JPS5912309A - Method for detecting abnormal part of profile of belt shaped body - Google Patents

Abstract

PURPOSE:To detect the abnormal part of the profile of a belt shaped body accurately without receiving the effect of temporary noises at the time of measurement, by decomposing the measured value at each point in the width direction into frequency components by Fourier transformation. CONSTITUTION:A physical quantity A(i) (i=1-N) of each point of a belt shaped body 10 in the width direction, e.g., a point (n), is measured at each point (e.g., a point N) in the longitudinal direction. Then, the measured value xj (j=1-n, where n is power of 2 in general) at each point in the width direction is decoposed into frequency components ak and bk by Frourier transformation by using an expression 1. The frequency component B(i) that is obtained in this way indicates the frequency component of 2pik/n of the original curve. The parts, where (k) is small, medium, and large, are low, medium, and high frequency parts, respectively. Then, only the medium frequency region, wherein an abnormal protruded part to be detected is present, in the frequency component B(i) undergoes inverse Fourier transformation. Thus the abnormal protruded part is extracted.

Description

DETAILED DESCRIPTION OF THE INVENTION The present invention relates to a method for detecting an abnormal part in a strip-shaped object profile, and in particular, a physical quantity of a strip-shaped object suitable for detecting an abnormal part in a thickness profile of a strip-shaped steel plate. The present invention relates to an improvement in a method for detecting an abnormality in a band-shaped object profile for detecting an abnormality in a profile obtained by measuring at each point in a direction.

Generally, the widthwise distribution of physical quantities of a strip-shaped object, for example, the widthwise distribution of the plate thickness and temperature of a strip-shaped steel plate, is collectively called a profile. The shape of this profile is usually an arc, but sometimes there is an abnormality in which the absolute value suddenly becomes thicker (or smaller) discontinuously in a part of the force direction distribution. In some cases, a strip-shaped object is manufactured that has a high spot (referred to as a high spot).In this case, not only does rolling work etc. not work smoothly, but the shape of the strip-shaped object itself becomes defective, resulting in serious quality defects. Become.

Therefore, when manufacturing a strip-shaped object, it is important to ensure that there are no high spots.In a hot strip mill, etc., when rolling a strip, the thickness of the strip in the width direction is periodically adjusted. It is common practice to carry out the entire rolling process while measuring the profile.

In addition, in order to detect such an abnormal part of the profile, for example, as shown in JP-A-54-155164, the thickness of the copper strip is measured in the width direction, and the actual measured value is subjected to Fourier expansion regression and polynomial expression. A curve is obtained by one of the expansion regressions, an upper limit value and a lower limit value are determined by adding a tolerance to this curve, and a comparison is made to see whether the actual measured value is included between these upper limit values and lower limit values. , if there is a measured value that is not included, this is determined to be a HISbot.

A high spot detection method has been proposed. According to this HISBOTS detection method, the HISBOTS can be detected without human intervention by measuring the widthwise thickness of a steel strip and based on the results. There is no function to remove this problem, and it may occur depending on the measurement conditions when measuring the profile.

Temporary noise that shows the same measurement value as the abnormal part has the disadvantage that it is detected as an abnormal part. In other words, as shown in Figure 1 (A), one edge of the profile In addition to the abnormal part of the needle A that occurred at a position B away from the other edge, for example, it occurred at a position i that was depressed by C from the other edge, as shown in FIG. 1 CB. Temporary noises similar to noise spots were also incorrectly detected as abnormal parts such as noise spots.

The present invention has been made in order to eliminate the above-mentioned conventional drawbacks, and is capable of accurately detecting an abnormal part of a band-shaped object profile without being affected by constant white noise or temporary noise during measurement. The purpose of the present invention is to provide a method for detecting abnormalities in a band-shaped object profile.

The present invention provides a method for detecting abnormalities in a profile of a strip-shaped object obtained by measuring the physical characteristics of the strip-shaped object at each point in the width direction. In the four-way area detection method, the measured value at each point in one width direction is decomposed into each frequency component by Fourier transformation.

Next, an extraction profile obtained by inversely transforming the middle frequency range of the frequency component using inverse Fourier transform is obtained for each point in the longitudinal direction of the strip object, and then the minimum value of each point in the width direction of the extraction profile is calculated. The above object is achieved by obtaining a minimum extraction profile consisting of the following, and determining that all points outside the minimum extraction profile are abnormal parts.

The present invention will be explained in detail below. .

First, as shown in FIG. 2, each point in the width direction of the strip-shaped object lO,
For example, the physical f&At1) opening=l-N)t at n points is measured at each point in the longitudinal direction, for example, N points.

Next, the measured value xj (j=x−n−
n is generally a power of 2), and the following equations are used to calculate each frequency component (and amplitude) ak, bk by Fourier transform.
Decompose into.

The frequency component B(1) obtained in this way is k f
A region where +- is small is a low frequency region, a region where k is medium is a medium frequency region, and a region where k is large is a high frequency region.

Next, only the medium frequency region of the frequency component B(i) in which the abnormal protrusion to be detected exists is inversely transformed by inverse Fourier transform to obtain an extraction profile C(i)k in which the abnormal protrusion is extracted.

Generally, inverse Fourier transform is expressed by a linear equation.

When completely integrated, it returns to the original curve.

+”nt!08πi C1=0.・-, n-1)−・
・(2) Therefore, inverse transformation in the medium frequency range is for 3 and t that satisfy 4 < s < t < 2 in equation (2).

In the wave region, the inverse Fourier transform in the medium frequency region is.

It is as shown in the following equation.

As shown in FIG. 3(A), 1. c Width direction distribution A (1
) is decomposed into each frequency component B (i) by Fourier transform, and then the frequency component B (
The extraction profile Ct of the low frequency range obtained by inversely transforming the low frequency range of 1) using the inverse 7-lier transform, (
1) is shown in FIG. 3(B), and the extraction profile in the high frequency range CH(1) t FIG. 3(c) VC.

Similarly, the extraction profile C(i)e in the medium frequency range is shown in Fig. 3C.
D) shows each.

Now, the extraction profile C(1) in the medium frequency range obtained from the profile A(1) as shown in Figure 3 above is
As shown in Fig. 4 (A), CB), and (C), respectively, Fig. 4 (D) consists of the minimum value of each point in the width direction of each extraction profile obtained in this way. A minimum extraction profile D as shown is obtained, and points where the minimum extraction profile D is not included in the allowable range are determined to be abnormal parts.

This makes it possible to detect abnormalities with high accuracy without being affected by constant white noise or temporary noise during measurement.

Detection of an abnormality in a band-shaped object profile in the present invention is as follows:
Specifically, the process is executed according to the flowchart shown in FIG.

That is, first, in step 101, the count value of the counter i corresponding to the longitudinal position of the strip-shaped object is set to 1. Next, the process proceeds to step 102, where the width direction physical quantity actual value A(1) (profile) at the longitudinal position l of the strip-shaped object is measured. Next, proceed to step 103 K, and the actual measurement value A(1)
From, the frequency component B(1)'f is obtained by Fourier transform FT.
r, get. Next, the process proceeds to step 104, where frequency component B
An extraction profile C(i) is obtained from the medium frequency region of (i) by inverse Fourier transformation. Then proceed to step 105,
It is determined whether the count value of counter i is greater than or equal to N. If the result is negative, the count value of counter 1 is incremented by 1 in step 106, and the process returns to step 102 described above.

For example, if the actual measured value of the physical quantity A (1) in the width direction when N = 1 is as shown in Fig. 6 (A), the frequency component B (1) is as shown in Fig. 6 (B). (And furthermore, the extraction profile C(1) is as shown in FIG. 6 CC'). Also, suppose that the actual measured value of the physical quantity A(2) in the width direction when N=2 is as shown in FIG. 7(A), ζ,
The frequency component B(2) is as shown in FIG. 7(CB), and the extraction profile C(2) is as shown in FIG. 7(C).

That is, the abnormal part D shown in FIG. 6(A) and FIG. 7(A),
F and the temporary noise G are shown in Fig. 6(C) and Fig. 7(
The protrusions F, H, and I shown in C) are extracted.

When the determination result in step 105 is positive, that is, when the measurement at each point in the longitudinal direction is completed, the process proceeds to step 3.07, and N extraction profiles C(i) are obtained.
Therefore, the value with the minimum absolute value at each point in the width direction is extracted, and the minimum extraction profile Di consisting of that value is obtained. This minimum extraction proa (-AD is 1, for example, as shown in FIG. 8).

Then, proceeding to step 108, the minimum extraction profile D
However, a point J exceeding preset upper and lower limit values is determined to be an abnormal portion. Further, the process proceeds to step 109, where the absolute value of the normal part J is set to the abnormality size of 8, and abnormal part detection is ended.

Next, an embodiment of an apparatus for detecting an abnormality in the thickness profile of a strip steel plate during hot rolling, in which the method for detecting an abnormality in the profile of a strip according to the present invention is adopted, will be described in detail.

In this embodiment, wheels 14a are used to measure the thickness of a steel strip 12 in a non-contact manner at each point in the width direction of the steel strip 12, which is moving in a direction perpendicular to the plane of paper, as shown in FIG.
2, an X-ray plate thickness gauge 14 that is movable in the width direction of the
Thick cut wire plate with 14 outputs.

The plate thickness measurement value at each point in the width direction of the steel strip 120 is measured at high speed 7.
- decomposed into each frequency component by Rie transform,
An extraction profile obtained by inversely transforming the middle frequency range of the frequency component by inverse fast Fourier transform is determined for each point in the longitudinal direction of the steel strip 12 - Then, the minimum value of the extraction profile at each point in the width direction is determined. a digital computer 16 consisting of, for example, a microcomputer, for obtaining a minimum extraction profile consisting of a
Cathode # for displaying a minimum extraction profile of 6 outputs
1 tube display device 18.

In this embodiment, the data interval in one width direction is 2.5 tll.

The number of data? 1:512, and measured the plate thickness profile at two points in the longitudinal direction.
) and CB). This measurement value.

Decompose into one frequency component using fast Fourier transform.

The middle frequency range is 8 to 100 Hz (a=8, t=10
0) was inversely transformed by inverse fast Fourier transformation, a γL extraction profile was obtained as shown in Figure 1O (C) and (1)). In addition, in this multi-speed Fourier conversion e, 1280 (2,5×512) is considered as one wavelength. From the extraction profiles shown in Figures 1O (C) and (D), the minimum extraction profiles shown in Figures 1O (E) and K can be obtained, and by setting the upper and lower limits to ±3 μm, J! ! Regular part J (detection position 2.5 xjth data)
I was able to measure 4 bundles of.

In this embodiment, since the Fourier transform and the inverse Fourier transform are performed using the full fast Fourier transform and the inverse fast 7-lier transform, the Fourier transform operation can be performed quickly. 1. However, methods of performing Fourier transform and inverse Fourier transform are not supported by this.

In the above-mentioned example, the present invention is applied to abnormal heat detection of the thickness profile of a strip steel plate during hot rolling. Detection of abnormalities in the temperature profile of thin plate rolling V, etc.
It is clear that abnormal heat detection V (of profiles of other strip objects) is equally applicable.

As explained above, 1. According to the present invention, it is not affected by constant white noise or temporary noise during measurement; 7. c<
This method has the excellent effect of being able to detect abnormalities in the band-shaped object profile with high accuracy.

[Brief explanation of the drawing]

Figure 1 (A) is a diagram showing an example of a profile where a high spot has occurred, Figure 1 (CB) is a diagram showing an example of a profile where a high spot and temporary noise have occurred, and Figure 2 is a diagram showing an example of a profile where a high spot and temporary noise have occurred. FIG. 3 is a plan view showing measurement positions in the width direction and longitudinal direction of a strip-shaped object, for the purpose of explaining the present invention in detail.
Fig. 4 is a diagram showing a comparison of the measurement profile and the extraction profiles in the low, high, and medium frequency ranges. Diagram shown. FIG. 5 is a flowchart showing the flow of the method for detecting abnormalities in a strip-shaped object according to the present invention, and FIG. 6 is a measurement profile, frequency components, and extraction profile at the first point in the longitudinal direction of the strip-shaped object. Diagram comparing and showing the 7th and 1st
2 is a diagram showing a comparison of a measurement profile, a frequency component, and an extraction profile at a second point in the longitudinal direction of a band-shaped object. The fgB diagram is a diagram showing the same minimum extraction profile, and FIG. 9 is a diagram showing an abnormal part in the thickness profile of a strip steel plate during hot rolling, in which the method for detecting an abnormal part in a strip profile according to the present invention is adopted. FIG. 10 is a block diagram including a partial view showing the configuration of an embodiment of the detection device.
11 is a diagram showing a comparison of the measurement profile, extraction profile, and minimum extraction profile at the first point and second point in the longitudinal direction in a practical example; FIG. lO...Strip-shaped object, 12...Strip-shaped steel plate, 14...
X-ray plate thickness gauge, 1G...Digital 1" calculator, 18...
Cathode ray tube display. “Yo”j! Hito Takaya Ron (1 other person) (D) D Figure 2 Figure 5 Figure 8 Figure 9 Q Figure 10

Claims (1)

[Claims] (1) In a method for detecting abnormalities in a profile of a strip-shaped object to detect abnormalities in a profile obtained by measuring physical quantities of a strip-shaped object at each point in the width direction, the measured values at each point in the width direction are subjected to Fourier transformation. An extraction profile obtained by decomposing each frequency component and then inversely transforming the middle frequency range of the frequency component by inverse Fourier transform is obtained for each point in the longitudinal direction of the belt-shaped object. Next, a minimum extraction profile consisting of the minimum value of each point in the width direction of the extraction profile is obtained, and a point where the minimum extraction profile is not included in an allowable range is determined to be an abnormal part. An abnormal part detection method.