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Method for predicting thickness of hot-rolled strip steel on basis of improved partial robust M-regression algorithm

A technology of hot-rolled strip steel and regression algorithm, applied in calculation, metal rolling, measuring devices, etc., can solve problems such as time-consuming, accurate analysis model cannot be obtained, etc.

Inactive Publication Date: 2013-11-27
BOHAI UNIV
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  • Claims
  • Application Information

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Problems solved by technology

[0005] In order to solve the problem that the accurate analysis model cannot be obtained or the modeling process is extremely time-consuming in the existing method for predicting the thickness, the present invention provides a hot-rolled strip thickness based on the improved partial robust M regression algorithm method of prediction

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  • Method for predicting thickness of hot-rolled strip steel on basis of improved partial robust M-regression algorithm
  • Method for predicting thickness of hot-rolled strip steel on basis of improved partial robust M-regression algorithm
  • Method for predicting thickness of hot-rolled strip steel on basis of improved partial robust M-regression algorithm

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specific Embodiment approach 1

[0016] Specific implementation mode one, combination figure 1 This specific embodiment is described. A hot-rolled strip thickness prediction method based on an improved partial robust M regression algorithm, which includes the following steps:

[0017] Step 1: Monitor the working data of 7 finishing mills to obtain observed variables (x i ,y i ), and according to the observed variable (x i ,y i ) Define the input data matrix X and the output data matrix Y, and calculate the initial value of the robust weighting factor ω i ;

[0018] The working data of the finishing mill includes the average distance between the work rolls of each finishing mill, the total pressure of each finishing mill, and the crimping force of the work roll of each finishing mill;

[0019] Step 2: To observe the variable (x i ,y i ) Perform weighting processing to obtain prediction data And forecast data Perform partial least squares analysis to obtain a partial least squares model of forecast data And calcul...

specific Embodiment approach 2

[0024] The second embodiment of the present embodiment is different from the first step: monitoring the working data of 7 finishing mills to obtain the observed variables (x i ,y i ), and according to the observed variable (x i ,y i ) Define the input data matrix X and the output data matrix Y, and calculate the initial value of the robust weighting factor ω i The process is:

[0025] Observed variables (x i ,y i ),among them:

[0026] x i Is the i-th row vector of input data X, x i1 ,...,x i7 Respectively is the average distance between the working rolls of each finishing mill, x i8 ,...,x i14 Respectively the total pressure of each finishing mill, x i15 ,...,x i21 Crimping force of each finishing mill work roll; y i Is the thickness of the final exported hot-rolled strip;

[0027] According to input data X and output data Y, calculate the total square loss center of input data X And the total square loss center of the output data Y

[0028] e ‾ ( X ) = X i ...

specific Embodiment approach 3

[0049] Specific embodiment three, this specific embodiment is different from specific embodiment one or two in the second step: the observation variable (x i ,y i ) Perform weighting processing to obtain prediction data And forecast data Perform partial least squares analysis to obtain a partial least squares model of forecast data Partial Least Squares Regression Model And the process of regression coefficient B is:

[0050] Do not multiply each row of the input data matrix X and the output data matrix Y by Obtain weighted observation data ( ω i x i , ω i y i ) ;

[0051] Perform partial least squares analysis on the weighted observation data to obtain the weighted least squares model:

[0052] X = TP T + X ~

[0053] Y = TQ + Y ~

[0054] Among them, T is the score matrix; P is the load matrix; Is the residual of X, and Q is the regression coefficient of the score matrix T Is the residual of Y;

[...

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Abstract

The invention discloses a method for predicting the thickness of hot-rolled strip steel on the basis of an improved partial robust M-regression algorithm, and relates to a method for predicting the thickness of hot-rolled strip steel. By the aid of the method, the problem of incapability of acquiring an accurate analytical model or extremely high time consumption of a modeling procedure of an existing method for predicting the thickness of hot-rolled strip steel is solved. The method includes monitoring operational data of seven finishing mills to acquire observational variables (x, y), defining an input data matrix X and an output data matrix Y and computing initial values omega of robust weighting factors; weighting the observational variables (x, y) to acquire predicted data, performing partial least-square analysis on the predicted data to acquire a partial least-square model of the predicted data and continuously computing to obtain a partial least-square regression model and regression coefficients B; judging whether estimation errors of a k regression coefficient B and a (k-1) regression coefficient B are smaller than a set threshold value or not, acquiring a certain regression coefficient B and determining a partial least-square regression model which is a prediction result for the thickness of the hot-rolled strip steel. The method can be widely applied to predicting the thickness of the hot-rolled strip steel.

Description

Technical field [0001] The invention relates to a method for predicting the thickness of hot rolled strip steel. Background technique [0002] In many industrial fields, such as chemical production, papermaking and oil refining, the analysis of the regression relationship between measurable data and production quality variables is helpful for the control and monitoring of the production process. A suitable regression model can be used as a soft measurement tool to assist process engineers in predicting the final production quality, which is of great significance for the control, optimization and error diagnosis of the production process. [0003] The main key performance index (KPI) of the hot strip rolling mill is the thickness, width and shape of the strip, among which the thickness is the decisive factor for the quality of the strip and the yield of steel production. Under the huge rolling pressure of the rolling mill, it is impossible to obtain the desired strip thickness by s...

Claims

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Application Information

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IPC IPC(8): B21B37/16G06F19/00G01B21/08
Inventor 尹珅潘瑞王光卫作龙高会军
Owner BOHAI UNIV
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