CN113219910A - Full-flow production self-diagnosis and optimization system - Google Patents
Full-flow production self-diagnosis and optimization system Download PDFInfo
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- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
- G05B19/41875—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM] characterised by quality surveillance of production
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- G—PHYSICS
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- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
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- G05B2219/32—Operator till task planning
- G05B2219/32368—Quality control
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- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
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Abstract
The invention relates to a full-flow production self-diagnosis and optimization system, which comprises a data acquisition unit, a database, a data analysis module and a self-diagnosis and evaluation module, wherein the database is connected with the data acquisition unit, and the data analysis module, the self-diagnosis and evaluation module and a data application module are respectively connected to the database and are used for monitoring, diagnosing and optimizing processes, quality and equipment in full-flow production, so that the rapid automatic identification of process defects, the tracing early warning of quality problems and the monitoring of the operation performance of the equipment can be realized; the expansibility is good, and the production self-diagnosis and optimization can be carried out aiming at a single unit or a plurality of units; can meet the requirements of transparence and controllability of the production in the intelligent manufacturing era, and is the foundation of the future digital and intelligent factory construction.
Description
Technical Field
The invention relates to the field of a processing flow management and control system, in particular to a full-flow production self-diagnosis and optimization system.
Background
At present, data of different control systems and management systems in the process industry or the discrete industry are distributed in different platforms, such as L1, L2 and MES systems, the difference between the storage relationship and the logic is large, professional data collection and arrangement work accounts for about 70% of the whole data analysis work, the workload is large, the efficiency is low, and the problems of process failure, equipment abnormity, quality tracing and the like are difficult to find; the problems are only treated afterwards, are not controlled in the process and are not prevented in advance; there is no effective control means in the production process, and the abnormal change observation, the fluctuation rule search, the abnormal phenomenon judgment and the analysis strategy mode are not formed. Meanwhile, a large amount of process parameters and historical data accumulated in production are not fully utilized, rules in the process are not found, the process cannot be continuously optimized, the product quality is improved and stabilized, and data resources are deposited and wasted.
On the other hand, some work is carried out in the flow industry, particularly in the aspect of the whole flow quality control of metal processing in China, and a whole flow quality control system is implemented in some enterprises, so that a certain effect is achieved in the aspect of quality improvement. However, these full-flow quality control systems are usually bulky, long in implementation time and high in cost, for example, in terms of data acquisition, the system needs to be connected to bottom-layer PLCs of different production devices, and after acquisition, correspondence, identification and arrangement of time and spatial relationships are performed on each data variable, and existing completed data acquisition sources and completed acquisition work are not well utilized; sometimes, because some devices lack the support of an L2-level system, the data association index is insufficient, and the cost investment of adding an L2-level system is caused; finally, the full-flow quality system is mainly biased to the diagnosis optimization in the aspect of process quality, and is not related to the aspect of equipment, and the problem of process quality is closely related to the equipment, so that the existing system has defects in problem analysis.
Disclosure of Invention
In order to overcome at least the defects in the prior art, the embodiment of the invention provides a full-flow production self-diagnosis and optimization system, which is simple in structure and convenient to use, can be used for monitoring, diagnosing and optimizing the process, quality and equipment in full-flow production, and can realize quick and automatic identification of process defects, tracing early warning of quality problems and monitoring of equipment operation performance.
The invention relates to a full-flow production self-diagnosis and optimization system, which comprises a data acquisition unit, a database, a data analysis module and a self-diagnosis and evaluation module, wherein the database is connected with the data acquisition unit, and the data analysis module, the self-diagnosis and evaluation module and the data application module are respectively connected to the database, wherein:
the data acquisition unit is used for being connected with a data source of the full-flow equipment or the sensor and reading, preprocessing and temporarily storing corresponding production data or detection data in real time;
the database adopts a lossless data compression algorithm and is used for processing, compressing and storing the production data or the detection data generated in real time according to a time sequence or an event signal, and extracting data characteristic quantity to correlate the full-flow data;
the data analysis module is used for analyzing the production data or experimental data stored in the database aiming at the characteristics of the full-flow production process;
the self-diagnosis and evaluation module is used for embedding user rules, a process performance optimization engine and a spectrum analysis tool aiming at the characteristics of all production equipment in the whole process, and dynamically monitoring, diagnosing, evaluating and early warning the process, the quality and the equipment through the sample learning, the establishment of a defect library and the presetting of parameters or signals by a user.
The system further comprises a data application module, wherein the data application module is used for displaying, exporting and sharing the production data or the detection data in a report form.
Furthermore, the data collector comprises an IBAFILE interface, an ADO/ODBC/JDBC interface, an API interface and an OPC interface, and the data collector supports the communication of Ethernet, RS232 serial ports and Modbus.
Furthermore, the data acquisition unit comprises a light database for caching and preprocessing data, and is also used for monitoring the network state in real time, caching the data in the light database according to a time sequence in the network fault state, detecting the cached data after the fault is eliminated, and continuously reading new data.
Further, the data analysis module comprises an analysis rule setting unit and a process data analysis engine, the analysis rule setting unit is in signal connection with the process data analysis engine, the analysis rule setting unit is used for selecting, adjusting, adding or deleting the data types analyzed by the process data analysis engine according to the user requirements, and the data analysis engine is provided with a key detail analysis unit, a group multi-parameter analysis unit, an SPC process quality analysis unit and a specific function analysis unit.
Further, the self-diagnosis and evaluation module is provided with a spectrum analysis unit which is used for rapidly analyzing the faults of the rolling mill equipment dynamic and the actuating mechanism through Fourier transformation.
Further, the self-diagnosis and evaluation module is provided with an evaluation rule setting unit and an evaluation function model, the self-diagnosis and evaluation module forms a process rule base for key parameters of each quality link based on the evaluation rule setting unit and the evaluation function model, forms a machine language for the process rule base, carries out real-time judgment on each parameter in the production process according to the rules, displays the judgment result on an HMI picture, and automatically alarms quality abnormal events.
Further, the self-diagnosis and evaluation module is provided with a process performance optimization engine which is used for classifying according to production rate, yield, equipment starting rate and energy consumption benchmark to establish a standard sample library.
Further, the self-diagnosis and evaluation module is provided with a machine learning module, the machine learning module is used for calculating and obtaining optimal parameters according to data in the standard sample library, and the optimal parameters are used for subsequent production setting.
Furthermore, the data collector is provided with a detachably connected expansion module, and the expansion module is used for expanding data sources of the devices or the sensors connected with the data collector.
The invention has the advantages that: the invention relates to a full-flow production self-diagnosis and optimization system, which comprises a data acquisition unit, a database, a data analysis module and a self-diagnosis and evaluation module, wherein the database is connected with the data acquisition unit, and the data analysis module, the self-diagnosis and evaluation module and a data application module are respectively connected to the database and are used for monitoring, diagnosing and optimizing processes, quality and equipment in full-flow production, so that the rapid automatic identification of process defects, the tracing early warning of quality problems and the monitoring of the operation performance of the equipment can be realized; the expansibility is good, and the production self-diagnosis and optimization can be carried out aiming at a single unit or a plurality of units; can meet the requirements of transparence and controllability of the production in the intelligent manufacturing era, and is the foundation of the future digital and intelligent factory construction.
In order to make the aforementioned and other objects, features and advantages of the invention comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic view of the overall structure of the present invention.
Fig. 2 is a schematic diagram of a connection structure of a data collector in the embodiment of the present invention.
Fig. 3 is a full-flow material information tracing map.
Fig. 4 is a schematic structural diagram of a data analysis module.
FIG. 5 is a diagram illustrating the effect of group analysis.
Fig. 6 is a schematic diagram of the analysis effect of the data analysis module on the process.
Fig. 7 is a schematic diagram of the internal structure of the self-diagnosis and evaluation module.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, the present invention in a preferred embodiment of the present invention provides an application of a full-flow production self-diagnosis and optimization system in the aluminum processing industry, as shown in fig. 1, including a data collector 1, a self-diagnosis and optimization database 2, a data analysis module 3, a self-diagnosis and evaluation module 4, and a data application module 5.
At present, the basic automation control of most of equipment of aluminum processing enterprises adopts a Siemens PLC system, and an IBA data acquisition system is configured on main rolling equipment, so that the production self-diagnosis and optimization system aiming at the characteristics of the production equipment and the whole process mainly adopts a data acquisition unit comprising an OPC interface and an IBA FILE interface, and the data acquisition unit supports the Siemens S7 protocol, thereby meeting the requirement of acquiring production data by connecting the existing data sources of different production equipment or sensors in the whole process.
Referring to fig. 2, in the above embodiment, the data collector in the production control system of the full-flow production self-diagnosis and optimization system for aluminum processing collects real-time data information (such as IBAPDA) generated by the automatic control system of level L1 through the data recording unit, and is also connected with the system of level L2 and the production management system (MES), and takes required statistical data and material roll information, and provides the MES system with a reference for determining whether each aluminum roll is qualified in key process performance index, so as to help the MES system to determine whether to continue the next processing procedure.
After the data acquisition unit acquires data from different data sources such as hot rolling, cold rolling, foil rolling, annealing and the like, the data can be subjected to edge processing including smooth filtering of signals, validity verification of the data, basic statistic processing (maximum, minimum, average, summation, counting and the like of acquired variables), data identification and the like, and the obvious abnormal data are removed and written into a self-diagnosis and optimization database in real time.
Referring to fig. 3, a large amount of real-time data with small granularity, such as thickness, rolling force, plate shape, convexity, etc., are included in the aluminum production process, and if the real-time data are directly stored in a database, the storage load of the database is increased, thereby affecting the processing and calling of the data. The database adopts a lossless data compression algorithm, processes, compresses and stores the real-time data according to a time sequence or an event signal, and simultaneously extracts the data characteristic quantity to correlate the full-flow data. The characteristics of the whole-flow production of aluminum processing include the front and back and continuity of the production process, the sequence of the production time of the product, the length interval of the product, and the fact that the product is often produced in the upstream and downstream processes by several same characteristic quantities, such as the product order number, the alloy number, the smelting number, the coil number, the blank specification, the production time, the equipment number and the like. According to the input casting blank number, the coil number or the production time, the material data of the coil number, the production actual performance data of the upstream and downstream processes, the process technological parameters, the performance actual performance, the defect data and the like are inquired, and the product quality problem can be comprehensively analyzed and traced. A full-process material information tracing map is constructed, and material pedigree maps of various products and process curve parameters of various processes can be inquired according to time, processes and volume numbers. In the process statistical information screening list, if any product is clicked, a material pedigree diagram of the product can be inquired and displayed in the process flow, all process variables under the selected process are displayed on the right side, and if the variables to be inquired are clicked in a double-click mode, a process curve graph is displayed in the middle, and meanwhile, main concerned variables of each process can be flexibly configured and used for automatically generating curve data.
Referring to fig. 4, in the above embodiment, the system for self-diagnosing and optimizing production in the full process includes a data analysis module, and based on the characteristics of the full-process production process, the key detail analysis, group multi-parameter analysis, SPC process quality analysis, and the like of the process, quality, and equipment data are implemented by the process data analysis engine according to the user settings.
The data analysis module extracts the production data recorded in the database, performs performance analysis of the production process, including analysis of productivity, quality data, equipment efficiency, energy medium, production performance and customer customization, and a user can set the analysis through a process data analysis engine of the data analysis module to meet personalized analysis requirements.
In the above embodiment, the process data analysis engine is embedded with a key detail analysis unit, a group multi-parameter unit, an SPC process quality unit, and a specific function analysis unit, where the key detail analysis unit mainly provides a historical data curve for quickly retrieving a problem product in a full process range when a problem occurs in the product, and associated current production process and equipment state information for further collaborative analysis. The problem of data curve dispersion in each equipment in the past, look up the difficulty, the analysis problem is difficult is solved.
Referring to fig. 5, in the above embodiment, the group multi-parameter unit is developed for three angles, i.e., a single process, a whole process or the same production process within the whole process range, and specifically includes performing batch group analysis of a single or multiple process parameters and quality indexes within a large time span (half a year or a year) from the above angles, displaying the distribution ranges of the process parameters and the quality indexes, and searching abnormal points from the distribution ranges, thereby facilitating quick query of problems. In the group analysis, information such as a corresponding production curve can be inquired by the key detail analysis function aiming at a certain process parameter or quality index. And finding abnormal points in the average rolling force group data of products with the same alloy and the same specification in the batch production of the cold rolling unit, and directly tracing the coil production data corresponding to the rolling force by clicking the abnormal points.
In the above embodiment, the SPC process quality unit mainly aims at some advanced analysis applications required for process, quality, and equipment problem analysis, such as process failure, process parameter optimization, quality fluctuation, and equipment stability control chart analysis in the full-flow production process, and searches for the reasons of abnormal variation and accidental variation to assist the full-flow production optimization.
In the above embodiments, the specific function analysis is directed to general production data analysis, including benchmark and root cause analysis in terms of productivity, yield, equipment start-up rate, energy consumption, and the like. These benchmarks determine the current production and control performance levels, providing assistance for further improvement and promotion. Such as the lateral thickness difference, rolling speed, plate shape, rolling force, tension and the like for several rolling processes, and the temperature, heating time, holding time and the like for a heat treatment process.
Referring to fig. 6, the data analysis module can automatically calculate the rolling time under acceleration, steady state and deceleration conditions for comparison of the rolling speed data of the final pass of hot rolling, cold rolling and foil rolling to which the selected coil is subjected, thereby analyzing the production efficiency and the cause of speed abnormality.
Referring to fig. 7, in the above embodiment, the production self-diagnosis and optimization system of the full process includes a self-diagnosis and evaluation module, which is embedded with user rules, a process performance optimization engine and a spectrum analysis tool, mainly aiming at the characteristics of each production device of the full process, and dynamically monitors, diagnoses, evaluates and warns the process, quality and devices by sample learning, establishing a defect library and presetting parameters or signals by a user.
The process monitoring based on the user rules forms a process rule base for key parameters of each quality link through production experience and an evaluation function model, forms the process rule base into a machine language, judges the difference of each parameter of the production process in real time according to the rules, displays the difference judgment result on an HMI picture, and automatically alarms quality abnormal events.
The process performance optimization engine is mainly classified according to production rate, yield, equipment starting rate, energy consumption and other benchmarks, the benchmarks can be determined according to excellent, good, common and unqualified benchmarks, a process, equipment and quality defect standard sample library is established, data in the standard sample library are calculated through a customized module or an intelligent machine learning unit, so that optimal parameters are calculated, and the optimal parameters are used for subsequent production setting, so that the performance of a unit can be continuously improved. For example, the optimal parameters of aluminum processing include the set values of the process data of hot rolling, cold rolling and foil rolling (rolling force, bending force, roll gap, tension, speed, coolant flow, concentration and the like) and the actions of manual operation (threading/strip-receiving time, bending, maximum rolling speed, primary acceleration and deceleration time and the like), and the production quality, the production efficiency or the energy consumption ratio are continuously improved by continuously adjusting the parameters.
The spectrum analysis tool is provided with a Fourier transform professional tool, and can be used for quickly analyzing faults of rolling mill equipment dynamics and an actuating mechanism, such as performance stability analysis of common sensors such as a tensiometer, a pressure sensor and a position sensor, and performance analysis of equipment such as rolling mill bounce, roller eccentricity, roller gap frequency response and servo valve drift.
The production self-diagnosis and optimization system of the whole process comprises a data application module, is used for the visual display, report derivation, data sharing and the like of the production data and KPI of the whole process, and can be used as the output display of intelligent centralized control management.
The production self-diagnosis and optimization system of the whole process is provided with an expansion module which is detachably connected to a data acquisition unit, and can carry out increase and decrease configuration on units or processes in the system according to requirements, so that the production self-diagnosis and optimization of a single unit or a plurality of units are met. If only the rolling process such as hot rolling, cold rolling, foil rolling process is aimed at, the production self-diagnosis and optimization system of the whole flow can be made to be self-diagnosis and optimization individually for the specific process.
The principle and the implementation mode of the invention are explained by applying specific embodiments in the invention, and the description of the embodiments is only used for helping to understand the method and the core idea of the invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.
Claims (10)
1. A full-flow production self-diagnosis and optimization system is characterized by comprising a data acquisition unit, a database, a data analysis module and a self-diagnosis and evaluation module, wherein the database is connected with the data acquisition unit, and the data analysis module, the self-diagnosis and evaluation module and the data application module are respectively connected to the database, wherein:
the data acquisition unit is used for being connected with a data source of the full-flow equipment or the sensor and reading, preprocessing and temporarily storing corresponding production data or detection data in real time;
the database adopts a lossless data compression algorithm and is used for processing, compressing and storing the production data or the detection data generated in real time according to a time sequence or an event signal, and extracting data characteristic quantity to correlate the full-flow data;
the data analysis module is used for analyzing the production data or experimental data stored in the database aiming at the characteristics of the full-flow production process;
the self-diagnosis and evaluation module is used for embedding user rules, a process performance optimization engine and a spectrum analysis tool aiming at the characteristics of all production equipment in the whole process, and dynamically monitoring, diagnosing, evaluating and early warning the process, the quality and the equipment through the sample learning, the establishment of a defect library and the presetting of parameters or signals by a user.
2. The full-process production self-diagnosis and optimization system according to claim 1, further comprising a data application module, wherein the data application module is used for report display, export and sharing of production data or detection data.
3. The full-flow production self-diagnosis and optimization system according to claim 1, wherein the data collector comprises an IBA FILE interface, an ADO/ODBC/JDBC interface, an API interface, and an OPC interface, and supports ethernet, RS232 serial port, and Modbus communication.
4. The full-flow production self-diagnosis and optimization system according to claim 1, wherein the data collector comprises a light database for caching and preprocessing data, and is further configured to monitor a network state in real time, cache the data in the light database in time series in a network failure state, and enable detection of the cached data after the failure is eliminated, and continue reading new data.
5. The full-flow production self-diagnosis and optimization system according to claim 1, wherein the data analysis module comprises an analysis rule setting unit and a process data analysis engine, the analysis rule setting unit is in signal connection with the process data analysis engine, the analysis rule setting unit is used for selecting, adjusting, adding or deleting data types analyzed by the process data analysis engine according to user requirements, and the data analysis engine is provided with a key detail analysis unit, a group multi-parameter analysis unit, an SPC process quality analysis unit and a specific function analysis unit.
6. The full-flow production self-diagnosis and optimization system according to claim 1, wherein the self-diagnosis and evaluation module has a spectrum analysis unit for fast analysis of rolling mill plant dynamics and actuator failures by fourier transform.
7. The full-flow production self-diagnosis and optimization system according to claim 1, wherein the self-diagnosis and evaluation module has an evaluation rule setting unit and an evaluation function model, and the self-diagnosis and evaluation module forms a process rule base for key parameters of each quality link based on the evaluation rule setting unit and the evaluation function model, forms the process rule base into a machine language, makes real-time difference judgment for each parameter of the production process according to the rules, displays the difference judgment result on an HMI picture, and automatically alarms quality abnormal events.
8. The full-flow production self-diagnostic and optimization system of claim 1 wherein the self-diagnostic and evaluation module has a process performance optimization engine for building a standard sample library by sorting on the basis of production rate, yield, equipment start-up rate, energy consumption.
9. The full-flow production self-diagnosis and optimization system of claim 8, wherein the self-diagnosis and evaluation module has a machine learning module for calculating optimal parameters for subsequent production settings according to data in a standard sample library.
10. The full-flow production self-diagnosis and optimization system according to claim 1, wherein the data collector has a detachably connected expansion module for expanding data sources of devices or sensors connected to the data collector.
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| CN114662208A (en) * | 2022-05-19 | 2022-06-24 | 安徽智享云科技有限公司 | Construction visualization system and method based on Bim technology |
| CN114819575A (en) * | 2022-04-18 | 2022-07-29 | 中国兵器装备集团自动化研究所有限公司 | Quality analysis and optimization method and system for multi-time scale data fusion |
| CN116128102A (en) * | 2022-12-09 | 2023-05-16 | 常熟中佳新材料有限公司 | Production optimization method and system for special-shaped copper pipe for refrigeration equipment |
| CN116846161A (en) * | 2023-07-04 | 2023-10-03 | 湖南贝特新能源科技有限公司 | Motor stator production system |
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101807265A (en) * | 2008-09-30 | 2010-08-18 | 洛克威尔自动控制技术股份有限公司 | System and method for dynamic multi-objective optimization of machine selection, integration and utilization |
| CN205384498U (en) * | 2016-03-01 | 2016-07-13 | 深圳制云科技有限公司 | PLC data collection station |
| CN107042234A (en) * | 2017-03-15 | 2017-08-15 | 中冶华天工程技术有限公司 | The intelligent production line and production method gathered based on bar whole process big data |
| CN107491044A (en) * | 2016-06-09 | 2017-12-19 | 罗克韦尔自动化技术公司 | Expansible analysis framework for automatic control system |
| WO2020245374A1 (en) * | 2019-06-05 | 2020-12-10 | Abb Power Grids Switzerland Ag | Method and device for facilitating storage of data from an industrial automation control system or power system |
| CN112329953A (en) * | 2020-10-29 | 2021-02-05 | 中冶南方（武汉）热工有限公司 | Full-process intelligent diagnosis and optimized operation system for industrial furnace |
-
2021
- 2021-03-19 CN CN202110296596.4A patent/CN113219910A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101807265A (en) * | 2008-09-30 | 2010-08-18 | 洛克威尔自动控制技术股份有限公司 | System and method for dynamic multi-objective optimization of machine selection, integration and utilization |
| CN205384498U (en) * | 2016-03-01 | 2016-07-13 | 深圳制云科技有限公司 | PLC data collection station |
| CN107491044A (en) * | 2016-06-09 | 2017-12-19 | 罗克韦尔自动化技术公司 | Expansible analysis framework for automatic control system |
| CN107042234A (en) * | 2017-03-15 | 2017-08-15 | 中冶华天工程技术有限公司 | The intelligent production line and production method gathered based on bar whole process big data |
| WO2020245374A1 (en) * | 2019-06-05 | 2020-12-10 | Abb Power Grids Switzerland Ag | Method and device for facilitating storage of data from an industrial automation control system or power system |
| CN112329953A (en) * | 2020-10-29 | 2021-02-05 | 中冶南方（武汉）热工有限公司 | Full-process intelligent diagnosis and optimized operation system for industrial furnace |
Non-Patent Citations (2)
| Title |
|---|
| 卢锐等: "轧机工况在线监测装置的开发与应用", 《山东冶金》 * |
| 岳临萍等: "济钢中厚板厂粗轧机工况在线监测系统的应用实践", 《冶金自动化》 * |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113962507A (en) * | 2021-09-03 | 2022-01-21 | 浙江中控技术股份有限公司 | Intelligent convenient industry-wide data analysis system and method based on SPC |
| CN114819575A (en) * | 2022-04-18 | 2022-07-29 | 中国兵器装备集团自动化研究所有限公司 | Quality analysis and optimization method and system for multi-time scale data fusion |
| WO2023218619A1 (en) * | 2022-05-12 | 2023-11-16 | 東芝三菱電機産業システム株式会社 | Rolling productivity improvement assistance device |
| JP7509321B2 (en) | 2022-05-12 | 2024-07-02 | 株式会社Tmeic | Rolling productivity improvement support device |
| CN114662208A (en) * | 2022-05-19 | 2022-06-24 | 安徽智享云科技有限公司 | Construction visualization system and method based on Bim technology |
| CN114662208B (en) * | 2022-05-19 | 2022-08-05 | 安徽智享云科技有限公司 | Construction visualization system and method based on Bim technology |
| CN116128102A (en) * | 2022-12-09 | 2023-05-16 | 常熟中佳新材料有限公司 | Production optimization method and system for special-shaped copper pipe for refrigeration equipment |
| CN116128102B (en) * | 2022-12-09 | 2024-01-23 | 常熟中佳新材料有限公司 | Production optimization method and system for special-shaped copper pipe for refrigeration equipment |
| CN116846161A (en) * | 2023-07-04 | 2023-10-03 | 湖南贝特新能源科技有限公司 | Motor stator production system |
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