A continuous rolling process comprehensive monitoring method considering rolling characteristics

Abstract

The rolling process comprehensive monitoring method considering rolling characteristics provided by the application comprises the following steps: collecting rolling procedure data, equipment data and rolling process parameters in a rolling site; constructing a rolling process state space equation for simulating a rolling process according to a rolling theory equation, the rolling procedure data and the equipment data; calculating Hurst indexes of the rolling process parameters; calculating LQG indexes of the rolling process parameters; calculating CUSUM offset indexes of the rolling process parameters; calculating comprehensive evaluation indexes of the rolling process parameters; calculating importance coefficients of the rolling process parameters; and calculating a comprehensive evaluation index of the continuous rolling process to evaluate the continuous rolling process. The rolling process comprehensive monitoring method considering rolling characteristics provided by the application not only realizes comprehensive and overall evaluation of the continuous rolling process, but also introduces rolling characteristic analysis, greatly improves the fidelity of the continuous rolling process evaluation index, and can be widely used in rolling production.

Description

Technical Field

[0001] This invention belongs to the field of rolling control technology and relates to a comprehensive monitoring method for continuous rolling processes that takes into account rolling characteristics. Background Technology

[0002] With the increasing demands on steel products from various industrial sectors, higher requirements are being placed on the control level of the continuous rolling process. The continuous rolling process involves numerous parameters and is accompanied by various unknown disturbances. Due to a lack of monitoring methods and system maintenance, the control accuracy of the rolling process declines, failing to meet the set control targets. Implementing comprehensive monitoring of the rolling process to promptly identify and control the causes of performance issues is crucial for ensuring the control accuracy of the rolling process.

[0003] Currently, with the development of computer technology and information technology, performance evaluation of rolling process monitoring and control has been applied. Chinese patent "CN117806263A" proposes a rolling process thickness control performance evaluation method based on multi-factor covariance ratio, which simultaneously monitors multiple rolling process parameters during the rolling process to achieve real-time evaluation of the performance of each pass and the overall rolling process. Chinese patent "CN117983668B" proposes a hot rolling process thickness looper tension optimization control method based on performance evaluation, using the Hurst exponent to evaluate the performance of the thickness-looper-tension control system in real time, and employing the crayfish optimization algorithm to optimize the control parameters of the control system. Chinese patent "CN116433085A" proposes a rolling process control system performance evaluation method, defining a Hurst exponent based on strip production data. Performance indicators are used to evaluate the performance of the current rolling process control system. Chinese patent "CN117519067B" proposes a method for evaluating the control performance of multi-stand continuous rolling processes. It uses a detrended fluctuation analysis algorithm to solve for the corresponding s-values ​​of the multivariate time series of multi-stand continuous rolling processes, and then calculates the Hurst exponent of the control system. The performance status of the controller is evaluated using the multi-stand control performance indicators. Chinese patent "CN116274421A" proposes a remote monitoring system and method for key processes in rolling based on sound sensors. By collecting audio signals from important parts of the rolling process, it solves the problem that operators cannot hear the on-site sounds after remote centralized control, and realizes the monitoring and analysis of the on-site situation.

[0004] Currently, the aforementioned studies have achieved some results in the evaluation of rolling process monitoring and control performance, but certain shortcomings remain. On the one hand, continuous rolling processes involve numerous parameters and complex operating conditions; the studies using only one evaluation index make it difficult to comprehensively evaluate the rolling process. On the other hand, the studies either did not consider evaluating the continuous rolling process itself, or merely constructed evaluation indicators through linear weighting, failing to consider the actual rolling characteristics of the continuous rolling process and thus unable to effectively evaluate it. Summary of the Invention

[0005] To address the aforementioned technical problems, the present invention aims to provide a comprehensive monitoring method for continuous rolling processes that considers rolling characteristics. By introducing rolling characteristics and a comprehensive evaluation system, a comprehensive evaluation of the continuous rolling process is achieved, filling the gap in the existing evaluation system regarding evaluation benchmark indicators for monitoring the continuous rolling process.

[0006] This invention provides a comprehensive monitoring method for continuous rolling processes that considers rolling characteristics, comprising:

[0007] Step 1: Collect rolling procedure data, equipment data, and rolling process parameters from the rolling site;

[0008] Step 2: Based on the rolling theory equations, on-site rolling procedure data, and equipment data, construct the state-space equations of the rolling process to simulate the rolling process;

[0009] Step 3: Calculate the Hurst exponent for each rolling process parameter;

[0010] Step 4: Calculate the LQG index for each rolling process parameter;

[0011] Step 5: Calculate the CUSUM offset index for each rolling process parameter;

[0012] Step 6: Calculate the comprehensive evaluation index of each rolling process parameter;

[0013] Step 7: Calculate the importance coefficient of each rolling process parameter;

[0014] Step 8: Calculate the comprehensive evaluation index of the continuous rolling process and evaluate the continuous rolling process according to the health evaluation standard of the continuous rolling process.

[0015] Furthermore, step 2 specifically includes:

[0016] Step 2.1: Establish the state-space equations for the rolling process:

[0017]

[0018] In the formula, A, B, C, D, K are the state-space matrices of the rolling process, and x is the state vector of the rolling process. Let x represent the first derivative, u be the rolling process control vector, e0 be the noise vector, y be the output vector, and t be time;

[0019] Step 2.2: Based on rolling theory, establish the rolling force increment equation, forward slip increment equation, exit thickness increment equation, and exit tension increment equation for each stand;

[0020] Step 2.3: Calculate the partial differential coefficients of each incremental equation based on the rolling schedule data and equipment data;

[0021] Step 2.4: Calculate the state space matrix A, B, C, D, K of the rolling process based on the partial differential coefficients of each incremental equation, and substitute A, B, C, D, K into the state space equation of Step 2.1.

[0022] Furthermore, step 3 specifically includes:

[0023] Step 3.1: Determine the autocorrelation sequence of each rolling process parameter:

[0024] Step 3.2: Divide the autocorrelation sequence into M windows of length l, and calculate the root mean square oscillation f(l) of the autocorrelation sequence when the window length is l:

[0025]

[0026] In the formula, Y m (i) is the autocorrelation sequence of the m-th window. Let a be the least squares fitted curve within the m-th window. m and b m , , are the slope and intercept of the least squares fitted curve, respectively, and i is the i-th data point in the m-th window;

[0027] Step 3.3: Calculate the logarithm log l of multiple sets of l and the logarithm log f(l) of the root mean square fluctuation corresponding to l;

[0028] Step 3.4: Fit the slope ρ of multiple sets of log l and log f(l) using the first-order least squares method;

[0029] Step 3.5: Calculate the Hurst exponent for each rolling process parameter:

[0030]

[0031] Among them, F Hurst The Hurst exponent represents the parameters of the rolling process.

[0032] Furthermore, step 4 specifically involves:

[0033] Step 4.1: Define the objective function of LQG:

[0034]

[0035] Among them, u t Let y be the control vector at time t. t Let E be the output vector at time t, E be the expectation, and λ be the weighting coefficient controlling the input variance.

[0036] Step 4.2: Based on the state-space equations of the rolling process, define the output matrix of the rolling process parameters:

[0037]

[0038] Rewrite the output matrix in subspace matrix equation form:

[0039]

[0040] Among them, y 0|N-1 The output matrix of rolling process parameters, u 0|N-1 This is the input matrix for the rolling process parameters. The input partial differential coefficient matrix, Let be the partial differential coefficient matrix of the noise;

[0041] Substituting the output matrix of the rolling process parameters into the LQG objective function, we obtain:

[0042]

[0043] Step 4.3: Obtain the rolling process parameter output matrix under the optimal control rate. The steps are as follows:

[0044] Make the objective function J with respect to u 0|N-1 Find the partial derivative:

[0045]

[0046] The control law that minimizes the objective function is:

[0047]

[0048] This leads to the output matrix of rolling process parameters under optimal control:

[0049]

[0050] Where I is the identity matrix and λ is the weighting coefficient for the control input variance;

[0051] Step 4.4: Define the coefficient matrix γ:

[0052]

[0053] Step 4.5: Find the optimal output sequence:

[0054]

[0055] Among them, e t-j The noise at time tj;

[0056] Step 4.6: Calculate the covariance of the optimal output sequence:

[0057]

[0058] Among them, Cov[e t-j [Noise e] t-j covariance, For γ j The transpose of , where N is the number of sampling points;

[0059] Step 4.7: Obtain the LQG performance baseline curve, the steps are as follows:

[0060] Calculate LQG performance benchmark:

[0061]

[0062] By taking values ​​of λ from 0 to ∞, the optimal input-output variance of the rolling process under the LQG benchmark is obtained, and then the LQG benchmark performance curve of the rolling process is obtained.

[0063] Step 4.8: Calculate the LQG exponent F using the LQG performance benchmark curve. LQG :

[0064]

[0065] Among them, V y This represents the actual variance of the rolling process parameters at time t. Let be the expected variance of the LQG performance benchmark corresponding to the rolling process parameters at time t.

[0066] Furthermore, step 5 specifically includes:

[0067] Step 5.1: Calculate the standard deviation σ of the rolling process parameters;

[0068] Step 5.2: Calculate the mean values ​​of the rolling process parameters.

[0069] Step 5.3: Calculate the CUSUM offset index F CUSUM :

[0070]

[0071] Among them, y n This represents the actual value of the nth rolling process parameter. This represents the average value of the rolling process parameters.

[0072] Furthermore, in step 6, the comprehensive evaluation index of each rolling process parameter is calculated according to the following formula:

[0073]

[0074] Among them, F k The comprehensive evaluation index of the k-th rolling process parameter The Hurst exponent is the parameter of the k-th rolling process. The LQG exponent for the k-th rolling process parameter. CUSUM offset index is the CUSUM of the k-th rolling process parameter.

[0075] Furthermore, step 7 specifically includes:

[0076] Step 7.1: Based on the constructed state-space equation of the rolling process, apply 5% noise to each control variable in the rolling process control vector in sequence, and calculate the change ΔW of the rolling process parameter caused by the noise of the p-th control variable. pk ;

[0077] Step 7.2: Calculate the relative change of the k-th rolling process parameter.

[0078]

[0079] in, This is the set value for the k-th rolling process parameter;

[0080] Step 7.3: Calculate the importance coefficient of the k-th rolling process parameter.

[0081]

[0082] Where P represents the number of control mechanisms.

[0083] Furthermore, in step 8, the comprehensive evaluation index of the continuous rolling process is calculated according to the following formula:

[0084] Step 8: Construct a comprehensive evaluation index for the continuous rolling process and evaluate the continuous rolling process, specifically as follows:

[0085] Step 8.1: Calculate the comprehensive evaluation index of the continuous rolling process:

[0086]

[0087] Where K represents the number of rolling process parameters.

[0088] Step 8.2: Establish a health evaluation standard for the continuous rolling process based on the comprehensive evaluation indicators:

[0089] When the comprehensive evaluation index is in the range of [0.9,1], the continuous rolling process is operating very well; when the comprehensive evaluation index is in the range of [0.7,0.9), the continuous rolling process deteriorates, but maintenance is not required; when the comprehensive evaluation index is in the range of [0,0.7), the continuous rolling process malfunctions and maintenance measures must be taken to maintain the continuous rolling process.

[0090] This invention discloses a comprehensive monitoring method for continuous rolling processes considering rolling characteristics. First, the Hurst index, LQG index, and CUSUM offset index for each rolling process parameter are calculated. Then, the average of these three indices is used to calculate the comprehensive evaluation index for each process parameter. Next, the importance coefficient of each process parameter is calculated through rolling characteristic analysis. Finally, the comprehensive evaluation index and importance coefficients of each process parameter are weighted and summed to obtain the comprehensive evaluation index for the continuous rolling process. This invention's comprehensive monitoring method for continuous rolling processes considering rolling characteristics not only achieves a comprehensive evaluation of the continuous rolling process but also introduces rolling characteristic analysis, significantly improving the fidelity of the evaluation index and making it widely applicable to rolling production. Attached Figure Description

[0091] Figure 1 This is a flowchart of a comprehensive monitoring method for continuous rolling processes that takes into account rolling characteristics, according to the present invention.

[0092] Figure 2 This refers to the exit thickness and back tension process data of each stand in the cold continuous rolling process collected during the implementation of this invention;

[0093] Figure 3 This is a schematic diagram of the LQG index calculation process of the present invention. Detailed Implementation

[0094] The specific embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and examples.

[0095] To verify the effectiveness of the comprehensive monitoring method for continuous rolling processes that takes into account rolling characteristics, a comprehensive evaluation of the thickness-tension control system of a cold rolling production line is conducted.

[0096] like Figure 1 As shown, the present invention provides a comprehensive monitoring method for continuous rolling processes considering rolling characteristics, comprising:

[0097] Step 1: Collect rolling procedure data, equipment data, and rolling process parameters from the rolling site.

[0098] The on-site rolling process data and equipment data include: work roll radius, mill stiffness, friction coefficient, stand spacing, inlet thickness setting, outlet thickness setting, rolling force setting, front tension setting, back tension setting, initial roll gap setting, roll speed setting, strip inlet speed setting, and inertia time constant setting. See Table 1.

[0099] The rolling process parameters include the actual value of the back tension and the actual value of the exit thickness. For example... Figure 2 As shown.

[0100] Table 1 Rolling process data and equipment data

[0101]

[0102] Step 2: Based on the rolling theory equations, on-site rolling procedure data, and equipment data, construct the state-space equations for the rolling process to simulate the rolling process, specifically:

[0103] Step 2.1: Establish the state-space equations for the rolling process:

[0104]

[0105] In the formula, A, B, C, D, K are the state-space matrices of the rolling process, and x is the state vector of the rolling process. Let x represent the first derivative of x, u be the rolling process control vector, e0 be the noise vector, y be the output vector, and t be the time.

[0106] Step 2.2: Based on rolling theory, establish the rolling force increment equation, forward slip increment equation, exit thickness increment equation, and forward tension increment equation for each stand.

[0107] The equation for the rolling force increment is:

[0108] ΔF=α1Δh in +α2Δh out +α3Δσ in +α4Δσ out

[0109] In the formula, Here, F is the partial differential coefficient of the rolling force with respect to the entry thickness, and h is the rolling force. in For the inlet thickness, Δh in For the increase in inlet thickness, h is the partial differential coefficient of rolling force with respect to exit thickness. out For the export thickness, Δh out To increase the thickness of exports, σ is the partial differential coefficient of rolling force with respect to back tension.in For the back tension, Δσ in For the post-tension increment, σ is the partial differential coefficient of the rolling force with respect to the front tension. out For the initial tension, Δσ out This represents the pre-tension increment.

[0110] Forward sliding incremental equation:

[0111] Δf=γ1Δh in +γ2Δh out +γ3Δσ in +γ4Δσ out

[0112] In the formula, Let f be the partial differential coefficient of the forward slip with respect to the inlet thickness, and f be the forward slip value. For the forward sliding, the partial differential coefficient with respect to the exit thickness is . For the partial differential coefficient of the forward slip relative to the backward tension, is the partial differential coefficient of the forward slip relative to the forward tension.

[0113] Export thickness control incremental equation:

[0114]

[0115] In the formula, Let be the partial differential coefficient of the exit thickness with respect to the roll gap, and ΔS be the roll gap increment. This is the partial differential coefficient of the outlet thickness with respect to the inlet thickness. This is the partial differential coefficient of the outlet thickness with respect to the back tension. M is the partial differential coefficient of the outlet thickness with respect to the front tension. m For rolling mill stiffness.

[0116] Incremental equation for tension control:

[0117]

[0118] b S =-Vγ2a S

[0119]

[0120] In the formula, E is the elastic modulus of the strip, and L is the frame spacing. Let be the partial differential coefficient of the rate of change of the front tension with respect to the rear tension. This is the partial differential coefficient of the rate of change of the front tension with respect to the front tension of the next frame, where +1 indicates the next frame. Let be the partial derivative coefficient of the rate of change of the front tension with respect to the front tension. This is the partial differential coefficient of the rate of change of the front tension with respect to the inlet thickness of the next frame. b is the partial differential coefficient of the rate of change of the pretension with respect to the inlet thickness. S Let be the partial derivative coefficient of the rate of change of the front tension with respect to the roll gap. b is the partial differential coefficient of the rate of change of the front tension with respect to the roll gap of the next stand. V Let f be the partial differential coefficient of the rate of change of front tension with respect to the rolling speed, V be the rolling speed, f be the forward slip value, and b be the backward slip value. β1 represents the rate of change of the front tension, and β2 is the partial differential coefficient of the strip deformation resistance with respect to the angular velocity of the work roll.

[0121] Step 2.3: Calculate the partial differential coefficients of each incremental equation based on the rolling schedule data and equipment data.

[0122] Step 2.4: Calculate the state space matrix A, B, C, D, K of the rolling process based on the partial differential coefficients of each incremental equation, and substitute A, B, C, D, K into the state space equation of Step 2.1.

[0123] In this embodiment, the state space matrices A, B, C, D, K are expressed as follows:

[0124]

[0125] Step 3: Calculate the Hurst index for each rolling process parameter to assess the trend of the production process, specifically:

[0126] Step 3.1: Determine the autocorrelation sequence of each rolling process parameter.

[0127]

[0128] Where y(i) are rolling process parameters. Y(j) represents the average value of the rolling process parameters, N represents the number of data points, i represents the i-th data point, and j represents the intermediate variable used to determine the autocorrelation sequence.

[0129] Step 3.2: Divide the autocorrelation sequence into M windows of length l, and calculate the root mean square oscillation f(l) of the autocorrelation sequence when the window length is l:

[0130]

[0131] In the formula, Y m (i) is the autocorrelation sequence of the m-th window. Let a be the least squares fitted curve within the m-th window. m and b m and represent the slope and intercept of the least squares fitted curve, respectively, and i is the i-th data point in the m-th window.

[0132] Step 3.3: Calculate the logarithm log l of multiple sets of l and the logarithm log f(l) of the root mean square fluctuation corresponding to l.

[0133] Step 3.4: Fit the slope ρ of multiple sets of log l and log f(l) using the first-order least squares method.

[0134] Step 3.5: Calculate the Hurst exponent for each rolling process parameter:

[0135]

[0136] Among them, F Hurst The Hurst exponent represents the parameters of the rolling process.

[0137] In this implementation, the Hurst index of the actual exit thickness and the actual back tension of each frame are shown in Table 2.

[0138] Table 2. Hurst index of actual exit thickness and back tension values ​​for each frame.

[0139]

[0140] Step 4: Calculate the LQG index for each rolling process parameter to assess the volatility of the production process, specifically:

[0141] Step 4.1: Define the objective function of LQG:

[0142]

[0143] Among them, u t Let y be the control vector at time t. t Let E be the output vector at time t, E be the expectation, and λ be the weighting coefficient for controlling the input variance.

[0144] Step 4.2: Based on the state-space equations of the rolling process, define the output matrix of the rolling process parameters:

[0145]

[0146] Rewrite the output matrix in subspace matrix equation form:

[0147]

[0148] Among them, y 0|N-1 The output matrix of rolling process parameters, u 0|N-1 This is the input matrix for the rolling process parameters. The input partial differential coefficient matrix, Let be the partial differential coefficient matrix of the noise.

[0149] Substituting the output matrix of the rolling process parameters into the LQG objective function, we obtain:

[0150]

[0151] Step 4.3: Obtain the rolling process parameter output matrix under the optimal control rate. The steps are as follows:

[0152] Make the objective function J with respect to u 0|N-1 Find the partial derivative:

[0153]

[0154] The control law that minimizes the objective function is:

[0155]

[0156] This leads to the output matrix of rolling process parameters under optimal control:

[0157]

[0158] Where I is the identity matrix and λ is the weighting coefficient for the control input variance.

[0159] Step 4.4: Define the coefficient matrix γ:

[0160]

[0161] Step 4.5: Find the optimal output sequence:

[0162]

[0163] Among them, e t-j This represents the noise at time tj.

[0164] Step 4.6: Calculate the covariance of the optimal output sequence:

[0165]

[0166] Among them, Cov[e t-j [Noise e] t-j covariance, For γ j The transpose of , where N is the number of sampling points.

[0167] Step 4.7: Obtain the LQG performance baseline curve, such as... Figure 3 As shown, the steps are as follows:

[0168] Calculate LQG performance benchmark:

[0169]

[0170] By sequentially taking values ​​from 0 to ∞, the optimal input-output variance of the rolling process under the LQG benchmark is obtained, and then the LQG benchmark performance curve of the rolling process is obtained.

[0171] Step 4.8: Calculate the LQG exponent F using the LQG performance benchmark curve. LQG :

[0172]

[0173] Among them, V y This represents the actual variance of the rolling process parameters at time t. Let be the expected variance of the LQG performance benchmark corresponding to the rolling process parameters at time t.

[0174] In this implementation, the LQG index of the actual exit thickness and the actual back tension of each frame is shown in Table 3.

[0175] Table 3. LQG index of actual strip exit thickness and back tension for each stand.

[0176]

[0177] Step 5: Calculate the CUSUM offset index for each rolling process parameter to assess the concentration of the production process, specifically:

[0178] Step 5.1: Calculate the standard deviation σ of the rolling process parameters.

[0179] Step 5.2: Calculate the mean values ​​of the rolling process parameters.

[0180] Step 5.3: Calculate the CUSUM offset index F CUSUM :

[0181]

[0182] Among them, y n This represents the actual value of the nth rolling process parameter.

[0183] In this implementation, the CUSUM offset index of the actual exit thickness and the actual back tension of each rack is shown in Table 4.

[0184] Table 4. CUSUM offset index of actual exit thickness and actual back tension values ​​for each stand in cold rolling mill.

[0185]

[0186] Step 6: Calculate the comprehensive evaluation index of each rolling process parameter.

[0187] In practice, the comprehensive evaluation index of each rolling process parameter is calculated according to the following formula:

[0188]

[0189] Among them, F k The comprehensive evaluation index of the k-th rolling process parameter The Hurst exponent is the parameter of the k-th rolling process. The LQG exponent for the k-th rolling process parameter. CUSUM offset index is the CUSUM of the k-th rolling process parameter.

[0190] In this implementation, the comprehensive evaluation index of the actual value of the exit thickness and the actual value of the back tension of each frame is shown in Table 5.

[0191] Table 5. Comprehensive evaluation index F of actual exit thickness and back tension values ​​for each stand in cold continuous rolling mill. k

[0192]

[0193] Step 7: Calculate the importance coefficients of each rolling process parameter, specifically:

[0194] Step 7.1: Based on the constructed state-space equation of the rolling process, apply 5% noise to each control variable in the rolling process control vector in sequence, and calculate the change ΔW of the rolling process parameter caused by the noise of the p-th control variable. pk .

[0195] Step 7.2: Calculate the relative change of the k-th rolling process parameter.

[0196]

[0197] in, This is the set value for the k-th rolling process parameter.

[0198] Step 7.3: Calculate the importance coefficient of the k-th rolling process parameter.

[0199]

[0200] Where P represents the number of control mechanisms.

[0201] In this implementation, the importance coefficients of the actual values ​​of the exit thickness and the actual values ​​of the back tension for each frame are shown in Table 6.

[0202] Table 6 Importance coefficients of actual exit thickness and back tension values ​​for each stand in cold rolling mill

[0203]

[0204] Step 8: Calculate the comprehensive evaluation index of the continuous rolling process, and evaluate the continuous rolling process according to the health evaluation standard, specifically as follows:

[0205] Step 8.1: Calculate the comprehensive evaluation index of the continuous rolling process:

[0206]

[0207] Where K represents the number of rolling process parameters.

[0208] In this implementation, the comprehensive evaluation index for the continuous rolling process is:

[0209]

[0210] Step 8.2: Establish a health evaluation standard for the continuous rolling process based on the comprehensive evaluation indicators:

[0211]

[0212] When the comprehensive evaluation index is in the range of [0.9, 1], the continuous rolling process is operating very well; when the comprehensive evaluation index is in the range of [0.7, 0.9), the continuous rolling process is deteriorating, but maintenance is not yet required; when the comprehensive evaluation index is in the range of [0, 0.7), the continuous rolling process has malfunctioned and maintenance measures must be taken. Based on the comprehensive evaluation index of 0.854, it is determined that the continuous rolling process is deteriorating at this point, but maintenance is not yet required.

[0213] This embodiment takes the thickness-tension system of a cold continuous rolling production line as an example. Based on the Hurst index, LQG index, and CUSUM offset index, a comprehensive evaluation index of the thickness and tension of each stand in the cold continuous rolling process is calculated. Through rolling characteristic analysis and standardization, the importance index of the thickness and tension of each stand is calculated. Finally, by weighted summation, the comprehensive score of the thickness-tension system of the cold continuous rolling process is obtained. The comprehensive monitoring method for the continuous rolling process that considers rolling characteristics proposed in this invention overcomes the shortcomings of existing methods and improves the detection accuracy of the continuous rolling process.

[0214] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for integrated monitoring of a continuous rolling process taking into account rolling characteristics, characterized in that The application relates to a method for evaluating a continuous rolling process, and comprises the following steps: Step 1: collecting rolling schedule data, equipment data and rolling process parameters on a rolling site; Step 2: constructing a rolling process state space equation for simulating the rolling process according to a rolling theory equation, the rolling schedule data and the equipment data; Step 3: calculating Hurst indexes of the rolling process parameters; Step 4: calculating LQG indexes of the rolling process parameters; Step 5: calculating CUSUM deviation indexes of the rolling process parameters; Step 6: calculating comprehensive evaluation indexes of the rolling process parameters; Step 7: calculating importance coefficients of the rolling process parameters; Step 8: calculating a comprehensive evaluation index of the continuous rolling process, and evaluating the continuous rolling process according to a continuous rolling process health degree evaluation standard; The step 4 is specifically as follows: Step 4.1: defining an LQG target function: wherein, is the control vector at time t, is the output vector at time t, is the expectation, is the weighting coefficient of the control input variance; Step 4.2: defining an output matrix of the rolling process parameters based on the rolling process state space equation: Rewriting the output matrix into a subspace matrix equation form: wherein is an output matrix of rolling process parameters, is an input matrix of rolling process parameters, is a partial differential coefficient matrix of input, is a partial differential coefficient matrix of noise; Bringing the output matrix of the rolling process parameters into the LQG target function to obtain: Step 4.3: obtaining the output matrix of the rolling process parameters under the optimal control rate, and the steps are as follows: minimize the objective function with respect to partial derivatives: Obtaining the control rate for minimizing the target function: Further obtaining the output matrix of the rolling process parameters under the optimal control rate: wherein is the identity matrix, is a weighting coefficient for the control input variance; Step 4.4: Defining the coefficient matrix : Step 4.5: obtaining an optimal output sequence: wherein is noise at the moment Step 4.6: obtaining a covariance of the optimal output sequence: wherein is the covariance of the noise , is the transpose of , is the number of sampling points; Step 4.7: obtaining an LQG performance benchmark curve, and the steps are as follows: Calculating the LQG performance benchmark: sequentially from 0 to , the optimal input-output variance of the rolling process under the LQG benchmark is obtained, and the LQG benchmark performance curve of the rolling process is further obtained. Step 4.8: Obtain LQG index from LQG performance benchmark curve : wherein is the actual variance of the rolling process parameter at the current time instant t, is the expected variance of the LQG performance criterion corresponding to the rolling process parameter at the current time instant t.

2. The comprehensive monitoring method for continuous rolling processes considering rolling characteristics as described in claim 1, characterized in that, The step 2 is specifically as follows: Step 2.1: establishing a rolling process state space equation: wherein is a state space matrix of the rolling process, is a state vector of the rolling process, is a first derivative of , and is a control vector of the rolling process, is a noise vector, is an output vector, and t is time. Step 2.2: establishing rolling force increment equations of each stand, front slip increment equations of each stand, outlet thickness increment equations of each stand and outlet tension increment equations of each stand according to a rolling theory; Step 2.3: calculating partial differential coefficients of the increment equations according to the rolling schedule data and the equipment data; Step 2.4: Calculate the state space matrix of the rolling process according to the partial differential coefficients of the individual balance equations and insert into the state space equations of step 2.

1.

3. The method of claim 1, wherein the rolling characteristic is determined by a rolling characteristic determination unit (30) of the rolling process monitoring system (10). The step 3 is specifically as follows: Step 3.1: determining autocorrelation sequences of the rolling process parameters: Step 3.2: Split the autocorrelation sequence into M windows of length l, and calculate the root mean square fluctuation of the autocorrelation sequence with window length l : wherein is the autocorrelation sequence for the mth window, is the least squares fit curve for the mth window, and are the slope and intercept, respectively, of the least squares fit curve, is the mth data point in the mth window; and is the mth data point in the mth window. Step 3.3: Obtain the logarithm of the set of l and the logarithm of the root mean square fluctuation corresponding to l ; Step 3.4: Fit multiple sets of with the slope of ;​ Step 3.5: obtaining Hurst indexes of the rolling process parameters: wherein, Hurst index representing the rolling process parameters.

4. The comprehensive monitoring method for continuous rolling processes considering rolling characteristics as described in claim 1, characterized in that, The step 5 is specifically as follows: Step 5.1 : Calculate standard deviation of rolling process parameters ; Step 5.2: Calculate the mean value of the rolling process parameters ; Step 5.3: Obtaining the CUSUM shift index : wherein is the actual value of the n-th rolling process parameter, is the average value of the rolling process parameter.

5. The method of claim 1, wherein the rolling characteristic is determined by a rolling characteristic determination unit (30) of the rolling process monitoring system (10). The comprehensive evaluation indexes of the rolling process parameters are calculated according to the following formula in the step 6: wherein, a comprehensive evaluation index of the kth rolling process parameter, is a Hurst index of the kth rolling process parameter, is a LQG index of the kth rolling process parameter, is a CUSUM shift index of the kth rolling process parameter.

6. The method of claim 1, wherein the rolling characteristic is determined by a rolling characteristic determination unit (100) of the rolling process monitoring system (1). The step 7 is specifically as follows: Step 7.1: Based on the constructed rolling process state space equation, 5% noise is applied to each control variable in the rolling process control vector in turn, and the change in the kth rolling process parameter caused by the pth control variable is calculated ; Step 7.2: Calculate the relative variation amount of the kth rolling process parameter : wherein is the set value for the kth rolling process parameter; Step 7.3: Calculate the importance coefficient of the kth rolling process parameter : wherein is the number of control mechanisms.

7. The method of claim 1, wherein the rolling characteristic is determined by a rolling characteristic determination unit (30) of the rolling process monitoring system (10). The comprehensive evaluation index of the continuous rolling process is calculated according to the following formula in the step 8: Step 8: constructing a comprehensive evaluation index of the continuous rolling process, and evaluating the continuous rolling process, and the steps are as follows: Step 8.1: calculating the comprehensive evaluation index of the continuous rolling process: wherein is the number of rolling process parameters; Step 8.2: establishing a continuous rolling process health degree evaluation standard according to the comprehensive evaluation index: When the comprehensive evaluation index is in the interval [0.9, 1], the running state of the continuous rolling process is very good; when the comprehensive evaluation index is in the interval [0.7, 0.9), the running state of the continuous rolling process is degraded but does not need to be maintained; when the comprehensive evaluation index is in the interval [0, 0.7), the continuous rolling process is in failure and must be maintained.

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