JP2013010110A - Method for detecting chattering of cold rolling mill - Google Patents

Method for detecting chattering of cold rolling mill Download PDF

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JP2013010110A
JP2013010110A JP2011142938A JP2011142938A JP2013010110A JP 2013010110 A JP2013010110 A JP 2013010110A JP 2011142938 A JP2011142938 A JP 2011142938A JP 2011142938 A JP2011142938 A JP 2011142938A JP 2013010110 A JP2013010110 A JP 2013010110A
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chattering
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pattern recognition
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JP5799611B2 (en
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Yusuke Takemura
祐輔 竹村
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JFE Steel Corp
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Abstract

PROBLEM TO BE SOLVED: To detect chattering vibrations generated only by a rolling state by distinguishing between chattering caused by a mechanical state and chattering caused by the rolling state.SOLUTION: The method for detecting the chattering includes the step of analyzing the shape of a FFT transformed frequency wave form by means of a pattern recognition among mill vibrations generated during rolling in a tandem rolling mill which performs cold rolling, when the shape of the analyzed frequency wave form is determined to be a predetermined triangle shape whose frequency component which becomes a peak value of frequency intensity is a vertex, it is detected as the chattering vibrations generated caused by the rolling state (slip, stick).

Description

本発明は、冷間圧延機のチャタリング検出方法に関する。   The present invention relates to a chattering detection method for a cold rolling mill.

鋼板の圧延時にミル振動によってチャタリングと呼ばれる板厚変動が発生するケースがある。チャタリングの原因は、圧延ロールチョックやベアリングの劣化により、回転部分に亀裂が入り、それによってチョックやベアリングの固有振動が発生する場合(以下、「機械状態起因」ともいう)と、圧延条件である摩擦係数や鋼板張力が安定領域から外れ、中立点がロールバイトの外に移動してしまうスリップ(またはスティック)によって引き起こされる縦振動が発生する場合(以下、「圧延状態起因」ともいう)の2種類がある。圧延状態起因のチャタリングが生じると、鋼板に縞状の模様が生じる製品不良の原因となる。そのため、圧延状態起因のチャタリングを確実に検出し、製品不良を防止することが重要である。   There is a case where a plate thickness variation called chattering occurs due to mill vibration during rolling of a steel plate. Chattering is caused by the rolling roll chock or bearing deterioration, which causes cracks in the rotating part, which causes natural vibrations of the chock or bearing (hereinafter also referred to as “mechanical state”), and friction that is the rolling condition. Two types when longitudinal vibration caused by slip (or stick) in which the coefficient or steel plate tension is out of the stable region and the neutral point moves out of the roll bite (hereinafter also referred to as “rolling state”) There is. When chattering due to the rolling state occurs, it causes a product defect in which a striped pattern is formed on the steel plate. Therefore, it is important to reliably detect chattering caused by the rolling state and prevent product defects.

ここで、従来の技術では、ミル振動時に発生する音の波形と振動センサーから検出される振動波形の両方にチャタリング固有の波形成分が存在したときにのみチャタリングとして検出する方法(特許文献1参照)や、ワークロールのロール周速と鋼板の板速度を測定し、スリップを検知してチャタリングを検出する方法(特許文献2参照)や、圧延されている鋼板の長手方向に2つ以上の板厚計を設置し、各々が測定した板厚の差が予め設定されている設定値以上となった場合にチャタリングとして検出する方法(特許文献3参照)等が提案されている。また、ミルハウジングなどに振動センサーを取り付け、その信号に対してFFT変換をした後に処理を行って、チャタリングを検出する方法(特許文献4ないし9参照)が提案されている。   Here, in the prior art, a method for detecting chattering only when a chattering-specific waveform component exists in both the waveform of the sound generated during mill vibration and the vibration waveform detected by the vibration sensor (see Patent Document 1). Or a method of measuring the roll peripheral speed of the work roll and the plate speed of the steel plate, detecting slip to detect chattering (see Patent Document 2), or two or more plate thicknesses in the longitudinal direction of the steel plate being rolled. A method has been proposed in which a meter is installed and detected as chattering when the difference in thickness measured by each of the meters exceeds a preset value (see Patent Document 3). Further, a method has been proposed in which chattering is detected by attaching a vibration sensor to a mill housing or the like and performing processing after performing FFT conversion on the signal (see Patent Documents 4 to 9).

特開2000−158044号公報JP 2000-158044 A 特開平8−24922号公報JP-A-8-24922 特公平5−87325号公報Japanese Patent Publication No. 5-87325 特公平4−46650号公報Japanese Examined Patent Publication No. 4-46650 特公平6−35004号公報Japanese Patent Publication No. 6-3504 特開平8−141612号公報JP-A-8-141612 特公昭54−38912号公報Japanese Patent Publication No.54-38912 特開平8−29250号公報JP-A-8-29250 特開平8−108205号公報JP-A-8-108205

しかしながら、特許文献1に開示される、音の波形と振動センサーからの振動波形によってチャタリングを検出する方法では、クレーン走行音などミルの周囲で発生する様々な音とミル振動時に発する音の分離ができない場合にはチャタリングの検出ができないという問題がある。
また、特許文献2に記載の、ロール周速と板速度とを比較する方法では、圧延時に発生するヒュームなどにより、板速計を使用することができない場合にはチャタリングの検出をすることができない。
However, in the method disclosed in Patent Document 1 for detecting chattering based on the sound waveform and the vibration waveform from the vibration sensor, various sounds generated around the mill such as crane traveling sound and sound generated during the mill vibration are separated. If this is not possible, there is a problem that chattering cannot be detected.
Further, in the method of comparing the roll peripheral speed and the plate speed described in Patent Document 2, chattering cannot be detected when the plate speed meter cannot be used due to fumes generated during rolling. .

また、特許文献3に記載の、板厚計を2つ以上設置する方法は、板厚計を複数設置することで設備コストが増大するという問題がある。また、圧延状態起因の場合のチャタリングで発生する板厚変動の周期は100〜250Hz程度であり、板厚計の応答速度と比較して高周波なので、高速圧延中に板厚計で板厚変動を測定することが困難であるため、チャタリングの検出が難しい。   Moreover, the method of installing two or more thickness gauges described in Patent Document 3 has a problem that the equipment cost increases by installing a plurality of thickness gauges. In addition, the period of the plate thickness variation that occurs due to chattering due to the rolling state is about 100 to 250 Hz, which is a high frequency compared with the response speed of the plate thickness meter. Chattering is difficult to detect because it is difficult to measure.

また、ミルハウジングなどに振動センサーを取り付ける方法において、特許文献4ないし7に記載の技術のように、圧延機の固有振動数のみを通過させるフィルタを取り付ける場合、チャタリングの周波数成分とされる周波数帯域に、圧延駆動系の振動等の周波数成分が含まれているとチャタリングとして誤検出をしてしまうという問題がある。
また、特許文献8記載の技術のように、ミルの直近に2段ロールを設け、その2段ロールに振動センサーを取り付けてその振動を測定する場合、新たに2段ロールを設置しなければならず、設置する場所のスペース確保の問題や、コストアップの問題がある。
In addition, in the method of attaching a vibration sensor to a mill housing or the like, when attaching a filter that allows only the natural frequency of a rolling mill to pass as in the techniques described in Patent Documents 4 to 7, the frequency band used as the frequency component of chattering In addition, if a frequency component such as vibration of the rolling drive system is included, there is a problem that false detection occurs as chattering.
Also, as in the technique described in Patent Document 8, when a two-stage roll is provided in the immediate vicinity of the mill and a vibration sensor is attached to the two-stage roll and the vibration is measured, a new two-stage roll must be installed. In other words, there are problems of securing the space of the installation place and cost increase.

また、特許文献9記載の技術のように、圧延状態に基づいて基本周波数を計算し、実際の振動をFFT変換した結果が、基本周波数の整数倍の周波数成分が設定値を超えたときにチャタリングとして検出する場合には、求めた基本周波数以外の周波数で発生するチャタリングを見逃してしまうという問題や、チャタリングに至らない振動の誤検出をしてしまうという問題が挙げられる。   In addition, as in the technique described in Patent Document 9, the fundamental frequency is calculated based on the rolling state, and the actual vibration is subjected to FFT conversion. When the frequency component that is an integral multiple of the fundamental frequency exceeds the set value, chattering occurs. In such a case, there are a problem that chattering that occurs at a frequency other than the obtained fundamental frequency is missed, and a problem that vibration that does not lead to chattering is erroneously detected.

そこで、本発明は、このような問題点に着目してなされたものであって、ミルの周囲で発生する様々な音の影響を受けることなく、また、板速計やフィルタの取り付けが不要であり、設置にも特段のスペース確保が不要であり、設備コストを比較的に少なくしつつも、冷間圧延を行うタンデム圧延機において圧延中に発生するミル振動のうち、圧延状態に起因して発生するチャタリングの振動のみを検出し得る冷間圧延機のチャタリング検出方法を提供することを目的としている。   Therefore, the present invention has been made paying attention to such problems, and is not affected by various sounds generated around the mill, and does not require the installation of a plate speedometer or a filter. There is no need to secure a special space for installation, and while the equipment cost is relatively low, among the mill vibrations that occur during rolling in tandem rolling mills that perform cold rolling, due to the rolling state It is an object of the present invention to provide a chattering detection method for a cold rolling mill that can detect only chattering vibrations that occur.

本発明者は、ミル振動信号を解析するにあたり、チョックやベアリングの劣化などの機械状態起因の振動とスリップなどの圧延状態起因の振動との間でそれぞれFFT変換した周波数波形に特性の違いがあることに着目した。
すなわち、この周波数波形の特性の違いとして、機械状態起因のチャタリングの場合には、図1に示すように、周波数強度のピーク値が特定の周波数成分が突出した形になる。これに対し、圧延状態起因の場合には、図2に示すように、周波数強度のピーク値となる周波数成分を頂点とした三角形の形を呈する。この理由としては、機械状態起因の場合は、チョックやベアリングの劣化によるひびや傷が回転によって固有振動を生むために周波数に広がりを持たないと考えられる。これに対し、圧延状態起因の場合は、スリップまたはスティックによって、スリップ、スティックが連続して起こる現象が発生し、それによってミルの固有振動が引き起こされるために非線形的な振動となって、周波数に広がりを持つ三角形の波形になると考えられる。つまり、チャタリングを引き起こすミル振動は、FFT変換した周波数波形の形に着目することで他の振動と判別することが可能である。
When analyzing the mill vibration signal, the inventor has a difference in characteristics in frequency waveforms obtained by FFT conversion between vibration caused by a mechanical state such as chock and bearing deterioration and vibration caused by a rolling state such as slip. Focused on that.
That is, as the difference in the characteristics of the frequency waveform, in the case of chattering caused by the machine state, as shown in FIG. 1, the peak value of the frequency intensity has a shape in which a specific frequency component protrudes. On the other hand, in the case of the rolling state, as shown in FIG. 2, it has a triangular shape with the frequency component serving as the peak value of the frequency intensity as a vertex. The reason for this is that, in the case of the mechanical state, cracks and scratches due to deterioration of the chock and the bearing generate natural vibrations due to rotation, so that the frequency does not spread. On the other hand, in the case of the rolling state, the slip or stick causes a phenomenon in which the slip and stick occur continuously, thereby causing the natural vibration of the mill, resulting in nonlinear vibration, It is thought to be a triangular waveform with a spread. That is, mill vibration that causes chattering can be distinguished from other vibrations by paying attention to the shape of the frequency waveform that has been subjected to FFT conversion.

これらのことから、ミル振動をFFT変換した周波数波形の形に対し、チャタリングを引き起こすミル振動のFFT変換した周波数波形の形との違いを定量的に表すことができれば、圧延状態起因のチャタリングを引き起こすミル振動の判別が可能となる。ここで、各々の図形の違いついて定量的に表す手法としては、パターン認識の手法が好適であり、本発明ではこのパターン認識の手法を用いて、圧延状態起因のチャタリングを引き起こすミル振動の判別を行うものである。   From these facts, if the difference between the shape of the frequency waveform obtained by FFT-transforming mill vibration and the shape of the frequency waveform obtained by FFT-transforming mill vibration can be expressed quantitatively, chattering caused by the rolling state is caused. Mill vibration can be identified. Here, a pattern recognition method is suitable as a method for quantitatively expressing the difference between each figure, and in the present invention, this pattern recognition method is used to discriminate mill vibration that causes chattering due to the rolling state. Is what you do.

すなわち、上記課題を解決するために、本発明は、冷間圧延を行うタンデム圧延機において圧延中に発生するミル振動のうち、FFT変換をした周波数波形の形をパターン認識の手法で解析し、この解析した周波数波形の形が、周波数強度のピーク値となる周波数成分を頂点とした所定の三角形状に対応する範囲のものであると判定されたときに、圧延状態に起因して発生するチャタリングの振動として検出することを特徴とする。   That is, in order to solve the above-mentioned problem, the present invention analyzes the shape of the frequency waveform subjected to FFT transformation among mill vibrations generated during rolling in a tandem rolling mill that performs cold rolling, and uses a pattern recognition technique. Chattering that occurs due to the rolling state when it is determined that the shape of the analyzed frequency waveform is in a range corresponding to a predetermined triangular shape having a peak at the frequency component that is the peak value of the frequency intensity. It is characterized by detecting as vibrations.

ここで、本発明に係る冷間圧延機のチャタリング検出方法において、前記パターン認識の手法は、周波数強度が所定のしきい値を超える波形をチャタリング可能性のある波形として抽出する工程と、その抽出した周波数波形に対してマハラノビス距離に基づくパターン認識の手法を用いて、チャタリング判別を行う工程とを含むとともに、更に、前記チャタリング判別を行う工程は、チャタリングが発生する周波数に対して所定に区分した周波数ごとの最大値のうち、所定のしきい値よりも高い点を候補点として抽出する工程と、その抽出した候補点のうちその点を中心とした所定幅の周波数以内で最大である場合に、当該候補点をチャタリング可能性のある波形の頂点として設定する工程と、その設定した頂点の強度を高さとするとともに所定周波数の幅を底辺の長さとする判別用の三角形を前記所定の三角形状として設定する工程と、この設定した三角形と前記チャタリング可能性のある波形に対してマハラノビス距離を適用したパターン認識により形状の差を定量的に求める工程とを含むことは好ましい。   Here, in the chattering detection method of the cold rolling mill according to the present invention, the pattern recognition method includes a step of extracting a waveform having a frequency intensity exceeding a predetermined threshold as a waveform having a possibility of chattering, and the extraction thereof. And a step of performing chattering discrimination using a pattern recognition technique based on the Mahalanobis distance with respect to the frequency waveform, and further, the step of performing chattering discrimination is divided into predetermined frequencies for the frequency at which chattering occurs. Of the maximum values for each frequency, a step of extracting a point higher than a predetermined threshold as a candidate point, and the extracted candidate points that are maximum within a predetermined frequency centered on that point , Setting the candidate point as a vertex of a waveform with a possibility of chattering, and increasing the strength of the set vertex A step of setting a triangle for determination having a constant frequency width as a base length as the predetermined triangle shape, and a pattern recognition applying a Mahalanobis distance to the set triangle and the waveform having the possibility of chattering. Including a step of quantitatively determining the difference between the two.

本発明によれば、ミル振動をFFT変換した周波数波形の形に対し、パターン認識の手法を用いて解析し、この解析した周波数波形の形が、周波数強度のピーク値となる周波数成分を頂点とした所定の三角形状に対応する範囲のものであると判定されたときに、圧延状態に起因して発生するチャタリングの振動として検出するので、圧延状態起因のチャタリングを引き起こすミル振動のみを判別することができる。   According to the present invention, the shape of the frequency waveform obtained by FFT-transforming the mill vibration is analyzed by using a pattern recognition technique, and the frequency waveform thus analyzed has a frequency component having a peak value of the frequency intensity as a vertex. Since it is detected as chattering vibration that occurs due to the rolling state when it is determined that it is within the range corresponding to the predetermined triangular shape, only mill vibration that causes chattering due to the rolling state is discriminated. Can do.

したがって、ミルの周囲で発生する様々な音の影響を受けることなく、また、板速計やフィルタの取り付けが不要であり、設置にも特段のスペース確保が不要であり、設備コストを比較的に少なくしつつも、冷間圧延を行うタンデム圧延機において圧延中に発生するミル振動のうち、圧延状態に起因して発生するチャタリングの振動のみを検出可能であり、また、板厚変動を起こさないような小さな強度の圧延状態起因のミル振動の検出も可能となることから、チャタリング発生を防ぐ対策を行うことができるという効果もある。   Therefore, it is not affected by various sounds generated around the mill, and there is no need to install a plate speedometer or filter, and it is not necessary to secure a special space for installation. It is possible to detect only chattering vibrations that occur due to the rolling state among mill vibrations that occur during rolling in tandem rolling mills that perform cold rolling. Since it is also possible to detect mill vibration due to such a low strength rolling state, there is an effect that it is possible to take measures to prevent chattering.

機械状態起因におけるチャタリングの周波数波形のイメージを示す図である。It is a figure which shows the image of the frequency waveform of chattering resulting from a machine state. 圧延状態起因におけるチャタリングの周波数波形のイメージを示す図である。It is a figure which shows the image of the frequency waveform of chattering resulting from a rolling state. 本発明で用いられるチャタリング検出システムの概略図である。It is the schematic of the chattering detection system used by this invention. 本発明に係るチャタリング検出処理を説明するフローチャートである。It is a flowchart explaining the chattering detection process which concerns on this invention. 本発明に係るパターン認識の一例を説明する図である。It is a figure explaining an example of pattern recognition concerning the present invention. 本発明に係るパターン認識の一例を説明する図である。It is a figure explaining an example of pattern recognition concerning the present invention. 機械状態起因におけるチャタリングの周波数波形の一例を示す図である。It is a figure which shows an example of the frequency waveform of chattering resulting from a machine state. 圧延状態起因におけるチャタリングの周波数波形の一例を示す図である。It is a figure which shows an example of the frequency waveform of chattering resulting from a rolling state.

以下、本発明の一実施形態について、図面を適宜参照しつつ説明する。
図3にチャタリング検出システムを示す。同図に示す符号10は圧延機であり、同図に示すように、この圧延機10は、鋼板Pを圧延する圧延ミル1を有するミルハウンジング3を備えて構成されている。そして、チャタリング検出システム20は、この圧延機10のミルハウンジング3に取り付けた振動センサー4により、振動加速度を計測するようになっている。なお、この振動センサー4の出力の計測周期は1500〜3000Hz程度である。そして、この振動センサー4により得られた振動加速度の信号がFFT変換器6に入力され、FFT変換器6でFFT変換された周波数波形は演算処理器7に送られるようになっている。この例では、FFT変換器6は、振動加速度で得られた信号に対して1秒周期でFFT変換を実施する。そして、演算処理器7は、FFT変換された周波数波形に対して、パターン認識の手法を用いて解析を行う。パターンの認識の手法としては、マハラノビス距離、ニューラルネットワーク、主成分分析など様々な方法が知られているが手法自体は特には問わないが、本実施形態の例では、マハラノビス距離に基づくパターン認識の手法を用いたチャタリング検出処理を実行する例である。以下、このチャタリング検出処理について詳しく説明する。
Hereinafter, an embodiment of the present invention will be described with reference to the drawings as appropriate.
FIG. 3 shows a chattering detection system. Reference numeral 10 shown in the figure is a rolling mill, and as shown in the figure, the rolling mill 10 includes a mill housing 3 having a rolling mill 1 for rolling a steel plate P. The chattering detection system 20 measures the vibration acceleration by the vibration sensor 4 attached to the mill housing 3 of the rolling mill 10. The measurement cycle of the output of the vibration sensor 4 is about 1500 to 3000 Hz. The vibration acceleration signal obtained by the vibration sensor 4 is input to the FFT converter 6, and the frequency waveform subjected to the FFT conversion by the FFT converter 6 is sent to the arithmetic processor 7. In this example, the FFT converter 6 performs FFT conversion on a signal obtained by vibration acceleration at a cycle of 1 second. Then, the arithmetic processor 7 analyzes the FFT-converted frequency waveform using a pattern recognition method. Various methods such as Mahalanobis distance, neural network, and principal component analysis are known as pattern recognition methods, but the method itself is not particularly limited. In the example of this embodiment, pattern recognition based on Mahalanobis distance is performed. It is an example which performs the chattering detection process using the method. Hereinafter, the chattering detection process will be described in detail.

演算処理器7でチャタリング検出処理が実行されると、図4に示すように、まずステップS1に移行して、周波数強度が所定のしきい値を超える波形をチャタリング可能性のある波形として抽出する。そして、続くステップS2では、ステップS1で抽出した周波数波形に対してマハラノビス距離に基づくパターン認識の手法を用いて、チャタリング判別処理を行う。   When the chattering detection process is executed by the arithmetic processor 7, as shown in FIG. 4, first, the process proceeds to step S1, and a waveform having a frequency intensity exceeding a predetermined threshold is extracted as a waveform with a possibility of chattering. . In subsequent step S2, chattering determination processing is performed on the frequency waveform extracted in step S1 using a pattern recognition method based on the Mahalanobis distance.

次に、このチャタリング判別処理の詳細を述べる。図5はチャタリングの可能性があるとして抽出された周波数波形(実線で示す)Kと、マハラノビス距離に基づくパターン認識の手法で用いる三角形(波線で示す)Dの例とを示している。
上記チャタリング判別処理が演算処理器7で実行されると、図4(b)に示すように、まずステップS11に移行して、チャタリングが発生する周波数である50〜300Hzに対し、図5に示すように、10Hzごとの最大値のうち、しきい値Tよりも高い点を候補点として抽出する。これによりチャタリング可能性のある波形の頂点の候補点A,B,Cが抽出される。続くステップS12では、ステップS11で抽出した候補点A,B,Cがその点を中心とした±20Hz以内で最大である場合、チャタリング可能性のある波形の頂点とする。従ってこの例では、候補点Aが頂点として設定される。続くステップS13では、設定した頂点Aの強度を高さとするとともに50Hzを底辺の長さとする判別用の三角形D(斜辺がD1,D2)を判別用波形として設定する。最後にステップS14に移行してパターン認識処理を実行する。このパターン認識処理は、ステップS13で設定した三角形Dとチャタリング可能性のある波形Kに対してマハラノビス距離を適用したパターン認識処理により形状の差を定量的に求めるものである。
Next, details of the chattering determination processing will be described. FIG. 5 shows an example of a frequency waveform (shown by a solid line) K extracted as a possibility of chattering and a triangle (shown by a wavy line) D used in a pattern recognition technique based on the Mahalanobis distance.
When the above-described chattering determination process is executed by the arithmetic processor 7, as shown in FIG. 4B, first, the process proceeds to step S11, and the frequency at which chattering occurs is 50 to 300 Hz. Thus, a point higher than the threshold value T is extracted as a candidate point from the maximum value every 10 Hz. As a result, candidate points A, B, and C of the vertices of the waveform having the possibility of chattering are extracted. In the subsequent step S12, if the candidate points A, B, and C extracted in step S11 are the maximum within ± 20 Hz with the point as the center, they are set as the vertices of a waveform that may be chattered. Therefore, in this example, candidate point A is set as the vertex. In subsequent step S13, a discrimination triangle D (having hypotenuses of D1 and D2) having the height of the set vertex A and a base length of 50 Hz is set as a discrimination waveform. Finally, the process proceeds to step S14 to execute pattern recognition processing. In this pattern recognition process, the difference in shape is quantitatively obtained by the pattern recognition process in which the Mahalanobis distance is applied to the triangle D set in step S13 and the waveform K having the possibility of chattering.

マハラノビス距離を適用したパターン認識処理について図6を参照して説明する。このパターン認識処理が実行されると、図4(c)に示すように、まずステップS21に移行して、図6に示す、チャタリングの可能性がある波形Kの頂点Aの周波数をαとし、頂点の周波数αから離れた所定範囲(点数)を定め、ここでは、例えばその波形Kの(α−15)Hzから(α−8)Hzまでの範囲8点の集合A1(図6(b)参照)を処理対象集合A1として抽出する。続くステップS22では、ステップS21で抽出した処理対象集合A1のx成分(周波数),y成分(周波数強度)の平均、分散、および共分散をそれぞれ求める。そして、ステップS23では、ステップS22で求めた平均、分散、および共分散を使って、周波数(α−15)Hzから(α−8)Hzまでに対応する三角形D(斜辺D1)の各点(a1i(図6の符号Gm),1≦i≦8)に対してマハラノビス距離を求め、その合計を算出する。以下に(式1)で示す。   A pattern recognition process using the Mahalanobis distance will be described with reference to FIG. When this pattern recognition process is executed, as shown in FIG. 4 (c), the process first proceeds to step S21, where the frequency of the vertex A of the waveform K having the possibility of chattering shown in FIG. A predetermined range (number of points) apart from the apex frequency α is determined. Here, for example, a set A1 of 8 ranges of the waveform K from (α−15) Hz to (α−8) Hz (FIG. 6B). Reference) is extracted as the processing target set A1. In the subsequent step S22, the average, variance, and covariance of the x component (frequency) and y component (frequency intensity) of the processing target set A1 extracted in step S21 are obtained. In step S23, using the average, variance, and covariance obtained in step S22, each point (triangle H1) of the triangle D (hypotenuse D1) corresponding to the frequency (α-15) Hz to (α-8) Hz ( The Mahalanobis distance is calculated for a1i (reference symbol Gm in FIG. 6), 1 ≦ i ≦ 8, and the total is calculated. This is shown by (Formula 1) below.

Figure 2013010110
Figure 2013010110

但し、μ1x:処理対象集合A1のx成分の平均、
μ1y:処理対象集合A1のy成分の平均、
σ1x:処理対象集合A1のx成分の分散、
σ1y:処理対象集合A1のy成分の分散、
σ1xy:処理対象集合A1のx成分とy成分の共分散、
a1ix:a1iのx成分(図6の符号Ga)、
a1iy:a1iのy成分(図6の符号R)、
Where μ1x: average of x components of the processing target set A1;
μ1y: average y component of the processing target set A1,
σ1x: variance of the x component of the processing target set A1,
σ1y: variance of the y component of the processing target set A1,
σ1xy: covariance of x component and y component of the processing target set A1;
a1ix: x component of a1i (symbol Ga in FIG. 6),
a1iy: y component of a1i (symbol R in FIG. 6),

続くステップS24では、他の処理対象集合の有無を確認し、次の処理対象集合があればステップS22に処理を戻し、そうでなければステップS25に移行する。つまり、上記の計算を(α−14)Hzから(α−7)Hzまでの範囲の処理対象集合A2から(α−11)Hzから(α−4)Hzまでの範囲の処理対象集合A5までと、(α+4)Hzから(α+11)Hzまでの範囲の処理対象集合A6から(α+8)Hzから(α+15)Hzまでの範囲の処理対象集合A10まで順次に行う。
そしてステップS25では、上記によって得られたこれらの値の平均を相関値Dとして計算する。これを式で表すと以下の(式2)に示すようになる。
In subsequent step S24, the presence or absence of another processing target set is confirmed. If there is a next processing target set, the process returns to step S22, and if not, the process proceeds to step S25. That is, the above calculation is performed from the processing target set A2 in the range from (α-14) Hz to (α-7) Hz to the processing target set A5 in the range from (α-11) Hz to (α-4) Hz. Then, processing is sequentially performed from the processing target set A6 in the range from (α + 4) Hz to (α + 11) Hz to the processing target set A10 in the range from (α + 8) Hz to (α + 15) Hz.
In step S25, the average of these values obtained as described above is calculated as the correlation value D. This is expressed by the following (Expression 2).

Figure 2013010110
Figure 2013010110

続くステップS26では、(式2)により算出された、チャタリングの可能性がある波形Kの相関値Dと予め設定された判別値とを比較して、相関値Dが所定の判別値未満であればステップS27に移行して圧延状態起因と判定して処理を戻す。つまり、解析の対象とされた周波数波形Kの形が、周波数強度のピーク値となる周波数成分を頂点とした所定の三角形Dに対応する範囲のものであると判定されたときに、圧延状態に起因して発生するチャタリングの振動として検出される。これに対し、相関値Dが所定の判別値以上であればステップS28に移行して、機械状態起因と判定して処理を戻す。   In the subsequent step S26, the correlation value D of the waveform K having the possibility of chattering calculated by (Equation 2) is compared with a preset discrimination value, and the correlation value D is less than the predetermined discrimination value. If it moves to step S27, it will determine with a rolling state origin and will return a process. That is, when it is determined that the shape of the frequency waveform K to be analyzed is in a range corresponding to a predetermined triangle D having a peak at the frequency component that is the peak value of the frequency intensity, It is detected as chattering vibrations caused by this. On the other hand, if the correlation value D is equal to or greater than the predetermined determination value, the process proceeds to step S28, where it is determined that the machine state is caused and the process is returned.

ここで、相関値Dと比較される所定の判別値は、表1に示すように、予めマハラノビス距離を適用したパターン認識の方法によるデータを集め、このデータから得られた相関値に対し、実際のチャタリングの内容が圧延状態起因か機械状態起因かを判断した結果に基づいて決定した。   Here, as shown in Table 1, the predetermined discriminant value to be compared with the correlation value D is obtained by collecting data according to a pattern recognition method to which the Mahalanobis distance is applied in advance, and the correlation value obtained from this data is actually It was determined based on the result of judging whether the chattering content was due to the rolling state or the mechanical state.

Figure 2013010110
Figure 2013010110

つまり、表1に示すデータにおいて、実際の確認結果によれば、No.1〜10は圧延状態起因のチャタリングであったが、No.11〜22は機械状態起因のチャタリングであった。この結果から、機械状態起因のチャタリングでは、相関値Dが31.0(No.18)が最小であったことから、本実施例では、所定の判別値を30として相関値Dと比較することとした。   That is, in the data shown in Table 1, according to the actual confirmation result, No. Nos. 1 to 10 were chattering due to the rolling state. 11 to 22 were chattering caused by the machine state. From this result, since the correlation value D is 31.0 (No. 18) in chattering caused by the machine state, the correlation value D is compared with the correlation value D by setting the predetermined discrimination value to 30 in this embodiment. It was.

本実施例としては、チャタリングの可能性のある周波数波形Kとして抽出できた図7と図8の波形に対して上記マハラノビス距離を適用したパターン認識処理を実行した。その結果、図8のチャタリング波形では相関値D=11.7(<30)だったのに対し、図7の過検出波形では相関値D=84.7(>30)であり、機械状態起因のチャタリングと圧延状態起因のチャタリングとを区別して圧延状態に起因して発生するチャタリングの振動のみを検出することができた。   In this embodiment, pattern recognition processing using the Mahalanobis distance is performed on the waveforms of FIGS. 7 and 8 that can be extracted as a frequency waveform K with the possibility of chattering. As a result, the correlation value D = 11.7 (<30) in the chattering waveform of FIG. 8, whereas the correlation value D = 84.7 (> 30) in the overdetection waveform of FIG. It was possible to detect only chattering vibration caused by the rolling state by distinguishing between chattering and chattering caused by the rolling state.

1 圧延ミル
2 被圧延材
3 ミルハウンジング
4 振動センサー
5 振動増幅器
6 FFT変換器
7 演算処理器
P 鋼板
DESCRIPTION OF SYMBOLS 1 Rolling mill 2 Rolling material 3 Mill housing 4 Vibration sensor 5 Vibration amplifier 6 FFT converter 7 Arithmetic processor P Steel plate

Claims (2)

冷間圧延を行うタンデム圧延機において圧延中に発生するミル振動のうち、FFT変換をした周波数波形の形をパターン認識の手法で解析し、この解析した周波数波形の形が、周波数強度のピーク値となる周波数成分を頂点とした所定の三角形状に対応する範囲のものであると判定されたときに、圧延状態に起因して発生するチャタリングの振動として検出することを特徴とする冷間圧延機のチャタリング検出方法。   Of the mill vibrations that occur during rolling in a tandem rolling mill that performs cold rolling, the shape of the frequency waveform that has been subjected to FFT transformation is analyzed using a pattern recognition technique, and the shape of the analyzed frequency waveform is the peak value of the frequency intensity. A cold rolling mill characterized in that it is detected as chattering vibration generated due to a rolling state when it is determined that the frequency component is in a range corresponding to a predetermined triangular shape having a frequency component as a vertex. Chattering detection method. 前記パターン認識の手法は、周波数強度が所定のしきい値を超える波形をチャタリング可能性のある波形として抽出する工程と、その抽出した周波数波形に対してマハラノビス距離に基づくパターン認識の手法を用いて、チャタリング判別を行う工程とを含むとともに、
更に、前記チャタリング判別を行う工程は、チャタリングが発生する周波数に対して所定に区分した周波数ごとの最大値のうち、所定のしきい値よりも高い点を候補点として抽出する工程と、その抽出した候補点のうちその点を中心とした所定幅の周波数以内で最大である場合に、当該候補点をチャタリング可能性のある波形の頂点として設定する工程と、その設定した頂点の強度を高さとするとともに所定周波数の幅を底辺の長さとする判別用の三角形を前記所定の三角形状として設定する工程と、この設定した三角形と前記チャタリング可能性のある波形に対してマハラノビス距離を適用したパターン認識により形状の差を定量的に求める工程とを含むことを特徴とする請求項1に記載の冷間圧延機のチャタリング検出方法。
The pattern recognition method uses a step of extracting a waveform whose frequency intensity exceeds a predetermined threshold as a waveform having a possibility of chattering, and a pattern recognition method based on the Mahalanobis distance for the extracted frequency waveform. And a step of performing chattering discrimination,
Furthermore, the step of performing the chattering determination includes a step of extracting a point higher than a predetermined threshold as a candidate point out of the maximum values for each frequency that are predetermined for the frequency at which chattering occurs, and the extraction thereof. If the candidate point is the maximum within a frequency of a predetermined width centered on that point, the step of setting the candidate point as a vertex of a waveform that may be chattered, and the strength of the set vertex as height And a step of setting a discrimination triangle having a predetermined frequency width as a base length as the predetermined triangle shape, and pattern recognition applying a Mahalanobis distance to the set triangle and the waveform having the possibility of chattering The method for detecting chattering of a cold rolling mill according to claim 1, further comprising the step of quantitatively determining the difference in shape.
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WO2022209295A1 (en) * 2021-03-31 2022-10-06 Jfeスチール株式会社 Abnormal vibration detection method for rolling mill, abnormality detection device, rolling method, and method for manufacturing metal strip
JP7103550B1 (en) * 2021-03-31 2022-07-20 Jfeスチール株式会社 Abnormal vibration detection method of rolling mill, abnormality detection device, rolling method and metal strip manufacturing method
EP4282551A4 (en) * 2021-03-31 2024-07-10 Jfe Steel Corp Abnormal vibration detection method for rolling mill, abnormality detection device, rolling method, and method for manufacturing metal strip

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