Center line deviation judgment method based on data analysis

Abstract

The invention relates to a central line deviation judgment method based on data analysis. The central line deviation judgment method comprises the steps of data acquisition, strip steel full-length deviation judgment, head deviation judgment, tail deviation judgment, head extreme value deviation judgment, tail extreme value deviation judgment and the like. According to the center line deviation judgment method based on data analysis, the problem that hot shutdown is generated in the follow-up hot rolling process can be solved, the problem of strip steel deviation generated in the rolling process is solved, and real-time judgment and tracking analysis are conducted on the steel coil with center line deviation. The hot shutdown event caused by the offset of the center line can be prevented, and the cause of the phenomenon can be solved and analyzed from the source, so that the production quality is improved.

Description

Technical Field

[0001] The invention relates to a method for determining center line deviation based on data analysis, and belongs to the technical field of hot rolling. Background Art

[0002] During the rolling process of the Meishan hot rolling line 1780, the strip rolling center line deviated. For the steel coils with such problems, the strip will deviate and even cause scrap steel in the subsequent production, which will cause the subsequent cold rolling process of the strip to be shut down. Summary of the invention

[0003] The technical problem to be solved by the present invention is: to overcome the disadvantages of the above-mentioned technology and to provide a method for determining the centerline deviation of the entire steel coil.

[0004] In order to solve the above technical problems, the technical solution proposed by the present invention is: a method for determining centerline deviation based on data analysis, comprising the following steps: (1) During the hot rolling mill, the centerline deviation data of the entire length of the strip is collected; (2) After taking the absolute value of the centerline deviation data, it is compared with the first threshold value. If it exceeds the first threshold value, it is determined that the centerline deviation has occurred; (3) Take the data of the 100-meter strip head in the centerline deviation data, select the maximum and minimum values, and compare the difference between the two with twice the second threshold. If it exceeds twice the second threshold, it is determined that the centerline deviation has occurred; (4) Take the data of the tail 100 m of the strip in the centerline deviation data, select the maximum and minimum values, and compare the difference between the two with twice the third threshold. If it exceeds twice the third threshold, it is determined that the centerline deviation has occurred; (5) Take the data of 100 m of the strip head in the centerline deviation data, where 20 consecutive data are taken as an interval, select the maximum and minimum values ​​in each interval, and compare the difference between the two with the fourth threshold. If the difference exceeds the fourth threshold, it is determined that the centerline deviation occurs; (6) Take the data of the tail 100 m of the strip in the centerline deviation data, where 20 consecutive data are taken as an interval, select the maximum and minimum values ​​in each interval, and compare the difference between the two with the fourth threshold. If the difference exceeds the fourth threshold, it is determined that the centerline deviation occurs.

[0005] A further improvement of the above scheme is that in the step (1), when collecting the centerline offset data of the entire length of the strip, one data is collected at an interval of 1 meter.

[0006] A further improvement of the above scheme is that in step (1), the collected data is formed into a curve for display.

[0007] A further improvement of the above scheme is that in the step (1), the data of the 100-meter head of the strip and the data of the 100-meter tail of the strip in the centerline offset data are taken and each is formed into a curve for display.

[0008] A further improvement of the above scheme is that in the steps (5) and (6), the difference between the maximum value and the minimum value in each interval is respectively formed into a curve for display.

[0009] A further improvement of the above scheme is that the first threshold, the second threshold, the third threshold and the fourth threshold are derived from the hot rolling field experience data, the first threshold value interval is [110, 120], the second threshold value interval is [100, 110], the third threshold value interval is [90, 110], and the fourth threshold value interval is [90, 110].

[0010] The centerline deviation determination method based on data analysis provided by the present invention can improve the problem of hot shutdown caused by the subsequent process of hot rolling, solve the problem of strip deviation during rolling, and conduct real-time determination and tracking analysis of the steel coil with centerline deviation. It can not only prevent hot shutdown events caused by centerline deviation, but also solve and analyze the causes of such phenomena from the source, thereby improving production quality. Implementation

[0011] Embodiment: The centerline deviation determination method based on data analysis of this embodiment includes the following steps: (1) When the hot rolling mill is rolling, the centerline offset data of the entire length of the strip is collected; the centerline offset data is collected at intervals of 1 meter, and is disabled by the data collector of L1, uploaded to the Oracle database of L2 via telegram, and stored in the SEQ data set. (2) After taking the absolute value of the centerline deviation data, it is compared with the first threshold value. If it exceeds the first threshold value, it is determined that the centerline deviation has occurred; (3) Take the data SEQ_head of the strip head 100 meters in the centerline offset data, and select the maximum value SEQ_head_max and the minimum value SEQ_head_min. The difference between the two, SEQ_head_max-SEQ_head_min, is compared with twice the second threshold. If it exceeds twice the second threshold, it is determined that the centerline offset occurs. (4) Take the data SEQ_tail of the tail of the strip steel of 100 meters in the centerline deviation data, and select the maximum value SEQ_tail_max and the minimum value SEQ_ tail _min. The difference between the two, SEQ_tail_max-SEQ_ tail _min, is compared with twice the third threshold value. If it exceeds twice the third threshold value, it is determined that the centerline deviation occurs. (5) Take the data SEQ_head of the 100-meter strip head in the centerline deviation data, where 20 consecutive data are taken as an interval, with a total of 81 intervals. In each interval, select the maximum value SEQ_max(i) and the minimum value SEQ_min(i), where i=[1,81]. Compare the difference between the two, SEQ_max(i)-SEQ_min(i), with the fourth threshold. If it exceeds the fourth threshold, it is determined that the centerline deviation occurs. (6) Take the data of the tail 100 meters of the strip in the centerline deviation data, and take 20 consecutive data as an interval, for a total of 81 intervals. Select the maximum value SEQ_max(j) and the minimum value SEQ_min(j) in each interval, where j = [1,81]; compare the difference between the two, SEQ_max(i) - SEQ_min(i), with the fourth threshold. If it exceeds the fourth threshold, it is determined that the centerline deviation has occurred.

[0012] In step (1), the collected data is displayed by connecting two adjacent points to form a curve, and the first threshold is displayed as a straight line.

[0013] In step (1), the data of the 100-meter head of the strip and the data of the 100-meter tail of the strip in the centerline offset data are taken and each is formed into a curve for display. At the same time, the second threshold and the third threshold are each displayed as a straight line.

[0014] In step (5) and step (6), the difference between the maximum value and the minimum value in each interval is displayed by connecting two adjacent points to form a curve, and the fourth threshold is displayed as a straight line.

[0015] Furthermore, when one of the above curves exceeds the threshold, it is highlighted and voice broadcast is given to prompt the operator.

[0016] In this embodiment, the first threshold, the second threshold, the third threshold and the fourth threshold are all 120.

[0017] Through the above steps, the quality of the rolled steel coil can be judged to determine whether a center line deviation occurs, and then the impact on subsequent processes can be determined.

[0018] The present invention is not limited to the above embodiments. Any technical solution formed by equivalent replacement falls within the protection scope required by the present invention.

Claims

1. A method for determining centerline deviation based on data analysis, characterized in that: The steps include: (1) During the hot rolling mill, the centerline deviation data of the entire length of the strip is collected; (2) After taking the absolute value of the centerline deviation data, it is compared with the first threshold value. If it exceeds the first threshold value, it is determined that the centerline deviation has occurred; (3) Take the data of the 100-meter strip head in the centerline deviation data, select the maximum and minimum values, and compare the difference between the two with twice the second threshold. If it exceeds twice the second threshold, it is determined that the centerline deviation has occurred; (4) Take the data of the tail 100 m of the strip in the centerline deviation data, select the maximum and minimum values, and compare the difference between the two with twice the third threshold. If it exceeds twice the third threshold, it is determined that the centerline deviation has occurred; (5) Take the data of 100 m of the strip head in the centerline deviation data, where 20 consecutive data are taken as an interval, select the maximum and minimum values ​​in each interval, and compare the difference between the two with the fourth threshold. If the difference exceeds the fourth threshold, it is determined that the centerline deviation occurs; (6) Take the data of the tail 100 m of the strip in the centerline deviation data, where 20 consecutive data are taken as an interval, select the maximum and minimum values ​​in each interval, and compare the difference between the two with the fourth threshold. If the difference exceeds the fourth threshold, it is determined that the centerline deviation occurs.

2. The method for determining centerline deviation based on data analysis according to claim 1, characterized in that: In the step (1), when collecting the centerline offset data over the entire length of the strip, one data point is collected at intervals of 1 meter.

3. The method for determining centerline deviation based on data analysis according to claim 2, characterized in that: In the step (1), the collected data is formed into a curve for display.

4. The method for determining centerline deviation based on data analysis according to claim 2, characterized in that: In the step (1), the data of the 100-meter head of the strip and the data of the 100-meter tail of the strip in the centerline offset data are taken and each is formed into a curve for display.

5. The method for determining centerline deviation based on data analysis according to claim 2, characterized in that: In the steps (5) and (6), the difference between the maximum value and the minimum value in each interval is displayed as a curve.

6. The method for determining centerline deviation based on data analysis according to claim 1, characterized in that: The first threshold, the second threshold, the third threshold and the fourth threshold are derived from the hot rolling field experience data. The first threshold value interval is [110, 120], the second threshold value interval is [100, 110], the third threshold value interval is [90, 110], and the fourth threshold value interval is [90, 110].