A data-based dynamic identification method for roll deflection interference signal of plate shape

Abstract

The application discloses a kind of based on data's plate shape roller deflection interference signal dynamic identification method, it is related to metallurgical rolling technical field, calculation method process includes the following aspects: obtain the data curve detected when plate shape roller works;Two curves are intercepted in the same period data curve outside the sensor package angle;According to the detected data, each parameter in the deflection interference signal function expression is obtained, the deflection interference signal waveform function expression is obtained, and the deflection interference signal generated when plate shape roller works is rapidly dynamically identified.The application can effectively improve the detection accuracy of plate shape roller by real-time monitoring and analysis of plate shape roller detection data, rapidly dynamically identify the deflection interference signal generated when plate shape roller works.

Description

Technical Field

[0001] This invention relates to the field of metallurgical rolling technology, and in particular to a data-based method for dynamic identification of plate roll flexural interference signals. Background Technology

[0002] In the course of my country's industrial development, cold-rolled strip has always occupied an important position as a raw material supplier. Cold-rolled strip is a high-quality metal strip widely used in industries such as automotive, home appliances, electrical and electronic engineering, construction, aerospace, and rail transportation. With the rapid development of aerospace, automotive and shipbuilding, precision instruments, and new energy fields, the demand for improving the quality and quantity of cold-rolled strip production has become increasingly urgent. Strip shape is an important quality indicator of cold-rolled strip, and strip shape detection and control are key technologies. Strip shape meters and strip shape control systems are standard technical equipment for producing high-quality cold-rolled strip and are an inevitable choice for producing high-grade cold-rolled strip. In recent years, the strip shape measurement and control technology team at Yanshan University has independently developed a seamless strip shape roll and a wireless embedded signal transmission and processing device, and has carried out a series of practical applications on industrial cold strip mills.

[0003] In the experimental calibration and industrial applications of shape measuring instruments, it was found that when the shape roller is running unloaded (only subject to gravity, without calibration pressure or strip pressure), the detection unit outputs a large-range, approximately sinusoidal waveform signal over 360°. When the shape roller is subjected to calibration pressure and strip pressure, the amplitude of this large-range waveform increases, superimposing with the small-range shape signal from the strip wrap angle, causing shape detection errors. This is especially problematic when rolling extremely thin strips, where the rolling tension is relatively low; in such cases, the shape detection error can reach over 15%.

[0004] Both Fourier transform and digital filtering can largely eliminate the influence of deflection signals. However, Fourier transform is slow and requires a large number of sampling points, making it suitable for analysis but not for online applications. Digital filtering is suitable for situations where the rolling speed is constant, i.e., the frequencies of the influencing and effective signals are fixed, but it is difficult to apply to industrial shape detection where the rolling speed and roll speed are highly time-varying. Furthermore, current methods for eliminating deflection signals aim to match the phase of the interference signal waveform with the phase of the detected signal. However, the actual maximum and minimum values ​​of the deflection signal are located at the bottom and top of the roll, while the detected signal value appears at the wrap angle, resulting in a phase discrepancy between the two signals. Therefore, it is currently impossible to accurately identify the deflection interference signal in the shape detection signal. Summary of the Invention

[0005] To quickly and accurately identify flexural interference signals in the online shape detection signal and improve the detection accuracy of the shape roller, this invention provides a data-based dynamic identification method for flexural interference signals of the shape roller.

[0006] Research on the identification of flexural interference signals revealed that while the flexural interference signal and the plate shape signal have the same period, their amplitudes differ. In studies on the identification and elimination of flexural interference signals, most studies assume that the phases of the flexural interference signal and the plate shape signal are the same. However, in actual detection, it has been found that their phases are not necessarily the same, sometimes exhibiting a certain deviation. Furthermore, the flexural interference signal changes with the rotational speed of the plate shape roller, thus making accurate identification impossible. This invention provides a data-based dynamic identification method for plate shape roller flexural interference signals, which can dynamically identify flexural interference signals online based on the signals detected by the plate shape roller.

[0007] This invention provides a data-based method for dynamic identification of plate roll flexural interference signals, the specific steps of which are as follows:

[0008] S1. Based on the diameter of the plate roller The sampling frequency of the signal acquisition processor The linear velocity of the plate roller rotation Calculate the number of sampling points included in one sampling period of the plate roll. ,in ;

[0009] S2. Select a sampling period for a region, and extract two segments from the data curve outside the sensor's envelope angle, with each segment accounting for 10% to 20% of the total length.

[0010] S3. Obtain the AD value corresponding to each sampling point in this period, and calculate the function expression of the flexural interference signal in this period. The specific implementation steps are as follows:

[0011] S31. Let the starting point of the first segment of the curve be A ( The endpoint is B ( Let the starting point of the second curve be C (); The endpoint is D ( ); obtain the AD values ​​corresponding to each sampling point of the two curve segments;

[0012] S32. The function waveform of the flexural interference signal is expressed as follows:

[0013]

[0014] in, The amplitude of the flexural waveform function; The angular frequency of the flexural waveform function affects the period; This represents the initial phase of the flexural waveform function; For zero-point drift of the flexural waveform;

[0015] S33. Since one rotation of the plate roller constitutes one signal cycle, one signal cycle contains... There are 1 sampling point, so

[0016]

[0017] S34, to exist Points accumulation:

[0018]

[0019] Right now:

[0020]

[0021] Performing sum-to-product transformation on the above equation, we get:

[0022]

[0023] Similarly, for exist Integrating, we get:

[0024]

[0025] Right now:

[0026]

[0027] S35. Obtain sampling points The AD value at time is denoted as y. Substitution

[0028]

[0029] have to:

[0030] We can obtain:

[0031] S36, will , Substitution

[0032]

[0033] have to:

[0034]

[0035] Further simplification yields:

[0036]

[0037] Right now:

[0038]

[0039] Solving this system of equations will yield the answer. and The value;

[0040] Therefore, it can be based on Seeking The value;

[0041] Finally, By substituting the values, the waveform function of the flexural interference signal can be obtained.

[0042]

[0043] Therefore, the method provided by this invention can accurately and dynamically identify the flexural interference signal in the plate shape detection signal online, thereby improving the detection accuracy of the plate shape roller.

[0044] Preferably, the plate roller has two or four rows of through holes along the roller body axis on its circular side, and multiple sensors are arranged at intervals in the through holes, with multiple sensors on each circumference forming a detection unit.

[0045] Preferably, the sensor is a piezoelectric sensor, and the output of each sensor is connected to the input of a plate-shaped signal processor.

[0046] Preferably, the waveform of the final obtained flexural interference signal can be obtained by subtracting the flexural interference signal value from the value of the original signal to dynamically obtain the true value of the plate shape detection signal.

[0047] Preferably, when the strip is subjected to tension and wrapped around the surface of the shape roll, the residual stress distribution of the cold-rolled strip caused by the shape error is quantitatively output in the form of relative length difference by measuring the magnitude of the unevenly distributed radial force along its axial direction. The result is the strip shape distribution measured by each detection unit of the shape roll.

[0048] The advantages of this invention compared to the prior art are:

[0049] (1) The present invention dynamically identifies the flexural interference signal in the plate shape detection signal by the plate shape data detected online by the plate shape roller, and calculates the waveform function of the flexural interference signal, thereby improving the detection accuracy of the plate shape roller; (2) The present invention can effectively avoid the phase error between the plate shape waveform signal and the flexural interference signal detected by the plate shape roller, improve the recognition accuracy, identify the flexural interference signal at different speeds online, and improve the accuracy of the plate shape detection by the plate shape roller. Attached Figure Description

[0050] Figure 1 This is a flowchart of the recognition algorithm calculation of the present invention.

[0051] Figure 2 This is a schematic diagram of the original signal for plate roller detection in this invention.

[0052] Figure 3 This is a schematic diagram of the calibration experiment of the present invention.

[0053] Figure 4 This is a waveform diagram of the experimental channel of the present invention.

[0054] Figure 5 This is a diagram showing the effect of the force signal detected during operation of the present invention when the plate roller rotates at a speed of 400 r / min.

[0055] Figure 6 This is a diagram showing the effect of the force signal detected after the invention is running at a plate roller speed of 400 r / min.

[0056] Figure 7 This diagram illustrates the effect of the force signal not detected during operation in industrial applications.

[0057] Figure 8 This is a diagram showing the effect of force signal detection after the invention is run in industrial applications.

[0058] Figure 9 This is a plate shape diagram obtained after the invention has been run in an industrial application. Detailed Implementation

[0059] The present invention will be further described below with reference to specific embodiments. The illustrative embodiments and descriptions herein are merely descriptions of preferred embodiments of the present invention and are not intended to limit the present invention.

[0060] like Figure 1 As shown, this invention provides a data-based method for dynamic identification of flexural interference signals in plate rolls, primarily through computer-based data processing and calculation. The specific operation steps of the dynamic identification method are as follows:

[0061] S1. Based on the diameter of the plate roller The sampling frequency of the signal acquisition processor The linear velocity of the plate roller rotation Calculate the number of sampling points included in one sampling period of the plate roll. ,in ;

[0062] S2. Select a sampling period and extract two segments from the data curve outside the sensor's envelope angle, with each segment accounting for 10% to 20% of the total length.

[0063] S3. Obtain the AD value corresponding to each sampling point within this period, and calculate the function expression of the flexural interference signal within this period. In this embodiment, as shown... Figure 2 As shown, within one sampling period, two segments of the curve at positions 1 and 2 are extracted to obtain the AD values ​​corresponding to the sampling points within curve 1 and curve 2.

[0064] The specific implementation steps are as follows:

[0065] S31. Let the starting point of the first segment of the curve be A ( The endpoint is B ( Let the starting point of the second curve be C (); The endpoint is D ( ); obtain the AD values ​​corresponding to each sampling point of the two curve segments;

[0066] S32. The function waveform of the flexural interference signal is expressed as follows:

[0067]

[0068] in, The amplitude of the flexural waveform function; The angular frequency of the flexural waveform function affects the period; This represents the initial phase of the flexural waveform function; For zero-point drift of the flexural waveform;

[0069] S33. Since one rotation of the plate roller constitutes one signal cycle, one signal cycle contains... There are 1 sampling point, so

[0070]

[0071] S34, to exist Points accumulation:

[0072]

[0073] Right now:

[0074]

[0075] Performing sum-to-product transformation on the above equation, we get:

[0076]

[0077] Similarly, for exist Integrating, we get:

[0078]

[0079] Right now:

[0080]

[0081] S35. Obtain sampling points The AD value at time is denoted as y. Substitution

[0082]

[0083] have to:

[0084] We can obtain:

[0085] S36, will , Substitution

[0086]

[0087] have to:

[0088]

[0089] Further simplification yields:

[0090]

[0091] Right now:

[0092]

[0093] Solving this system of equations will yield the answer. and The value;

[0094] Therefore, it can be based on Seeking The value;

[0095] Finally, By substituting the values, the waveform function of the flexural interference signal can be obtained.

[0096]

[0097] Therefore, when the plate roller is working online, the waveform of the flexural interference signal can be dynamically identified in real time through the detected signal data.

[0098] The data-based dynamic identification method for flexural interference signals of the plate roll provided by this invention is written into a LabVIEW program and written into the plate roll shape detection system, and works during the plate roll shape detection signal processing.

[0099] First, the algorithm effectiveness of the present invention is verified using a calibration device. The calibration device is mounted and fixed on the bearing seat of the plate roller. The roller is placed on the channel area to be tested, and the plate roller is started to conduct the experiment. The installation principle and effect are as follows: Figure 3 As shown. Figure 4The waveform diagrams for the experimental channels include the original signal curve, the deflection signal curve, and the true signal curve. The original signal is the signal acquired by the plate roll, the deflection signal is the signal identified by this invention, and the true signal is the actual value of the original signal after removing deflection interference. In the experiment, the waveform of the deflection signal identified by this invention almost completely overlaps with the waveform of the original signal outside the peak segment. The values ​​of the true signal curve outside the peak are almost all 0, indicating that the waveform of the deflection signal identified by this invention is very accurate. This method was used to experiment on each channel of the plate roll. The deflection signal identified by the recognition algorithm provided by this invention is very close to the actual value, and the accuracy of the true value of the plate roll signal calculated by this algorithm is very high, with an error of 0.5I.

[0100] Then, experiments were conducted to test the recognition performance of this invention at various speed stages. Under no-load conditions, since the plate roller is unloaded, both the radial force and plate shape values ​​detected by the plate roller should be zero. Therefore, the plate roller can be started under no-load conditions, and the algorithm effect of this invention was verified through the radial force detection interface. The experimental results are as follows: Figure 5 and Figure 6 As shown, Figure 5 When the plate roller rotates at 400 r / min, this invention does not detect any force signal during operation. Figure 6 This is the force signal detected after the invention has been running at a plate roller speed of 400 r / min, compared with... Figure 5 and Figure 6 The two figures show that after the invention was implemented, the radial force detection value dropped to below 0.5N, a significant reduction compared to before implementation, approaching the ideal real-world state. The identification algorithm provided by this invention maintained the unloaded radial force below 1N across all speed ranges, demonstrating the significant effectiveness of the invention. By using the data-based dynamic identification method for plate roll deflection interference signals provided by this invention, the plate roll deflection interference signal attached to the plate shape detection signal can be accurately and dynamically identified, significantly reducing the impact of the plate roll interference signal on the detection signal.

[0101] Furthermore, the data-based dynamic identification method for plate roll flexure interference signals provided by this invention has also been applied in a practical industrial setting. The results of this practical industrial application are as follows: Figures 7-9 As shown, Figure 7 This invention does not detect force signals during runtime. Figure 8 This is the force signal detected after the invention is running. Figure 9This is a plate shape diagram detected after the operation of this invention. Before the operation of this invention, the detected radial force had a distinct sawtooth shape, which did not match the actual plate shape and was greatly affected by the deflection interference signal. After the operation of this invention, the detected radial force signal was relatively smooth and matched the actual plate shape. Therefore, by using the data-based plate shape roller deflection interference signal dynamic identification method provided by this invention, the plate shape roller deflection interference signal attached to the plate shape detection signal can be accurately and dynamically identified, greatly improving the detection accuracy of the plate shape roller.

[0102] The working principle of this invention is as follows: When the plate roll is working, based on the raw data collected by the plate roll, the flexural interference signal in the plate roll detection signal is dynamically identified using the identification method provided by this invention. The calculation method includes the following aspects: 1. Obtaining the data curve detected when the plate roll is working; 2. Extracting two curve segments from the same period data curve outside the sensor's wrap angle; 3. Calculating the parameters in the flexural interference signal function expression based on the detected data, thus obtaining the flexural interference signal function expression and rapidly and dynamically identifying the flexural interference signal generated when the plate roll is working. This invention, through real-time monitoring and analysis of the plate roll detection data, rapidly and dynamically identifies the flexural interference signal generated when the plate roll is working, effectively improving the detection accuracy of the plate roll.

Claims

1. A data-based method for dynamic identification of plate roll flexure interference signals, characterized in that: The dynamic recognition method includes the following process: S1. Based on the diameter of the plate roller The sampling frequency of the signal acquisition processor The linear velocity of the plate roller rotation Calculate the number of sampling points included in one sampling cycle of the plate roll. The calculation formula is: ; The plate roller has two or four rows of through holes along the axial direction of the roller body on its circular side. Multiple sensors are arranged at intervals in the through holes, and multiple sensors on each circumference form a detection unit. The sensors are piezoelectric sensors, and the output of each sensor is connected to the input of a plate-shaped signal processor. S2. Select a sampling period for a region, and extract two segments from the data curve outside the sensor's envelope angle, with each segment accounting for 10% to 20% of the total length. S3. Obtain the AD value corresponding to each sampling point in this period, and calculate the function expression of the flexural interference signal in this period. The implementation steps are as follows: S31. Let the starting point of the first segment of the curve be A ( The endpoint is B ( Let the starting point of the second curve be C (); The endpoint is D ( ); obtain the AD values ​​corresponding to each sampling point of the two curve segments; S32. The function waveform of the flexural interference signal is expressed as follows: ； in, The amplitude of the flexural waveform function; The angular frequency of the flexural waveform function affects the period; This represents the initial phase of the flexural waveform function; For zero-point drift of the flexural waveform; S33. Since one rotation of the plate roller constitutes one signal cycle, one signal cycle contains... There are 1 sampling point, therefore: ； S34, to exist Points accumulation: ； Right now: ； Performing sum-to-product transformation on the above equation, we get: ； Similarly, for exist Integrating, we get: ； Right now: ； S35. Obtain sampling points The AD value at time is denoted as y. Substitution ； have to: ; Find: ; S36, will , Substitution ； have to: ； Further simplification yields: ； Right now: ； Solve this system of equations to obtain and The value; Therefore, according to Seeking The value; Finally, Substitution ； Find the waveform function of the flexural interference signal; Therefore, when the plate roll is working online, the waveform of the flexural interference signal detected by each detection unit can be dynamically identified in real time through the detected signal data.

2. The data-based dynamic identification method for plate roll flexure interference signals as described in claim 1, characterized in that: Finally, the waveform of the flexural interference signal is obtained. By subtracting the flexural interference signal value from the original signal value, the true value of the plate shape detection signal is dynamically obtained.

3. The data-based dynamic identification method for plate roll flexure interference signals as described in claim 2, characterized in that: When the strip is subjected to tension and wrapped around the surface of the shape roll, the residual stress distribution caused by the shape error of the cold-rolled strip is quantitatively output in the form of relative length difference by measuring the magnitude of the unevenly distributed radial force along its axial direction. The result is the strip shape distribution measured by each detection unit of the shape roll.

Images