JPH11347614A - Method and device for abnormality diagnosis - Google Patents

Abstract

PROBLEM TO BE SOLVED: To diagnose an abnormal plate thickness under rolling by calculating a deviation between the plate thickness of a rolling material t be rolled and a target plate thickness and recognizing the abnormal plate thickness when the deviation exceeds a reference value. SOLUTION: A control device (measuring means) 2 measures the plate thickness of a rolling material. A monitor device (calculating means, recognizing means) 3 calculates a deviation between an actual plate thickness and a target plate thickness and the deviation is compared to a discrimination reference value of a plate thickness abnormality. In the cast that the deviation exceeds the discrimination reference value, it is recognized as the plate thickness abnormal. It is desirable to detect a local min value and local max value of the plate thickness. A cause of the abnormality is desirably assumed by applying a cause assumption rule. As the cause assumption rule, an abnormal stand is assumed from an actual louver angle, an actual speed correction value for a stand mill, an actual torque and an actual rolling load of a mill motor etc. For example, a speed set value of a mill stand is corrected from the actual louver angle or a forwarding rate set value is corrected from the speed correction value.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an abnormality diagnosis apparatus and an abnormality diagnosis method for diagnosing an abnormal thickness of a rolled material.

[0002]

2. Description of the Related Art Conventionally, in a hot finishing mill, various sheet thickness control methods have been developed and used in order to realize a highly accurate product sheet thickness. Recently, a plurality of sheet thickness control methods have been used for a rolling mill at the same time.
However, as described in Japanese Patent Application Laid-Open No. 9-323109, an abnormality diagnosis device for diagnosing an abnormality in a rolling mill itself has been developed, but an abnormality diagnosis device for diagnosing an abnormality in the thickness of a rolled material has been developed. Therefore, it was not possible to automatically determine whether or not the thickness control was functioning normally during the rolling. That is, the method of judging whether or not the thickness control is functioning normally uses a method of printing out a rolling result (an analog chart) or the like after the end of the rolling and making a comparison with a target thickness by an operator. As a result, it was not possible to automatically judge an abnormal thickness.

[0003]

Since the conventional abnormality diagnosis apparatus is configured as described above, the abnormality of the rolling mill itself can be diagnosed, but the thickness control is normally performed during the rolling operation. Since it is not possible to diagnose whether or not the rolling is performed, there is a problem that even if a thickness error occurs during rolling, the control amount of the thickness cannot be adjusted.

SUMMARY OF THE INVENTION The present invention has been made to solve the above-described problems, and an object of the present invention is to provide an abnormality diagnosis apparatus and an abnormality diagnosis method capable of diagnosing an abnormal thickness during rolling.

[0005]

SUMMARY OF THE INVENTION An abnormality diagnosis apparatus according to the present invention calculates a deviation between the thickness of a rolled material rolled by a rolling mill and a target thickness, and when the deviation exceeds a reference value, an abnormal thickness. Is to be certified.

An abnormality diagnosis apparatus according to the present invention detects a local minimum value and a local maximum value of a sheet thickness, calculates a deviation between the local minimum value and a local maximum value, and calculates the deviation as a reference value. Above this, the thickness abnormalities are recognized.

[0007] The abnormality diagnosis apparatus according to the present invention estimates the cause of occurrence of an abnormality by applying the rolling results of a rolling mill to a cause estimation rule.

[0008] In the abnormality diagnosis apparatus according to the present invention, when the cause of the abnormality is the speed balance of the rolling mill, the abnormal stand is identified from the looper angle results.

An abnormality diagnosis apparatus according to the present invention is adapted to identify an abnormal stand from the result of speed correction on a stand mill when the cause of the abnormality is a speed balance of a rolling mill.

The abnormality diagnosis apparatus according to the present invention calculates the actual tension of each stand from the actual torque of the mill motor and the actual rolling load, and specifies the abnormal stand from the actual tension of each stand.

The abnormality diagnosis apparatus according to the present invention calculates the speed correction amount of the stand mill from the looper angle results and corrects the speed setting value of the stand mill based on the speed correction amount.

An abnormality diagnosis apparatus according to the present invention calculates an advanced rate correction amount from a speed correction result for a stand mill, and corrects a stand mill advanced rate set value based on the advanced rate correction amount. It is.

An abnormality diagnosis apparatus according to the present invention calculates a torque correction amount of a mill motor from the actual tension of each stand,
The torque set value of the mill motor is corrected based on the torque correction amount.

The abnormality diagnosis apparatus according to the present invention is provided with simulation means for verifying the correction result of the correction means in advance.

The abnormality diagnosis apparatus according to the present invention is provided with re-correction means for re-correcting the correction result of the correction means.

[0016] The abnormality diagnosis apparatus according to the present invention is adapted to learn the correction result of the correction means for each roll.

The abnormality diagnosis apparatus according to the present invention is adapted to learn the correction result of the correction means for each layer.

According to the abnormality diagnosis method of the present invention, a deviation between the thickness of a rolled material rolled by a rolling mill and a target thickness is calculated, and the deviation is compared with a reference value. Above this, the thickness abnormalities are recognized.

The abnormality diagnosis method according to the present invention detects a local minimum value and a local maximum value of a sheet thickness, calculates a deviation between the local minimum value and the local maximum value, and calculates the deviation as a reference value. When the deviation exceeds a reference value, a sheet thickness abnormality is recognized.

In the abnormality diagnosis method according to the present invention, the cause of occurrence of an abnormality is estimated by applying the rolling results of a rolling mill to a cause estimation rule.

[0021]

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS One embodiment of the present invention will be described below. Embodiment 1 FIG. FIG. 1 is a configuration diagram showing an abnormality diagnosis apparatus according to Embodiment 1 of the present invention. In the drawing, reference numeral 1 denotes a rolling plant, 2 controls a rolling mill, and controls the thickness of a rolled material rolled by the rolling mill. The control device (measuring means) 3 for measuring calculates a deviation between the thickness of the rolled material and the target thickness, compares the deviation with a criterion value for abnormal thickness,
When the deviation exceeds a judgment reference value, a monitoring device (calculating means, certifying means) for certifying the thickness abnormality is provided. 4 is a guidance processing device (presenting means) for displaying the certifying result of the monitoring device 3 on the display device 5; Denotes a display device (presenting means) for displaying the result of the certification of the monitoring device 3, and 6 denotes an operator.

Next, the operation will be described. First, the control device 2 controls the rolling mill using a predetermined thickness control method to bring the thickness of the rolled material closer to the target thickness.
The thickness of the rolled material rolled by the rolling mill is measured.

When the control device 2 measures the thickness of the rolled material, the monitoring device 3 determines whether or not the measurement result of the thickness is a measurement result relating to the leading end portion of the rolled material (for example, It is determined that the tip portion is about 10 seconds after the tip of the plate has passed through the thickness gauge.) If the measurement result is related to the tip portion, the measurement result is the thickness result and the target thickness. Is calculated. When calculating the deviation between the actual thickness and the target thickness, the monitoring device 3 compares the deviation with a reference value for determining an abnormal thickness, and if the deviation exceeds the reference value, recognizes the abnormal thickness. ｜ Sheet thickness-Target thickness ｜> Judgment standard value → Thickness error

The criterion value for the sheet thickness abnormality is a sheet thickness management target of a deviation from a target sheet thickness of a product.
As shown in FIG. 2, when a value of ± 30 μ is given as a determination reference value, the deviation between the actual thickness and the target thickness is ± 30 μ.
When the value exceeds the value, the sheet thickness abnormality is recognized.

Then, the monitoring device 3 repeats the above-described processing until it exceeds the range of the leading end portion of the rolled material, and classifies the above-described recognition results for each stratified division. Here, the stratification is a classification using the steel type, target plate thickness, and target plate width of the product as keys, and indicates the characteristics of each product to be rolled. Then, the guidance processing device 4 classifies and displays the certification results of the monitoring devices 3 classified by stratification as shown in FIG.

As is apparent from the above, the first embodiment
According to the above, a deviation between the thickness of the rolled material rolled by the rolling mill and the target thickness is calculated, and when the deviation exceeds the determination reference value, the sheet thickness is configured to be recognized as abnormal, so during the rolling operation, It is effective in diagnosing abnormal thickness.

Embodiment 2 FIG. FIG. 4 is a configuration diagram showing an abnormality diagnosis apparatus according to Embodiment 2 of the present invention. In the figure, the same reference numerals as those in FIG. 1 denote the same or corresponding parts, and a description thereof will be omitted. 7 detects a local minimum value and a local maximum value of the sheet thickness from the measurement result of the control device 2, calculates a deviation between the local minimum value and the local maximum value, and when the deviation exceeds a determination reference value. It is a monitoring device (detecting means, calculating means, and certifying means) for certifying a thickness abnormality.

Next, the operation will be described. In the first embodiment, the case where the sheet thickness abnormality is recognized when the deviation between the sheet thickness actual value and the target sheet thickness exceeds the determination reference value has been described. However, when the deviation does not exceed the determination reference value, FIG. Even if a drop in the plate thickness occurs as shown in (1), it is not possible to identify an abnormal plate thickness.

Therefore, in the second embodiment, the monitoring device 7 detects the local minimum value and the local maximum value of the sheet thickness from the measurement result of the control device 2, and calculates the deviation between the local minimum value and the local maximum value. Is calculated. Then, when calculating the deviation between the local minimum value and the local maximum value, the monitoring device 7 compares the deviation with the reference value for the thickness abnormality, and recognizes the thickness abnormality when the deviation exceeds the determination reference value. I do. | Local minimum value-Local maximum value |> Judgment reference value → Thickness abnormality

The reference value for judging abnormal thickness is a management target for a change in thickness of a product. For example, a value of ± 30 μ or less is given as the reference value.

As is apparent from the above, the second embodiment
According to, the local minimum value and local maximum value of the sheet thickness are detected, the deviation between the local minimum value and the local maximum value is calculated, and if the deviation exceeds the reference value, the sheet thickness abnormality is recognized. With such a configuration, it is possible to diagnose a thickness abnormality in which the thickness varies greatly.

Embodiment 3 FIG. FIG. 6 is a block diagram showing an abnormality diagnosis apparatus according to Embodiment 3 of the present invention. In the figure, the same reference numerals as those in FIG. 1 denote the same or corresponding parts, and a description thereof will be omitted. Reference numeral 8 denotes a rule application unit (estimating means) for estimating the cause of the abnormality by applying the rolling results of the rolling mill to the cause estimation rule when the monitoring device 3 (or the monitoring device 7) recognizes the sheet thickness abnormality. A guidance processing unit (estimating means) for displaying the estimation result of the application unit 8 on the display device 5.

Next, the operation will be described. First, when the monitoring device 3 (or the monitoring device 7) recognizes the sheet thickness abnormality, the rule application unit 8 determines the rolling performance of the rolling mill (the finishing input-side rolling temperature, By applying the reduction amount, operator intervention, etc.) to the cause estimation rule, the cause of the abnormality is estimated.

Specifically, first, the cause of the occurrence of the abnormality is estimated using the rule for estimating the cause of the thickened end plate (FIG. 7). First, it is determined whether or not the rolling position is moving (step ST1). If it is not moving, the speed balance is bad (step ST2), and a speed balance cause estimation rule described later is applied. On the other hand, if the rolling position is moving, it is determined whether or not the operator is intervening in the movement of the rolling position (step ST3).

When the operator intervenes in the movement of the rolling position, it is estimated that the operator's intervention to secure a desired plate thickness is the cause of the abnormality (step ST4). On the other hand, when the operator does not intervene in the movement of the rolling-down position, it is determined whether or not a load change has occurred (step ST5).

If no load fluctuation has occurred, AG
It is estimated that there is a defective part in the system C (step S
T6). On the other hand, if a load change has occurred, it is determined whether the rolling temperature on the finishing side has changed (step ST7).

When the rolling temperature on the finishing side fluctuates,
Since there is a problem in the upstream cooling process (step ST8),
Apply the rule for estimating the cause of the cooling process. On the other hand, if the rolling temperature on the finishing side does not fluctuate, it is estimated that there is a problem in the measurement of the feedback signal because the load is bad (step ST9).

Next, if it is determined in step ST1 that the rolling position has not moved, the cause of the abnormality is estimated using the rule for estimating the cause of speed balance (FIG. 8).
First, it is determined whether or not the deviation between the actual speed and the set speed is larger than a reference value (step ST11). If the deviation is smaller than the reference value, it is determined that the failure of the speed setting by the FSU is the cause of the abnormality. Estimate and instruct the guidance processing unit 9 to present guidance for instructing review of the advanced rate (step ST12). On the other hand, if the deviation between the actual speed and the set speed is larger than the reference value, it is determined whether or not the impact drop amount of the speed is larger than the reference value (step ST13).

If the speed impact drop amount is smaller than the reference value, it is estimated that the speed control system is the cause of the abnormality (step ST14). On the other hand, if the speed impact drop amount is larger than the reference value, it is estimated that the speed impact drop compensation is the cause of the abnormality, and the problematic stand is identified from the change in the impact drop amount (step ST15).

Next, when it is determined in step ST7 that a load change has occurred, as described above, the cause of the occurrence of the abnormality is estimated using the rule for estimating the cause of the cooling process. Description is omitted.

As described above, when the rule application unit 8 estimates the cause of the abnormality, the guidance processing unit 9 displays the estimation result of the rule application unit 8 on the display device 5. The reason for dividing the cause estimation rule into a plurality of modules is to efficiently and quickly estimate the cause of the abnormality.

As is clear from the above, the third embodiment
According to the above, when the monitoring device 3 (or the monitoring device 7) recognizes the sheet thickness abnormality, the rolled material for which the sheet thickness abnormality has been recognized is
Since the configuration is made so that the cause of the abnormality is estimated by applying the rolling results of the rolling mill to the cause estimation rule, the effect that the operator 6 can recognize the cause of the abnormality during the execution of the rolling is exerted.

Embodiment 4 FIG. FIG. 9 is a configuration diagram showing an abnormality diagnosis apparatus according to Embodiment 4 of the present invention. In the figure, the same reference numerals as those in FIG. 6 denote the same or corresponding parts, and a description thereof will be omitted. Reference numeral 10 denotes a model unit (estimating means) for specifying an abnormal stand from the looper angle results when the cause of the abnormality is the speed balance of the rolling mill.

Next, the operation will be described. First, when the rule applying unit 8 estimates that the cause of the abnormality is the speed balance of the rolling mill, the model unit 10 performs a predetermined time (angle monitoring period) from the time when the rolled material is caught in the finishing stand mill. , Monitor the actual looper angle LP _ACT of the looper installed between the finishing stand mill and the preceding stand, and determine the deviation ΔL between the actual looper angle LP _ACT and the looper angle set value LP _SET
Calculate P (see FIG. 10). ΔLP = LP _SET −LP _ACT

If the deviation ΔLP between the actual looper angle LP _ACT and the looper angle set value LP _SET is smaller than the judgment reference value, no overtension has occurred between the stands sandwiching the looper, but the deviation ΔLP is smaller than the judgment reference value. If the value is larger than the value, the looper is suppressed by the influence of the tension, and it means that over tension has occurred. Therefore, when the deviation ΔLP is larger than the determination reference value, the model unit 10 estimates that the rolled material has been pulled between the stands and the thickness break has occurred. The angle monitoring period and the criterion value are:
It is set for each stand, and is set for each layer (steel type, plate thickness, plate width).

For example, in the case of a finishing tandem rolling mill composed of six stands, the above-described processing is executed from a looper between the five stands and the six stands toward the upstream. For example, when the deviation ΔLP is larger than the determination reference value in the looper installed between the third stand and the fourth stand, the difference between the third stand and the fourth stand is due to the fact that the actual speed of the three stands is lower than the set value. Since it is understood that over tension has occurred, three stands are specified as abnormal stands.

When the three stands are specified as abnormal stands in this way, it is necessary to correct the speed for all finishing stands (including three stands) preceding the three stands. 9 displays the finishing stand that needs to be corrected on the display device 5 and displays a message that the speed needs to be corrected on the display device 5.

As is clear from the above, the fourth embodiment
According to the above, when the cause of the abnormality is due to the speed balance of the rolling mill, the abnormal stand is identified from the looper angle results, so that the operator 6 can immediately start the processing for the cause of the thickness break. As a result, it is possible to produce a rolled material having a high yield.

Embodiment 5 In the fourth embodiment, the abnormal stand is specified based on the looper angle record. However, the abnormal stand may be specified based on the speed correction record for the stand mill.

That is, the model unit 10 includes the rule application unit 8
However, when it is estimated that the cause of the abnormality is the speed balance of the rolling mill, the result of speed correction for each finishing stand mill is stored. And the model part 10 compares the correction | amendment result of the speed with respect to each finishing stand mill with a determination reference value,
If the speed correction result exceeds the determination reference value, it is estimated that an overtension occurs between the stand and the next stand, the rolled material is pulled, and the sheet thickness has been cut (see FIG. 11).

As described above, the reason why the occurrence of sheet thickness breakage can be estimated from the results of speed correction is that stable rolling is performed when the speed setting of the finishing stand mill is set to an ideal value. Therefore, the speed correction amount for each finishing stand mill is almost zero, but if the speed setting is poor, the performance correction speed for each finishing stand mill will be large, and over tension between the stands will be inevitable. Because it occurs.

For example, in the case of a finishing tandem rolling mill composed of six stands, the above processing is executed from five stands upstream. For example, in the case of the three stands, when the speed correction result is higher than the determination reference value, the three stand four speeds are lower because the speed result of the three stands is lower than the set value.
It is understood that there is over tension between the stands,
The three stands are specified as abnormal stands.

When the three stands are specified as abnormal stands in this way, the speed needs to be corrected for all finishing stands (including three stands) preceding the three stands. 9 displays the finishing stand that needs to be corrected on the display device 5 and displays a message that the speed needs to be corrected on the display device 5.

As is clear from the above, the fifth embodiment
According to the above, when the cause of the abnormality is due to the speed balance of the rolling mill, the abnormal stand is specified from the correction result of the speed with respect to the stand mill, so that the operator 6 immediately performs the processing for the cause of the thickness break. As a result, it is possible to produce a rolled material having a high yield. Further, unlike the above-described fourth embodiment, the abnormal stand can be identified even when the thickness of the plate is broken due to the intervention of the operator 6.

Embodiment 6 FIG. In the above fourth embodiment, the abnormal stand is specified from the looper angle result. However, the actual tension of each stand is calculated from the mill motor torque result and the rolling load result, and the abnormal stand is specified from the tension result of each stand. You may make it.

That is, the model unit 10 is provided with the rule application unit 8
However, when it is estimated that the cause of the abnormality is due to the speed balance of the rolling mill, the actual torque of the mill motor and the actual load of the mill motor when the rolled material is bitten by each finishing stand mill are stored, and the actual tension σf of each stand is stored. Is calculated.

ΔG = (G ₀ / F ₀ ) × F′−G ′ ΔG × ω = A × v × σf ΔG × ω = A × R × (1 + f) × σf σf = ｛1 / A × R × ( 1 + f)｝ × ｛(G ₀ / F ₀ ) ×
F'-G '} However, G _0: reference time of the mill motor torque G': finishing mill motor torque F when biting stand mill _0: reference time of rolling force F ': rolling load when biting finishing stand mill ω: First stand roll speed A: Plate cross-sectional area between first and second stands f: First stand advance rate v: First stand exit side plate speed R: First stand work roll radius The first stand is a target stand, and the second stand is a subsequent stand of the target stand.

When the actual tension σf is calculated for each stand as described above, the actual tension σf and the set tension σf
Calculate the deviation Δσf of _SET . Δσf = σf _SET −σf Then, the model unit 10 compares the deviation Δσf with the determination reference value, and when the deviation Δσf exceeds the determination reference value,
The stand exceeding the determination reference value is in an over-tension state, and it is determined that there is a problem in the speed setting of the stand. Note that the determination reference value is set for each stand and for each layer (steel type, plate thickness, plate width).

For example, in the case of a finishing tandem rolling mill composed of six stands, the above processing is executed from five stands to one stand. For example, when the determination result exceeds the determination reference value in three stands, it is understood that an excessive tension has occurred between the three stands and the four stands because the actual speed of the three stands is lower than the set value. Identify the stand as an abnormal stand.

When the three stands are specified as abnormal stands in this way, the speed needs to be corrected for all finishing stands (including three stands) preceding the three stands. 9 displays the finishing stand that needs to be corrected on the display device 5 and displays a message that the speed needs to be corrected on the display device 5.

As is apparent from the above, the sixth embodiment
According to the above, if the cause of the abnormality is due to the speed balance of the rolling mill, the actual tension of each stand is calculated from the actual torque of the mill motor and the actual rolling load, and the abnormal stand is identified from the actual tension of each stand. Therefore, the operator 6 can immediately start the processing for the cause of the thickness break, and as a result, there is an effect that a rolled material having a high yield can be manufactured. In the fourth and fifth embodiments, the abnormal stand is specified by using the actual angle of the looper.
Although the abnormal stand cannot be specified for a second, in the sixth embodiment, the abnormal stand can be specified from the time when the rolled material is caught in the finishing stand mill.

Embodiment 7 FIG. 12 is a configuration diagram showing an abnormality diagnosis apparatus according to Embodiment 7 of the present invention. In the figure, the same reference numerals as those in FIG. Reference numeral 11 denotes an online correction unit (correction unit) that calculates the speed correction amount of the stand mill based on the looper angle record and corrects the speed setting value of the stand mill based on the speed correction amount.

Next, the operation will be described. In the fourth embodiment, an abnormal stand is identified from the looper angle record. In the seventh embodiment, when the abnormal stand is specified, the online correction unit 11 _converts the looper angle record LP _{ACTi and the} like into the following. The speed correction amounts ΔV _i of all the stand mills preceding the abnormal stand are calculated by substituting into the arithmetic expression. ΔV _i = ΔSLP _i / T _i ΔSLP _i = ｛(k _1i · ΔLP _i ) ² + (k _2i · ΔLP
_i ) ² ｝ ^1/2 + ｛(k _3i · ΔLP _i ) ² + (k _4i · ΔLP
^{_{i) 2} 1/2 ΔLP i =}} LP SETi -LP ACTi However, T _i: angle monitoring period k _1i to k _4i: looper equipment, the value determined by the material size, steels such as i: number of finishing stands (i = 1 ~ N)

[0064] Then, the online correction unit 11, when calculating the speed correction amount [Delta] V _i stand mill, to correct the speed setpoint of the stand mill on the basis of the speed correction amount [Delta] V _i stand mill online state during rolling running .

As is clear from the above, the seventh embodiment
According to the configuration described above, the speed correction amount of the stand mill is calculated from the looper angle results, and the speed setting value of the stand mill is corrected based on the speed correction amount. This has the effect that the speed set value of the speed can be corrected to the optimum value.

Embodiment 8 FIG. In the fifth embodiment, an abnormal stand is specified from the speed correction results for the stand mill. In the eighth embodiment, when the abnormal stand is specified, the online correction unit 11
Calculates the advanced rate correction amount Δα _i of all the stand mills preceding the abnormal stand by substituting the speed correction results and the like into the following equation.

Δα _i = (SVE _LPCi + SVE _OPEi + SVE _DMCi ) / V
_ACTi However, SVE _LPCI: speed correction amount for the i stands from the looper i SVE _OPEi: speed correction amount SVE _DMCI for i stand from operator: i + 1 speed correction amount for the i stands from the stand V _ACTi: Rolling i stand mill Roll speed record i: Number of finishing stand and looper (i = 1 to
N)

Then, the online correction unit 11 calculates the advance rate correction amount Δα _i of the stand mill, and then calculates the advance rate correction amount Δα _i of the stand mill in the online state during rolling.
Modify the stand mill advance rate setting based on the.

As is clear from the above, the eighth embodiment
According to the configuration, the advanced rate correction amount is calculated from the speed correction result for the stand mill, and the advanced rate set value of the stand mill is corrected based on the advanced rate correction amount. The effect of being able to correct the advanced ratio set value of the stand mill according to the above is to an optimum value. Further, according to the eighth embodiment, even when the physical rolling phenomenon used in the setting calculation is different from the actual rolling phenomenon, occurrence of an abnormal thickness can be suppressed.

Embodiment 9 In the sixth embodiment described above, the abnormal stand is specified from the actual tension of each stand. In the ninth embodiment, when the abnormal stand is specified, the online correction unit 11 determines the actual tension σf of each stand and the like. Is substituted into the following equation to calculate the torque correction amounts ΔTq _i of all the stand mill motors preceding the abnormal stand.

ΔTq _i = q × (ΣΔσf / m) Δσf = σf _SET -σf where, q: value determined from rolling roll, material size, steel type, etc. m: number of deviation Δσf measured i: number of finishing stand (i = 1 to N)

After calculating the torque correction amount ΔTq _i of the stand mill motor, the online correction unit 11 calculates the torque setting value of the stand mill motor based on the torque correction amount ΔTq _i of the stand mill motor in the online state during rolling. To correct.

As is apparent from the above, the ninth embodiment
According to the present invention, the amount of torque correction of the mill motor is calculated from the actual tension of each stand, and the torque set value of the mill motor is corrected based on the amount of torque correction. There is an effect that the torque setting value of the motor can be corrected to the optimum value.

Embodiment 10 FIG. FIG. 13 is a configuration diagram showing an abnormality diagnosis apparatus according to Embodiment 10 of the present invention. In the figure, the same reference numerals as those in FIG. 12 denote the same or corresponding parts, and a description thereof will be omitted. Reference numeral 12 denotes a simulator (simulation means) for verifying the correction result of the online correction unit 11 in advance.

Next, the operation will be described. In the above seventh to ninth embodiments, the case where the online correction unit 11 calculates the correction amount and immediately corrects the set value based on the correction amount has been described. You may make it simulate the physical phenomenon etc. of a rolling mill.

That is, before the online correction unit 11 reflects the corrected set value in the control system, the simulator 12
In consideration of the characteristics of the plant model and control system, simulate the thickness of the rolled material, looper angle, mill speed, rolling position, etc. when using the set values, and confirm whether or not it operates normally . Then, when the normal operation can be confirmed, an instruction to reflect the set value to the control system is given to the online correction unit 11. On the other hand, if the normal operation cannot be confirmed, the online correction unit 11 is instructed to correct the set value, and the simulation is executed again using the corrected set value.

As is apparent from the above, the first embodiment
According to No. 0, since the correction result of the online correction unit 11 is configured to be verified in advance, the behavior of the entire plant equipment is assured. As a result, stable operation of the plant equipment and improvement of the quality of the rolled material are achieved. The effect is obtained.

Embodiment 11 FIG. FIG. 14 is a configuration diagram showing an abnormality diagnosis apparatus according to Embodiment 11 of the present invention. In the figure, the same reference numerals as in FIG. A learning unit (learning means) 13 learns the correction result of the online correction unit 11 in units of roles.

Next, the operation will be described. In the above seventh to ninth embodiments, the case where the online correction unit 11 corrects the set value is described. However, when the set value is corrected in the online state during the rolling, the correction is performed using the model formula. If there is an error, there occurs a problem that the thickness accuracy is deteriorated.

Therefore, in the eleventh embodiment, the learning unit 13 replaces the rolling rolls in order to converge the effects (model errors) caused by friction between the rolling rolls and the rolled material and wear of the rolling rolls to zero. At each time, the following learning is executed to correct the set value. _{Gr '= (ΔV N + V} N) / V N Gr N + 1 = (1-α) × Gr N + α × Gr' However, alpha: learning smoothing coefficient (0~1) Gr ': error due to this rolling Gr _N : Set value used for the current control Gr _{N + 1} : Set value reflecting the current learning result

As is clear from the above, the first embodiment
According to No. 1, since the correction result of the online correction unit 11 is configured to be learned on a roll basis, it is possible to reduce the effects of friction between the rolling roll and the rolled material, wear of the rolling roll, and the like, As a result, there is an effect that the plate thickness accuracy can be improved.

Embodiment 12 FIG. FIG. 15 is a configuration diagram showing an abnormality diagnosis apparatus according to Embodiment 12 of the present invention. In the figure, the same reference numerals as those in FIG. 13 denote the same or corresponding parts, and a description thereof will not be repeated. A learning unit (learning means) 14 learns the correction result of the online correction unit 11 for each layer.

Next, the operation will be described. In the eleventh embodiment, the case where the correction result of the online correction unit 11 is learned for each role has been described. However, the correction result of the online correction unit 11 may be learned for each layer.

That is, the learning unit 14 determines the hardness of the material,
To converge the effects of deformation resistance and other factors (model errors) that depend on the sheet width, etc. to zero, execute the learning shown below for each layer (steel type, sheet thickness, sheet width) and correct the set values I do. G
r ′ = (ΔV _N + V _N ) / V _N Gr _{N + 1} = (1−α) × Gr _N + α × Gr ′ where α: learning smoothing coefficient (0 to 1) Gr ′: error due to current rolling Gr _N : Set value used for the current control Gr _{N + 1} : Set value reflecting the current learning result

As is apparent from the above, the first embodiment
According to 2, since the correction result of the online correction unit 11 is learned for each layer, it is possible to reduce the influence of material-dependent deformation resistance and the like, and as a result, to improve the thickness accuracy. The effect that can be aimed at is produced.
It is to be noted that the present invention has the effect of improving the thickness accuracy even for a rolled material having a low rolling frequency.

[0086]

As described above, according to the present invention, a deviation between the thickness of a rolled material rolled by a rolling mill and a target thickness is calculated, and if the deviation exceeds a reference value, an abnormal thickness is recognized. Therefore, there is an effect that a thickness abnormality can be diagnosed during rolling.

According to the present invention, a local minimum value and a local maximum value of the sheet thickness are detected, and a deviation between the local minimum value and the local maximum value is calculated. Since the configuration is such that the thickness abnormality is recognized, there is an effect that it is possible to diagnose a thickness abnormality in which the thickness varies greatly.

According to the present invention, the cause of the abnormality is estimated by applying the rolling results of the rolling mill to the cause estimating rule, so that the operator can recognize the cause of the abnormality during the rolling. There is an effect that can be.

According to the present invention, when the cause of the abnormality is the speed balance of the rolling mill, the abnormal stand is specified from the looper angle record, so that the operator can immediately perform the processing for the cause of the thickness break. As a result, it is possible to produce a rolled material having a high yield.

According to the present invention, when the cause of the abnormality is the speed balance of the rolling mill, the abnormal stand is specified based on the result of correcting the speed of the stand mill. Can be started immediately, and as a result, there is an effect that a rolled material having a high yield can be manufactured.

According to the present invention, the actual tension of each stand is calculated from the actual torque of the mill motor and the actual rolling load.
The system is configured to identify the abnormal stand from the tension results of each stand, so that the operator can immediately start the processing for the cause of the thickness break, and as a result,
There is an effect that a rolled material having a high yield can be manufactured.

According to the present invention, the speed correction amount of the stand mill is calculated from the actual looper angle, and the speed setting value of the stand mill is corrected based on the speed correction amount. There is an effect that the speed setting value of the stand mill related to the stand can be corrected to the optimum value.

According to the present invention, the advanced ratio correction amount is calculated from the speed correction result for the stand mill, and the advanced ratio set value of the stand mill is corrected based on the advanced ratio correction amount. There is an effect that the advanced ratio set value of the stand mill relating to the abnormal stand can be corrected to the optimum value during execution.

According to the present invention, the amount of torque correction of the mill motor is calculated from the actual tension of each stand, and the torque set value of the mill motor is corrected based on the amount of torque correction. There is an effect that the torque set value of the stand mill motor related to the stand can be corrected to an optimum value.

According to the present invention, since the simulation means for verifying the correction result of the correction means in advance is provided, the behavior of the entire plant equipment is assured, and as a result, the stable operation of the plant equipment is ensured. This has the effect of improving the quality of the rolled material.

According to the present invention, the re-correction means for re-correcting the correction result of the correction means is provided. If the correction result of the correction means is inappropriate, the correction result is corrected. There is an effect that can be.

According to the present invention, since the correction result of the correction means is learned for each roll, it is possible to reduce the influence of friction between the rolling roll and the rolled material, wear of the rolling roll, and the like. As a result, there is an effect that plate thickness accuracy can be improved.

According to the present invention, since the correction result of the correction means is learned for each layer, it is possible to reduce the influence of material-dependent deformation resistance and the like. There is an effect that can be improved.

According to the present invention, the deviation between the thickness of the rolled material rolled by the rolling mill and the target thickness is calculated,
The deviation is compared with a reference value, and when the deviation exceeds the reference value, the thickness abnormality is recognized, so that the thickness abnormality can be diagnosed during the rolling.

According to the present invention, the local minimum value and the local maximum value of the sheet thickness are detected, the deviation between the local minimum value and the local maximum value is calculated, and the deviation is compared with the reference value. When the deviation exceeds a reference value, the thickness of the sheet is determined to be abnormal. Therefore, it is possible to diagnose an abnormality of the sheet thickness such that the sheet thickness greatly varies.

According to the present invention, the cause of the abnormality is estimated by applying the rolling results of the rolling mill to the cause estimation rule, so that the operator can recognize the cause of the abnormality during the rolling operation. There is an effect that can be.

[Brief description of the drawings]

FIG. 1 is a configuration diagram showing an abnormality diagnosis device according to a first embodiment of the present invention.

FIG. 2 is an explanatory diagram illustrating an abnormal plate thickness.

FIG. 3 is an explanatory diagram illustrating a display example of a display device.

FIG. 4 is a configuration diagram showing an abnormality diagnosis device according to a second embodiment of the present invention.

FIG. 5 is an explanatory diagram illustrating an abnormal plate thickness.

FIG. 6 is a configuration diagram illustrating an abnormality diagnosis device according to a third embodiment of the present invention.

FIG. 7 is a flowchart showing a rule for estimating the cause of the end plate thickness break.

FIG. 8 is a flowchart showing a rule for estimating the cause of the speed balance.

FIG. 9 is a configuration diagram illustrating an abnormality diagnosis device according to a fourth embodiment of the present invention.

FIG. 10 is an explanatory diagram illustrating an abnormal plate thickness.

FIG. 11 is an explanatory diagram illustrating an abnormal thickness.

FIG. 12 is a configuration diagram illustrating an abnormality diagnosis device according to a seventh embodiment of the present invention.

FIG. 13 is a configuration diagram illustrating an abnormality diagnosis device according to a tenth embodiment of the present invention.

FIG. 14 is a configuration diagram illustrating an abnormality diagnosis device according to an eleventh embodiment of the present invention.

FIG. 15 is a configuration diagram illustrating an abnormality diagnosis device according to a twelfth embodiment of the present invention.

[Explanation of symbols]

2 control device (measuring means), 3 monitoring device (computing means,
Certification means), 4 guidance processing device (presentation means),
5 display device (presentation means), 7 monitoring device (detection means,
Calculation means, recognition means), 8 rule application section (estimation means), 9 guidance processing section (estimation means), 10 model section (estimation means), 11 online correction section (correction means), 12 simulator (simulation means),
13, 14 Learning unit (learning means).

Claims (16)

[Claims] 1. A measuring means for measuring the thickness of a rolled material rolled by a rolling mill, a calculating means for calculating a deviation between the thickness measured by the measuring means and a target thickness, and a calculating means for calculating by the calculating means. An abnormality diagnosis device comprising: a certifying means for comparing the obtained deviation with a reference value, and when the deviation exceeds the criterion value, certifies a sheet thickness abnormality; and a presentation means for presenting the qualification result of the certifying means. 2. A measuring means for measuring the thickness of a rolled material rolled by a rolling mill, detecting a local minimum value of the thickness from the measurement result of the measuring means, and determining a local maximum value of the thickness. Detecting means for detecting; calculating means for calculating a deviation between the local minimum value and local maximum value detected by the detecting means; comparing the deviation calculated by the calculating means with a reference value; An abnormality diagnosis device comprising: a certifying means for certifying a sheet thickness abnormality when the value exceeds a value; and a presenting means for presenting a result of qualification by the certifying means. 3. An estimating means for estimating a cause of occurrence of an abnormality by applying a rolling result of a rolling mill to a cause estimating rule and outputting the estimation result to a presenting means is provided. Item 2. The abnormality diagnosis device according to Item 2. 4. The abnormality diagnosis apparatus according to claim 3, wherein the estimating means identifies the abnormal stand from the looper angle results when the cause of the abnormality is a speed balance of the rolling mill. 5. The abnormality diagnosis apparatus according to claim 3, wherein when the cause of the abnormality is a speed balance of the rolling mill, the estimating means identifies the abnormal stand from the result of correcting the speed of the stand mill. 6. The abnormality diagnosis according to claim 3, wherein the estimating means calculates the actual tension of each stand from the actual torque of the mill motor and the actual rolling load, and specifies an abnormal stand from the actual tension of each stand. apparatus. 7. The apparatus according to claim 4, further comprising correction means for calculating a speed correction amount of the stand mill from the looper angle record and correcting a speed setting value of the stand mill based on the speed correction amount. Abnormal diagnostic device. 8. A correction means for calculating an advanced rate correction amount from a speed correction result of a stand mill and correcting an advanced rate set value of the stand mill based on the advanced rate correction amount. Item 6. The abnormality diagnosis device according to Item 5. 9. The method according to claim 6, further comprising the step of: calculating a torque correction amount of the mill motor from the actual tension of each stand, and correcting a torque set value of the mill motor based on the torque correction amount. Abnormal diagnostic device. 10. A simulation device for preliminarily verifying a correction result of the correction device.
The abnormality diagnosis device according to any one of claims 1 to 10. 11. The abnormality diagnosis apparatus according to claim 10, further comprising a re-correction unit for re-correcting a correction result of the correction unit. 12. The abnormality diagnosis apparatus according to claim 10, further comprising learning means for learning a correction result of the correction means on a roll basis. 13. The abnormality diagnosis apparatus according to claim 10, further comprising learning means for learning a correction result of the correction means for each layer. 14. A method for measuring a thickness of a rolled material rolled by a rolling mill, calculating a deviation between the thickness of the rolled material and a target thickness, comparing the deviation with a reference value, and calculating the deviation. Abnormality diagnosis method that recognizes abnormal thickness when the value exceeds the standard value and presents the result of the certification. 15. A sheet thickness of a rolled material rolled by a rolling mill is measured, a local minimum value and a local maximum value of the sheet thickness are detected, and a deviation between the local minimum value and the local maximum value is detected. Is calculated, and the deviation is compared with a reference value. If the deviation exceeds the reference value, a sheet thickness abnormality is recognized, and the result of the diagnosis is presented. 16. The abnormality diagnosis method according to claim 14, wherein the cause of the abnormality is estimated by applying the rolling results of the rolling mill to the cause estimation rule, and the estimation result is presented.