JPS6360011A - Monitoring method for rolling mill - Google Patents

Abstract

PURPOSE:To improve monitoring capacity by measuring the rolling load and driving torque of a rolling mill and the oscillation of a driving system, then subjecting the respective measured values to satistical processing and deciding the wear of universal joints for driving work rolls. CONSTITUTION:A multiplexer 10 to collect various kinds of data on the rolling mill 1 is provided. An A/D converter 11, a memory device 12, an arithmetic unit 13, an analyzer 15, etc., are successively disposed. The data sampled at various timing are inputted via the A/D converter 11 to the memory device 12 and the arithmetic unit 13 and are stored in the form of various analysis variables in a data memory device 14. A main component analysis is then carried out with the selected variables by the analyzer 15 and the variables are subjected to the statistical processing to decide the wear of the universal joints 8 for driving the work rolls 7. Since the deterioration of the equipment is exactly decided from various sets of the information generated by the rolling mill 1, the monitoring capacity for the rolling mill 1 is improved.

Description

DETAILED DESCRIPTION OF THE INVENTION (a) Field of Industrial Application The present invention relates to a method for comprehensively monitoring and diagnosing the deterioration status of equipment in a rolling mill.

(b) Conventional technology In a rolling mill, upper and lower rolls or left and right rolls rotate while pressing the rolled material to obtain a product with the required dimensions. Maintenance of the equipment requires a lot of experience, labor, and expense (materials such as consumables).

In particular, in the work/roll rotation drive system, universal joints such as slipper metals and gear couplings are used to transmit the enormous power (the largest in the rolling mill) and to constantly change the position of the rolls. It is being said. This universal joint always has a constant clearance (gap). but,
The wear of this clearance progresses due to the huge transmission force mentioned above, and when it becomes large, the quality of the rolled material is affected.

Therefore, it is necessary to periodically replace the metals used in the universal joints for driving work rolls. However, conventionally, there is no suitable method for measuring the amount of wear (measuring the relative movement by applying force when the machine is stopped), and if the replacement time is early, the material cost will increase, and conversely, if the replacement time is late, the product There was a loss due to quality deterioration, and determining the optimal replacement timing was a maintenance issue.

Typical conventional examples for determining the optimal replacement timing include the following1. The equipment deterioration status evaluation method (Japanese Patent Application Laid-Open No. 59-77514) is particularly concerned with a method for detecting abnormalities in the rotational force transmission system. be. There is also a method (Japanese Unexamined Patent Publication No. 1982-17367) that uses this method to evaluate the state of deterioration of the motor system. Furthermore, a roll drive monitoring device (Japanese Patent Laid-Open No. 61-887
No. 76), rolling (the effect of several rolls is mathematically modeled and utilized for control of the transmission system.Rolling mill; when the roll drive system is mathematically modeled and controlled on t control, joints are used. Wear on the parts will greatly affect the control system,
The aforementioned gap management will become even more important.

In the end, conventional equipment monitoring and diagnosis methods use various sensors to detect the state of specific and limited parts of the equipment, perform some processing on it, and diagnose it.
This can be said to be equipment monitoring within a limited range. For example, if torque or housing torque effect is used as a parameter, these amounts also change depending on operating conditions, so it is clear from changes in this amount alone whether it is due to equipment deterioration or operational effects; τ
I can't judge.

(c) Problems that the invention attempts to solve The problems that the invention attempts to solve are that the various information generated by the rolling mill is subjected to statistical processing, information loss is minimized, and new deterioration information is generated. It is an object of the present invention to provide a method for deriving parameters and thereby monitoring and diagnosing the deterioration state of a comprehensive lip rolling mill.

1. Means for Solving Hot Problems The rolling mill monitoring method of the present invention involves measuring the rolling load, drive torque, and vibration of the drive system of the rolling mill, and statistically processing the measured values by principal component analysis. The above-mentioned point m is solved by determining the wear of the universal joint for moving the workpiece roll.

Preferably, the vibration of the roll chock in the rolling mill is measured as the vibration of the drive system.

It is preferable to use the ratio between the maximum value and the average value of each measurement value as data for principal component analysis.

(E) Embodiment The schematic structure of an apparatus for carrying out the method of the present invention is shown in FIG. The information obtained from the rolling mill
current, vibration of main reducer 3, vibration and temperature of binion stand 4, vibration of mill housing 5, vibration of backup roll choke 6, spindle thrust,
These include spindle transmission torque, rolling load, etc. The operational data includes the material of the rolled material, rolling dimensions, rolling temperature, rolling reduction amount, etc.

In a normal rolling mill 1, work rolls 7 are interlocked by the power of a main motor 2 through a universal joint 8.

The analysis device 9 according to the present invention has a function of arbitrarily selecting analysis data for the imported data, and is capable of performing analysis using an ideal combination of variables.

In the analysis device 9 of the present invention, the multiplexer 10 operates using the rolling load 5fi as a trigger signal to acquire set data for various data obtained from the rolling mill 1. Figure 2 shows an overview of data import. Each symbol in FIG. 2 represents the following items.

A: Data during biting B: Data during steady rolling C: Data during ash removal D: Valid data E: ) IJ gadiring time (can be set arbitrarily) F:
) The data sampled at the above timing is stored in the storage device 12 through the A/D converter 1. The stored data is sequentially calculated by the arithmetic unit 13 and stored in the data storage device 4 as various analysis variables.

The calculation contents include the biting value, the value for ash removal, the effective value during steady rolling, the average value, the maximum value, the minimum value, the biting value/average value, the value/average value for ash removal, and the vibration value. Regarding this, footsis value and skewness value are also calculated.

Then, extract and combine variables for actual analysis.

Using the analyzer 15, principal component analysis is performed on these selected variables. The method of principal component analysis is well known and will therefore be omitted.

The output of analyzer 15 is input to output device 16 .

Analyzer 15 receives further setting inputs from pre-input device 17 .

Specific examples are shown below. In the case of this example, the following eight variables were selected and principal component analysis was performed.

Xl: Rolling load at biting X2: Rolling load biting value/average value X3: Spindle torque at biting X4: Torque biting value/average value X5: Backup roll chock line direction vibration at biting X6: Bite value/average value of backup roll chock line direction vibration Xl: Backup roll chock vertical vibration during biting Tables 1 to 4 show the results of analyzing 49 data of 8 variables. Table 1 shows the correlation coefficients of each variable, Table 2 shows the eigenvalues, contribution rates and cumulative contribution rates calculated from the correlation matrix, Table 3 shows the factor loadings calculated from the correlation matrix, and Table 4 shows the factor loadings calculated from the correlation matrix. Principal component analysis results are shown.

FIG. 6 shows main component 2, which will be described later. ．． 22 score calculation results are shown. Using this analysis result, the condition of the rolling mill 1 is evaluated.

In this example, main component 2. ．． The cumulative contribution rate up to 22 is 82.2%. This reduces the amount of information held by the analysis variables X1 to X8 by 2. ．． 22, which means that 82.2% can be expressed with two new variables. 21+22
When considering the characteristics of 2. ．． We pay attention to the factor loading of each analysis variable for 22.

B: Each variable shows relatively high values, and all take positive values. This means that no matter which variable is thicker, the value of Z will be larger, indicating the overall size at the time of biting.

Z2: Load and torque take positive values, and vibration value takes negative values.

In addition, if the factor loading of the bite value/average value for both load and torque is large compared to others, and Z2 tends to move away from 0 in the positive direction, the value of impact load or torque tends to be large. It means that. As the vibration value increases, the negative value tends to increase.

These 2. ．． Based on the characteristics of 22, the equipment condition is evaluated using the score scatter diagram shown in Figure 6. In the case of this example, data obtained when the equipment was in a state that was considered normal and data immediately before the spindle slipper metal was replaced were analyzed. From the distribution of scores, it can be seen that there is a clear difference in the data direction between normal state N and slipper metal wear progressing state A.

Therefore, by monitoring the score distribution state of this variable, it is possible to comprehensively monitor the rolling mill including the power transmission system.

In the case of this example, the combination of variables was used to monitor the degree of impact at the time of biting, but if it is desired to mainly monitor abnormal vibrations of the mill/Using body, another combination of variables can be used. It has become.

Examples of variable combinations are shown below.

Xl: Torque bite value X2: Torque average value (during steady rolling) X3 Double backup roll chock, line direction vibration biting value X4: Backup roll chock line direction vibration average value (during steady rolling) Backup roll chock vertical vibration bite value X6: Backup roll chock vertical vibration average value (during steady rolling) It is possible to extract deterioration information and accurately determine the equipment condition.

[Brief explanation of drawings]

FIG. 1 is a schematic structural explanatory diagram of an apparatus for carrying out the method of the present invention. FIG. 2 is an explanatory diagram of importing data obtained from a rolling mill. FIG. 3 is a graph showing the results of principal component score calculations. 1: Rolling mill 2: Main motor 3: Main reducer 4: Binion stand 5:
Mill housing 6: Backup roll chock 7: Work roll 8: Universal joint 9: Analysis device Patent applicant Sumitomo Metal Industries, Ltd. (5 others)

Claims (3)

[Claims] (1) Rolling consists of measuring the rolling load, drive torque, and vibration of the drive system of the rolling mill, and statistically processing the measured values using principal component analysis to determine the wear of the work roll drive universal joint. Machine monitoring method. (2) The method according to claim (1), characterized in that vibrations of a roll chock in a rolling mill are measured. (3) The method according to claim (1), characterized in that the ratio between the maximum value and the average value of each measurement value is used as data for principal component analysis.