KR101778392B1 - Apparatus and method for monitoring facility - Google Patents

Apparatus and method for monitoring facility Download PDF

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Publication number
KR101778392B1
KR101778392B1 KR1020150178105A KR20150178105A KR101778392B1 KR 101778392 B1 KR101778392 B1 KR 101778392B1 KR 1020150178105 A KR1020150178105 A KR 1020150178105A KR 20150178105 A KR20150178105 A KR 20150178105A KR 101778392 B1 KR101778392 B1 KR 101778392B1
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South Korea
Prior art keywords
ratio
fracture
measured
ductile
steel
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KR1020150178105A
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Korean (ko)
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KR20170070902A (en
Inventor
강주석
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주식회사 포스코
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/10—Services
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N3/00—Investigating strength properties of solid materials by application of mechanical stress
    • G01N3/30—Investigating strength properties of solid materials by application of mechanical stress by applying a single impulsive force, e.g. by falling weight
    • G01N3/303—Investigating strength properties of solid materials by application of mechanical stress by applying a single impulsive force, e.g. by falling weight generated only by free-falling weight
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2203/00—Investigating strength properties of solid materials by application of mechanical stress
    • G01N2203/003—Generation of the force
    • G01N2203/0032—Generation of the force using mechanical means
    • G01N2203/0033—Weight
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2203/00—Investigating strength properties of solid materials by application of mechanical stress
    • G01N2203/0058—Kind of property studied
    • G01N2203/006—Crack, flaws, fracture or rupture
    • G01N2203/0067—Fracture or rupture

Abstract

A facility monitoring apparatus according to an embodiment of the present invention includes a measurement unit for measuring a fracture characteristic of a material produced in a facility, a modeling unit for modeling and calculating fracture characteristics of the material on the basis of relevant factors of the material produced in the facility, And an analyzing unit for analyzing the state of the equipment based on the measurement result of the measuring unit and the modeling result of the modeling unit.

Description

[0001] Apparatus and method for monitoring facility [

The present invention relates to a facility monitoring apparatus and method.

In general, it is important to diagnose equipment abnormality early and to respond promptly to maintenance / repair for stable operation of the field facility.

To do this, the machine can monitor the temperature measurement and the hydraulic / hydraulic valve opening and closing using a number of measuring instruments.

However, if the instrument malfunctions due to steam or frost, or if equipment changes that can not be measured by the instrument occur, there is a limit to detecting the equipment abnormality.

Published Patent Application No. 10-2009-0058748

One embodiment of the present invention provides a facility monitoring apparatus and method.

A facility monitoring apparatus according to an embodiment of the present invention includes a measurement unit for measuring a measured ductile wavefront ratio or a measured brittle fracture wave rate of a steel material produced in a steel processing facility; A modeling unit for modeling and calculating an expected ductile wave fracture rate or an expected brittle fracture wave rate of the steel material based on the relevant factors of the steel material; And an analyzing unit for analyzing an abnormal state of the steel processing facility based on the measured ductile wave fracture ratio or a measured value of the measured brittle fracture wave ratio relative to the estimated ductile wave fracture ratio or estimated brittle fracture ratio; . ≪ / RTI >

For example, the measuring unit may measure the measured ductile wavefront ratio or the measured brittle wavefront ratio using a drop weight tear test (DWTT) test method.

For example, the modeling unit may analyze an abnormal state of the steel processing facility by comparing a ratio obtained by subtracting the expected brittle fracture ratio from 100% to the measured ductile wave fracture ratio.

For example, the relevant factor may comprise at least one of a micro precipitate related factor, a cleanliness related factor and a grain control factor.

For example, the modeling unit may model the expected ductile wave fracture ratio or the predicted brittle fracture wave rate by fixing the temperature of the steel material to a temperature lower than the ductile-to-brittle transition temperature of the steel material.

A facility monitoring method according to an embodiment of the present invention includes the steps of: measuring a measured ductile wavefront ratio or a measured brittle fracture wave rate of a steel material produced in a steel processing facility; The facility monitoring apparatus modeling and calculating an expected ductile wave fracture rate or a predicted brittle fracture wave rate of the steel material based on the relevant factors of the steel material; And analyzing the abnormal state of the steel processing facility based on the measured ductile wave fracture ratio or a measured value of the measured brittle fracture wave ratio relative to the predicted ductile wave fracture ratio or estimated brittle fracture wave ratio; . ≪ / RTI >

For example, the facility monitoring method may further include a step of changing the finishing rolling finishing temperature of the thick plate process when the facility monitoring apparatus analyzes that the abnormality occurs in the steel processing facility by the analyzing step .

According to the present invention, by tracking the fluctuation of the predicted value and the actual value, it is possible to quickly cope with the abnormality of the facility by monitoring the state of the facility on the site complementary to the meter.

In addition, according to the present invention, it is possible to secure the reliability of the stable state of the on-site facilities and to stabilize the quality of the final product through the same, and to derive operating conditions for quality optimization in the steady state of the facility.

1 is a block diagram of a facility monitoring apparatus according to an embodiment of the present invention.
2 is a graph for explaining equipment status analysis of the facility monitoring apparatus shown in FIG.
3 is a graph for explaining the correspondence according to the equipment status analysis of the facility monitoring apparatus shown in FIG.
4 is a graph showing a change in a related factor used for modeling the facility monitoring apparatus shown in FIG.
5 is a flowchart illustrating a facility monitoring method according to an embodiment of the present invention.
6 is a diagram illustrating an exemplary computing environment in which one or more embodiments disclosed herein may be implemented.

DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. However, the embodiments of the present invention can be modified into various other forms, and the scope of the present invention is not limited to the embodiments described below. The shape and the size of the elements in the drawings may be exaggerated for clarity and the same elements are denoted by the same reference numerals in the drawings.

1 is a block diagram of a facility monitoring apparatus according to an embodiment of the present invention.

1, a facility monitoring apparatus 100 according to an embodiment of the present invention may include a measurement unit 110, a modeling unit 120, and an analysis unit 130, The state of the producing facility 200 can be monitored.

The measurement unit 110 can measure the fracture characteristics of the steel 300 produced in the facility 200. [ Here, the fracture characteristics may include a value indicating a degree of fracture upon receiving a load such as a shear fracture percentage and a brittleness fracture percentage. Therefore, the measurement unit 110 can measure the fracture characteristics of the steel 300 by the Drop Weight Tear Test (DWTT).

Here, the DWTT test method refers to an evaluation method in which fracture characteristics are measured by breaking a notched specimen at a specific temperature. For example, the DWTT test method can be used as a criterion for evaluating the low temperature toughness of a steel for line peening.

The modeling unit 120 can model and calculate the fracture characteristics of the steel 300 based on the relevant factors of the steel 300 produced in the facility 200. [ The steel material may have soft-brittle transition characteristics in which the failure mode changes from soft to brittle as the use temperature is lowered. At the transition temperature, the DWTT ductility factor characteristics can be sensitive to the manufacturing conditions of steel. Therefore, the modeling unit 120 can model the failure characteristics of the steel 300 by setting the manufacturing conditions of the steel to the related factors of the steel 300 in the vicinity of the transition temperature.

For example, the modeling unit 120 may model a brittle wavefront ratio or a soft wavefront ratio using a production database of the facility 200 by a statistical technique. Here, the modeling unit 120 can easily calculate the other one by modeling one of the brittle wavefront ratio and the ductile wavefront ratio, by calculating the sum of the brittle wavefront ratio and the softened wavefront ratio as 100%.

For example, because brittle fracture rates are random variables associated with failure modes, it can be difficult to predict with the multiple ear regression analysis. Accordingly, the modeling unit 120 can predict the brittle wavefront ratio through a logistic regression analysis.

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The analysis unit 130 may analyze the state of the facility 200 based on the measurement result of the measurement unit 110 and the modeling result of the modeling unit 120.

If an unmeasurable equipment change occurs in the meter 200 of the facility 200, the apparent production status of the measurement information may be the same as the normal state of the equipment and the equipment abnormal state. However, since the inherent manufacturing conditions of the material itself are different from the normal state of the equipment and the state of equipment abnormality, the destruction characteristics may be different depending on the state of the equipment.

Accordingly, the analyzer 130 can monitor the state of the on-site facilities in a complementary manner to the instrument by tracking the change in the predicted value and the actual value, thereby quickly responding to the abnormality in the equipment.

Furthermore, the facility monitoring apparatus 100 according to an embodiment of the present invention can assure the reliability of the stable state of the field facility and stabilize the quality of the final product through the same, It is also possible to derive operating conditions for optimization.

2 is a graph for explaining equipment status analysis of the facility monitoring apparatus shown in FIG.

Referring to FIG. 2, the horizontal axis represents the production order, and the vertical axis represents the fracture characteristics (PROPERTY) in the form of relative values of the reference fracture characteristics.

The PREDICTION and the MEASURE of the failure characteristics can be similarly changed in the normal condition of the equipment, and the PREDICTION and the MEASURE can be clearly different from each other in the equipment failure state.

Therefore, the facility monitoring apparatus according to an embodiment of the present invention can monitor the state of the facility by monitoring the change of the failure characteristics.

3 is a graph for explaining the correspondence according to the equipment status analysis of the facility monitoring apparatus shown in FIG.

Referring to FIG. 3, the horizontal axis represents the production order and the vertical axis represents the DWTT ductile wavefront ratio as a ratio. The small points in the curve represent the ductile wave fracture ratios measured or predicted respectively for steel of one or two production units and the large point represents the ductile wave fracture ratios of a plurality of steel group in 10 to 20 production units.

Here, the prediction model was modeled on API-X70 grade line pipe production process. The graph of FIG. 3 shows a tendency that the predicted value and the measured value are similar to each other in the production sequence 1 to 140, and the measured value is lower than the predicted value in the production sequence 140 to 200. This means that the state of the equipment has changed from the production order 140 to the abnormal state.

Thereafter, the graph of FIG. 3 again shows a tendency that the predicted value and the measured value become similar to each other over the production order 200 or more. This is the result of lowering the finish rolling temperature of the equipment from the production sequence 190 and immediately responding to steel quality defects.

4 is a graph showing a change in a related factor used for modeling the facility monitoring apparatus shown in FIG.

Referring to FIG. 4, C, Nb, and V represent microstructure-related factors, N, P, and S represent cleanliness related factors, and T0, T5, and SCT represent crystal grain control factors. Further, T0 represents the slab extraction temperature, T5 represents the finishing rolling finishing temperature, and SCT represents the accelerated cooling starting temperature. That is, the facility monitoring apparatus according to an embodiment of the present invention models at least one of micro-precipitate-related factors, cleanliness-related factors, and crystal grain control factors of steel as an independent variable in logistic regression analysis, .

Here, the micro precipitant-related factors and the cleanliness-related factors may have similar performance values regardless of the entire production period. On the contrary, the grain control factor can be maintained at a low temperature according to the low temperature extraction operation by heating up to the production order 80. Thereafter, the slab extraction temperature (T0) increases to the production sequence 140 and the finish rolling finish temperature (T5) can be lowered to improve the productivity.

Production deviation was observed in the vicinity of the production sequence 140 to 160, but the production proceeded to about 190 times under the same slab extraction temperature (T0) and the same finish rolling finish temperature (T5). In other words, in the production sequence 141 to 190, the operation conditions were the same as those in the production sequence 80 to 140, but the results showed that the quality of the DWTT was lowered because the slab extraction The values of the temperature (T0) and finish rolling finish temperature (T5) were the same as before, but the actual manufacturing conditions felt by the material varied. Therefore, a meter or equipment abnormality can be estimated in this section.

Also, the DWTT brittle fracture surface ratio can be greatly affected by the test temperature by the ductile-brittle transition phenomenon. When the test temperature is selected as an independent variable, the influence of other variables becomes relatively negligible so that the performance of the modeling may deteriorate. Thus, in modeling, the test temperature can be set to a fixed temperature, such as -20 ° C. For example, the fixed test temperature may be lower than the soft-brittle transition temperature of the steel.

The ranges of the independent variables in the logistic regression analysis can be set as shown in Table 1 below. Here, the unit of T0, T5, and SCT is Celsius, and the unit of the remaining variable is a percentage.

C P S Nb V N T0 T5 SCT Min 0.05 0.0065 0.0010 0.0010 0.045 0.0010 1050 730 700 Max 0.07 0.0140 0.0050 0.0045 0.060 0.0080 1200 880 820

5 is a flowchart illustrating a facility monitoring method according to an embodiment of the present invention.

Referring to FIG. 5, a facility monitoring method according to an embodiment of the present invention includes a step (S10) of measuring destructive properties of steel produced in a facility, and a step (S30) of analyzing the state of the equipment based on the fracture characteristics of the steel obtained by the measurement and the fracture characteristics of the steel obtained by the modeling .

For example, the facility may be a facility for a steel plate process.

Accordingly, the modeling and calculating step S20 may be performed by modeling the brittle fracture surface ratio of the steel using at least one of the microstructure-related factors, the cleanliness-related factors, and the grain-size control factors of the steel. The control factor may include at least one of a slab extraction temperature, a finish rolling finish temperature, and an accelerated cooling start temperature.

On the other hand, when it is analyzed by the analyzing step that an abnormality occurs in the facility, the facility monitoring apparatus may change the finishing rolling finishing temperature of the thick rolling process.

FIG. 6 is a diagram illustrating an exemplary computing environment in which one or more embodiments disclosed herein may be implemented, and is illustrative of a system 1000 including a computing device 1100 configured to implement one or more of the embodiments described above. / RTI > For example, the rolling simulation drive apparatus, the dynamic plate shape control image generation apparatus, the image conversion processing apparatus, and the like disclosed in the present specification can be implemented by the computing environment described with reference to FIG.

For example, the computing device 1100 may be a personal computer, a server computer, a handheld or laptop device, a mobile device (mobile phone, PDA, media player, etc.), a multiprocessor system, a consumer electronics device, A distributed computing environment including any of the above-described systems or devices, and the like.

The computing device 1100 may include at least one processing unit 1110 and memory 1120. [ The processing unit 1110 may include, for example, a central processing unit (CPU), a graphics processing unit (GPU), a microprocessor, an application specific integrated circuit (ASIC), a field programmable gate array And may have a plurality of cores. The memory 1120 can be a volatile memory (e.g., RAM, etc.), a non-volatile memory (e.g., ROM, flash memory, etc.) or a combination thereof.

In addition, the computing device 1100 may include additional storage 1130. Storage 1130 includes, but is not limited to, magnetic storage, optical storage, and the like. The storage 1130 may store computer readable instructions for implementing one or more embodiments as disclosed herein, and other computer readable instructions for implementing an operating system, application programs, and the like. The computer readable instructions stored in storage 1130 may be loaded into memory 1120 for execution by processing unit 1110.

In addition, computing device 1100 may include input device (s) 1140 and output device (s) 1150. Here, input device (s) 1140 may include, for example, a keyboard, a mouse, a pen, a voice input device, a touch input device, an infrared camera, a video input device, or any other input device. Also, output device (s) 1150 can include, for example, one or more displays, speakers, printers, or any other output device. In addition, computing device 1100 may use an input device or output device included in another computing device as input device (s) 1140 or output device (s) 1150. [

The computing device 1100 may also include communication connection (s) 1160 that enable communication with other devices (e.g., computing device 1300) via the network 1200. (S) 1160 may include a modem, a network interface card (NIC), an integrated network interface, a radio frequency transmitter / receiver, an infrared port, a USB connection or other Interface. Also, the communication connection (s) 1160 may include a wired connection or a wireless connection.

Each component of the computing device 1100 described above may be connected by various interconnects (e.g., peripheral component interconnect (PCI), USB, firmware (IEEE 1394), optical bus architecture, etc.) And may be interconnected by a network.

As used herein, terms such as "component," "module," "system," "interface," and the like generally refer to a computer-related entity that is hardware, a combination of hardware and software, software, or software in execution. For example, an element may be, but is not limited to being, a processor, an object, an executable, an executable thread, a program and / or a computer running on a processor. For example, both the application running on the controller and the controller may be components. One or more components may reside within a process and / or thread of execution, and the components may be localized on one computer and distributed among two or more computers.

The present invention is not limited to the above-described embodiments and the accompanying drawings. It will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims. It will be self-evident.

100: Equipment monitoring device
110:
120: Modeling unit
130:
200: Equipment
300: Steel

Claims (9)

A measuring unit for measuring a measured ductile wavefront ratio or a measured brittle wavefront ratio of a steel material produced in a steel processing facility;
A modeling unit for modeling and calculating an expected ductile wavefront ratio or an expected brittle wavefront ratio of the steel material on the basis of a relevant factor of the steel material; And
An analysis unit for analyzing an abnormal state of the steel processing facility based on the measured ductile wave fracture ratio or a measured value of the measured brittle fracture wave ratio relative to the predicted ductile wave fracture ratio or estimated brittle fracture ratio; A steel process facility monitoring device comprising:
The method according to claim 1,
Wherein the measuring unit measures the measured ductile wave fracture ratio or the measured brittle fracture wave ratio using a Drop Weight Tear Test (DWTT) test method.
3. The method of claim 2,
Wherein the modeling unit analyzes the abnormal state of the steel processing facility by comparing a ratio obtained by subtracting the expected brittle fracture ratio from 100% to the measured soft fracture ratio.
The method according to claim 1,
Wherein the associated factor comprises at least one of a micro deposit related factor, a cleanliness related factor and a grain control factor.
5. The method of claim 4,
Wherein the modeling unit models the predicted ductile wave fracture rate or the predicted brittle fracture wave rate by fixing the temperature of the steel material to a temperature lower than the ductile-brittle transition temperature of the steel material.
delete The facility monitoring apparatus comprising: measuring a measured ductile wavefront ratio or a measured brittle wavefront ratio of a steel material produced in a steel processing facility;
The facility monitoring apparatus modeling and calculating an expected ductile wave fracture rate or a predicted brittle fracture wave rate of the steel material based on the relevant factors of the steel material; And
Analyzing an abnormal condition of the steel processing facility based on the measured ductile wave fracture ratio or a measured value of the measured brittle fracture wave ratio relative to the expected ductile fracture wave rate or the expected brittle fracture wave rate; The method comprising the steps of:
8. The method of claim 7,
The steel processing facility is a facility for a steel plate process for the steel material,
The modeling and calculation may be performed by modeling an expected ductile wavefront ratio or an expected brittle wavefront ratio of the steel material using at least one of microstructure-related factors, cleanliness-related factors, and crystal grain control factors of the steel material,
Wherein the grain control factor comprises at least one of a slab extraction temperature, a finish rolling finish temperature, and an accelerated cooling start temperature.
9. The method of claim 8,
Further comprising the step of changing the finishing rolling finishing temperature of the steel plate process when the facility monitoring apparatus analyzes that an abnormality occurs in the steel processing facility by the analyzing step.
KR1020150178105A 2015-12-14 2015-12-14 Apparatus and method for monitoring facility KR101778392B1 (en)

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KR20190070168A (en) 2017-12-12 2019-06-20 주식회사 포스코 Steel process simulation apparatus and method
WO2020049338A1 (en) * 2018-09-06 2020-03-12 Arcelormittal Method and electronic device for monitoring a manufacturing of a metal product, related computer program and installation

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO1996022575A1 (en) 1995-01-17 1996-07-25 Intertech Ventures, Ltd. Control systems based on simulated virtual models
JP2010112942A (en) 2008-10-10 2010-05-20 Kobe Steel Ltd Method for monitoring of steel structure

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO1996022575A1 (en) 1995-01-17 1996-07-25 Intertech Ventures, Ltd. Control systems based on simulated virtual models
JP2010112942A (en) 2008-10-10 2010-05-20 Kobe Steel Ltd Method for monitoring of steel structure

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