopting the monitoring and early warning method for rolling stability according to any one of claims 1-5, characterized in that, The system includes: A rolling process monitoring module, which is used to collect in real time the data of individual equipment in each functional area during the rolling process; A rolled product monitoring module, which is used to collect in real time the product data of the rolled products; A data analysis module, which is used to analyze the data collected by the rolling process monitoring module and the rolled product monitoring module; An early warning module, which is used to issue corresponding early warnings according to the analysis results of the data analysis module.

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