CN113569445A - Steel structure health monitoring system and method based on digital twinning technology - Google Patents
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Abstract
The invention discloses a steel structure health monitoring system based on a digital twinning technology, which comprises a distributed data acquisition system and a remote data transmission and control system; the remote data transmission and control system comprises a local computer system, a remote management monitoring computer system and a steel structure safety performance prediction module; the steel structure safety performance prediction module comprises a steel structure digital twin model, a finite element simulation analysis model and a load identification module, wherein the steel structure digital twin model corresponds to the finite element simulation analysis model. The invention constructs a scientific and full-life-cycle steel structure management system, and provides scientific and normative guidance for the design of measuring point arrangement, data processing, calculation comparison, load identification, fatigue evaluation and maintenance schemes. The method can realize long-term tracking monitoring of the overall damage of the steel structure, greatly expands the connotation of the steel structure detection field and improves the reliability of prediction evaluation.
Description
Technical Field
The invention relates to the field of steel structure monitoring, in particular to a steel structure health monitoring method, and particularly relates to a steel structure health monitoring system and method based on a digital twinning technology.
Background
Structural Health Monitoring (SHM) refers to the detection of structural damage or degradation by analyzing structural system characteristics, including structural response, using in-situ non-destructive sensing techniques. The traditional structural health monitoring technology comprises three parts: sensor research and development, a data acquisition system and signal processing and evaluation. The method can achieve real-time and accurate early warning of the monitoring point, but cannot early warn the health state of the whole structure. In addition, the traditional method for realizing accurate monitoring and evaluation of the whole health of the structure has high requirements on the accuracy, the installation quantity and the signal evaluation of the sensor, and the cost of the traditional method cannot be popularized.
The digital twinning technique can effectively solve the above problems. The definition of the digital twin is to fully utilize data such as a physical model, sensor updating, operation history and the like, integrate a multidisciplinary, multi-physical quantity, multi-scale and multi-probability simulation process and complete mapping in a virtual space so as to reflect the full life cycle process of corresponding entity equipment. The digital twin technology adopts a digital mode to create a real-time mirror image of a physical entity in a digital space, and the state and the behavior of the physical entity in a real physical environment can be simulated, monitored, diagnosed and controlled through information interaction of the physical entity and a digital model.
Chinese patent CN202011276245.9 provides a modular building health monitoring system based on a digital twin platform, and the health monitoring problem of the whole structure is solved by using a digital twin technology. But this system does not implement a high quality digital model and analysis and prediction bias may occur.
Disclosure of Invention
In order to effectively solve the technical problems, the invention provides a steel structure health monitoring system based on a digital twinning technology.
In order to achieve the purpose, the invention adopts the technical scheme that: a steel structure health monitoring system based on a digital twin technology comprises a distributed data acquisition system and a remote data transmission and control system; the remote data transmission and control system comprises a local computer system, a remote management monitoring computer system and a steel structure safety performance prediction module; the distributed acquisition system comprises sensors arranged on a steel structure to be detected, dynamically monitors and acquires data acquired by each sensor on line, and transmits the acquired data to the remote management monitoring computer system through a network; the local computer system is connected with the remote management monitoring computer system through a network to collect the collected data, and the local computer system stores the collected data in a local database; the remote management monitoring computer system is connected with and communicates with the steel structure safety performance prediction module; the steel structure safety performance prediction module comprises a steel structure digital twin model, a finite element simulation analysis model, a load identification module and a damage identification module, wherein the steel structure digital twin model comprises an integral structure and the material attribute of each component, the steel structure digital twin model and the acquired data are integrated, and the current constraint and load state of the steel structure is determined through the load identification module; applying the current constraint and the load state to the steel structure digital twin model for finite element calculation analysis to obtain a corresponding finite element simulation analysis model; and carrying out fault location for inversion through the damage identification module so as to accurately predict the structural response.
Further, the sensors comprise a structural load type sensor and an environment monitoring type sensor; and the acquired data is sent to the remote management monitoring computer system through the Ethernet.
Furthermore, the steel structure safety performance prediction module further comprises a complex large-scale model processing module, a finite element model correction module and an experimental model fusion module; and obtaining a fine structure value of the initial structure value of the finite element simulation analysis model through the finite element model correction module and the experiment model fusion module.
Furthermore, the remote data transmission and control system further comprises a three-dimensional model and a visualization module, and the acquired data is dynamically displayed in a three-dimensional visualization mode through the three-dimensional model and the visualization module.
Furthermore, the remote data transmission and control system also comprises an alarm module; the alarm module is respectively connected with the local computer system and the remote management monitoring computer system.
On the other hand, the invention also provides a steel structure health monitoring method based on the digital twinning technology, which comprises the following steps: the distributed acquisition system carries out dynamic online monitoring and acquisition on the data acquired by each sensor and transmits the acquired data to the remote management monitoring computer system through a network; the local computer system is connected with the remote management monitoring computer system through a network to collect the collected data, and the local computer system stores the collected data in a local database; the remote management monitoring computer system sends the acquired data acquired in real time to the steel structure safety performance prediction module and receives a processing result of the steel structure safety performance prediction module; the steel structure digital twin model in the steel structure safety performance prediction module comprises an integral structure and material attributes of each component, the steel structure digital twin model and the collected data are collected, and the current constraint and load state of the steel structure is determined through the load identification module; applying the current constraint and the load state to the steel structure digital twin model for finite element calculation analysis to obtain a corresponding finite element simulation analysis model; and carrying out inversion fault positioning through the damage identification module so as to accurately predict structural response and judge failure. And evaluating the performance state of the actual structure according to the mapping relation between the steel structure digital twin model and the actual structure through the analysis and calculation of the steel structure digital twin model.
Further, the steel structure health monitoring method based on the digital twinning technology uses a finite element simulation analysis model refinement establishing method, and comprises a complex large-scale model processing technology, a finite element model correction technology and an experimental model fusion technology; and converting the initial structure numerical value of the finite element simulation analysis model into a fine structure numerical value through the finite element model correction technology and the experimental model fusion technology, and constructing the finite element simulation analysis model with the fine structure numerical value.
Further, according to the steel structure health monitoring method based on the digital twinning technology, the collected data are dynamically displayed in a three-dimensional visual mode.
Further, in the steel structure health monitoring method based on the digital twinning technology, the applied sensors comprise a structural load sensor and an environment monitoring sensor; and the acquired data is sent to the remote management monitoring computer system through the Ethernet.
Further, in the steel structure health monitoring method based on the digital twin technology, the local computer system and the remote management monitoring computer system are respectively connected with the alarm module; when the local computer system finds that the acquired data is abnormal, starting the alarm module; and when the remote management monitoring computer system receives that the processing result of the steel structure safety performance prediction module is abnormal, starting the alarm module.
The invention has the beneficial effects that:
the steel structure health monitoring system based on the digital twin technology constructs a scientific steel structure management system with a full life cycle, and provides scientific and normative guidance for the design of measuring point arrangement, data processing, calculation comparison, load identification, fatigue evaluation and maintenance schemes. Simultaneously provides data analysis with all-round and expansibility. The method can realize long-term tracking monitoring of the overall damage of the steel structure, is beneficial supplement to local and short-term damage diagnosis technology, greatly expands the connotation of the steel structure detection field, and improves the reliability of prediction and evaluation.
(1) The steel structure health monitoring system based on the digital twin technology is a comprehensive monitoring system which integrates the subfunction systems of optimized combined structure monitoring, environment monitoring, equipment monitoring, damage identification, comprehensive alarming, information network analysis and processing and the like by applying modern sensing, communication and network technologies.
(2) The core method of the steel structure health monitoring system based on the digital twin technology is that the detection equipment is utilized to obtain the current strain and other information of the equipment, then the twin model is used for load identification, and finally the fault early warning and positioning of the whole structure are carried out according to finite element simulation analysis and damage identification.
(3) The steel structure health monitoring system based on the digital twin technology increases the damage identification and fault early warning functions of the whole structure simulation model on the basis of processing and evaluating signals.
(4) The steel structure health monitoring system based on the digital twin technology adopts the model correction technology, corrects the finite element simulation model based on the measured data, and improves the simulation analysis precision, thereby improving the precision of monitoring evaluation and early warning.
Drawings
In order to better express the technical scheme of the invention, the following drawings are used for explaining the invention:
FIG. 1 is a schematic diagram of a system architecture according to an embodiment;
FIG. 2 is a schematic structural diagram of a second system according to the embodiment;
the reference numbers illustrate: 1. the system comprises a distributed acquisition system, 11, a sensor, 2, a remote data transmission and control system, 21, a local computer system, 22, a remote management monitoring computer system, 23, a steel structure safety performance prediction module, 231, a steel structure digital twin model, 232, a finite element simulation analysis model, 233, a load identification module, 234, a damage identification module, 235, a complex large-scale model processing module, 236, a finite element model correction module, 237, an experimental model fusion module, 24, a three-dimensional model and visualization module, 25 and an alarm module.
Detailed Description
Preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only for explaining the technical principle of the present invention, and do not limit the scope of the present invention.
It should be noted that in the description of the present invention, the terms of direction or positional relationship indicated by the terms "upper", "lower", "left", "right", "inner", "outer", etc. are based on the directions or positional relationships shown in the drawings, which are only for convenience of description, and do not indicate or imply that the device or element must have a specific orientation, be constructed in a specific orientation, and be operated, and thus, should not be construed as limiting the present invention.
Furthermore, it should be noted that, in the description of the present invention, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
In the first embodiment, referring to fig. 1, a steel structure health monitoring system and method based on a digital twin technology comprises a distributed data acquisition system (1) and a remote data transmission and control system (2); the remote data transmission and control system (2) comprises a local computer system (21), a remote management monitoring computer system (22), a steel structure safety performance prediction module (23), a three-dimensional model and visualization module (24) and an alarm module (25); the distributed acquisition system (1) comprises a plurality of structural load sensors (11) and environmental monitoring sensors (11) which are arranged on a steel structure to be detected, the distributed acquisition system (1) carries out dynamic online monitoring and acquisition on data acquired by each sensor (11), and the acquired data are uploaded to a remote management monitoring computer system (22) through a 5G network; the local computer system (21) is connected with the remote management monitoring computer system (22) through a network, the collected data are collected through the Ethernet, and the local computer system (21) stores the collected data in a local database; the remote management monitoring computer system (22) is connected with and communicates with the steel structure safety performance prediction module (23), the remote management monitoring computer system (22) sends real-time acquired data to the steel structure safety performance prediction module (23) and receives a processing result of the steel structure safety performance prediction module (23); the steel structure safety performance prediction module (23) comprises a steel structure digital twin model (231), a finite element simulation analysis model (232), a load identification module (233) and a damage identification module (234), wherein the steel structure digital twin model (231) comprises an integral structure and material properties of each part, the steel structure digital twin model (231) is combined with collected data, the real-time load state of the structure is obtained through a load identification module (233) and is applied to a steel structure digital twin model (231) for finite element calculation analysis to obtain a corresponding finite element simulation analysis model (232), the fault location based on inversion is carried out through a damage identification module (234) to judge failure, and through the analysis and calculation of a steel structure digital twin model (231), and evaluating the performance state of the actual structure according to the mapping relation between the steel structure digital twin model (231) and the actual structure.
The local computer system (21) and the remote management monitoring computer system (22) are respectively connected with the three-dimensional model and visualization module (24) and the alarm module (25). Collected data stored in a database of the local computer system (21) can be dynamically displayed in the form of texts and curves through a three-dimensional model and a visualization module (24), and if the collected data is found to have problems, a communication alarm module (25) starts alarm; the steel structure digital twin model (231) can also be displayed through the three-dimensional model and the visualization module (24), if system damage occurs, the remote management monitoring computer system (22) receives the abnormal processing result of the steel structure safety performance prediction module (23) to perform fault location, and the communication alarm module (25) starts alarm.
The second embodiment is shown in fig. 2, and the difference between the system and the method for monitoring the health of the steel structure based on the digital twin technology and the first embodiment is that a method for creating a finite element simulation analysis model in a refined mode is used, and the steel structure safety performance prediction module (23) further comprises a complex large-scale model processing module (235), a finite element model correction module (236) and an experiment model fusion module (237); the initial structure value of the finite element simulation analysis model (232) is converted into a fine structure value through a finite element model modification module (236) and an experimental model fusion module (237), and the finite element simulation analysis model (232) with the fine structure value is obtained. And fault positioning based on inversion is carried out through a damage identification module (234) so as to accurately predict structural response and judge failure, and the performance state of the actual structure is evaluated according to the mapping relation between the steel structure digital twin model (231) and the actual structure through the analysis and calculation of the steel structure digital twin model (231).
According to the steel structure health monitoring system based on the digital twinning technology, the engineering and scientific computing software platform SiPESC is preferably adopted, the multi-level substructure technology is utilized, finite element simulation analysis is carried out, the computing scale of a finite element simulation model (232) is effectively reduced on the premise of ensuring the analysis precision, and the monitoring evaluation and early warning delay is reduced.
The above-mentioned embodiments only express several embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention.
Claims (10)
1. A steel structure health monitoring system based on a digital twin technology is characterized by comprising a distributed data acquisition system and a remote data transmission and control system; the remote data transmission and control system comprises a local computer system, a remote management monitoring computer system and a steel structure safety performance prediction module; the distributed acquisition system comprises sensors arranged on a steel structure to be detected, dynamically monitors and acquires data acquired by each sensor on line, and transmits the acquired data to the remote management monitoring computer system through a network; the local computer system is connected with the remote management monitoring computer system through a network to collect the collected data, and the local computer system stores the collected data in a local database; the remote management monitoring computer system is connected with and communicates with the steel structure safety performance prediction module; the steel structure safety performance prediction module comprises a steel structure digital twin model, a finite element simulation analysis model, a load identification module and a damage identification module, wherein the steel structure digital twin model comprises an integral structure and material attributes of each component, and corresponds to the finite element simulation analysis model.
2. The steel structure health monitoring system based on the digital twinning technology is characterized in that the sensors comprise a structural load sensor and an environment monitoring sensor; and the acquired data is sent to the remote management monitoring computer system through the Ethernet.
3. The system of claim 1, wherein the steel structure safety performance prediction module further comprises a complex large-scale model processing module, a finite element model modification module and an experimental model fusion module.
4. The steel structure health monitoring system based on the digital twin technology as claimed in claim 1, wherein the remote data transmission and control system further comprises a three-dimensional model and a visualization module.
5. The steel structure health monitoring system based on the digital twin technology as claimed in claim 1, wherein the remote data transmission and control system further comprises an alarm module; the alarm module is respectively connected with the local computer system and the remote management monitoring computer system.
6. A steel structure health monitoring method based on a digital twinning technology is characterized in that the steel structure health monitoring system based on the digital twinning technology of any one of claims 1 to 5 is used, the distributed acquisition system carries out dynamic online monitoring and acquisition on the acquired data of each sensor, and the acquired data is sent to the remote management monitoring computer system through a network; the local computer system is connected with the remote management monitoring computer system through a network to collect the collected data, and the local computer system stores the collected data in a local database; the remote management monitoring computer system sends the acquired data acquired in real time to the steel structure safety performance prediction module and receives a processing result of the steel structure safety performance prediction module; the steel structure digital twin model in the steel structure safety performance prediction module comprises an integral structure and material attributes of each component, the steel structure digital twin model and the collected data are collected, and the current constraint and load state of the steel structure is determined through the load identification module; applying the current constraint and the load state to the steel structure digital twin model for finite element calculation analysis to obtain a corresponding finite element simulation analysis model; and carrying out inversion fault positioning through the damage identification module, and judging failure.
7. The method for monitoring the health of the steel structure based on the digital twinning technology is characterized in that a finite element simulation analysis model refinement creation method is used, and the method comprises a complex large-scale model processing technology, a finite element model correction technology and an experimental model fusion technology; and converting the initial structure numerical value of the finite element simulation analysis model into a fine structure numerical value through the finite element model correction technology and the experimental model fusion technology.
8. The method for monitoring the health of the steel structure based on the digital twinning technology as claimed in claim 6, wherein the collected data is dynamically displayed in a three-dimensional visualization manner.
9. The method for monitoring the health of a steel structure based on the digital twinning technology as claimed in claim 6, wherein the applied sensors comprise a structural load sensor and an environmental monitoring sensor; and the acquired data is sent to the remote management monitoring computer system through the Ethernet.
10. The steel structure health monitoring method based on the digital twin technology as claimed in claim 6, wherein the local computer system and the remote management monitoring computer system are respectively connected with the alarm module; when the local computer system finds that the acquired data is abnormal, starting the alarm module to alarm; and when the remote management monitoring computer system receives that the processing result of the steel structure safety performance prediction module is abnormal, the alarm module is started to give an alarm.
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Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114143742A (en) * | 2021-12-30 | 2022-03-04 | 交通运输部水运科学研究所 | General gantry crane intelligent monitoring system based on digital twin |
| CN114166269A (en) * | 2021-11-07 | 2022-03-11 | 中国水利水电第七工程局有限公司 | Construction safety monitoring system adjacent to existing building rain and sewage pipe ditch based on digital twinning |
| CN114325081A (en) * | 2021-12-29 | 2022-04-12 | 润建股份有限公司 | Non-invasive load identification method based on multi-modal characteristics |
| CN114626278A (en) * | 2022-05-16 | 2022-06-14 | 湖南大学 | Method for evaluating structural performance and monitoring health of power battery bracket |
| CN114757088A (en) * | 2022-03-08 | 2022-07-15 | 北京中电飞华通信有限公司 | Energy equipment monitoring system and method based on digital twinning |
| CN114997466A (en) * | 2022-05-06 | 2022-09-02 | 广东无限阵列科技有限公司 | Digital twin technology-based water and soil loss prevention and control responsibility range failure prediction method |
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| CN117992803A (en) * | 2024-04-03 | 2024-05-07 | 江西千策信息工程有限公司 | Multi-scale combination monitoring method and system based on steel structure health |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20170286572A1 (en) * | 2016-03-31 | 2017-10-05 | General Electric Company | Digital twin of twinned physical system |
| CN111881495A (en) * | 2020-06-24 | 2020-11-03 | 北京工业大学 | Prestress steel structure safety assessment method based on digital twinning |
| CN112381674A (en) * | 2020-11-16 | 2021-02-19 | 同济大学 | Modular building health monitoring system based on digital twin platform |
| CN113051838A (en) * | 2021-05-12 | 2021-06-29 | 南京航空航天大学 | Reliable life prediction method for space on-orbit manufacturing structure based on digital twinning technology |
-
2021
- 2021-07-01 CN CN202110750364.1A patent/CN113569445A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20170286572A1 (en) * | 2016-03-31 | 2017-10-05 | General Electric Company | Digital twin of twinned physical system |
| CN111881495A (en) * | 2020-06-24 | 2020-11-03 | 北京工业大学 | Prestress steel structure safety assessment method based on digital twinning |
| CN112381674A (en) * | 2020-11-16 | 2021-02-19 | 同济大学 | Modular building health monitoring system based on digital twin platform |
| CN113051838A (en) * | 2021-05-12 | 2021-06-29 | 南京航空航天大学 | Reliable life prediction method for space on-orbit manufacturing structure based on digital twinning technology |
Cited By (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114166269A (en) * | 2021-11-07 | 2022-03-11 | 中国水利水电第七工程局有限公司 | Construction safety monitoring system adjacent to existing building rain and sewage pipe ditch based on digital twinning |
| CN114166269B (en) * | 2021-11-07 | 2023-09-08 | 中国水利水电第七工程局有限公司 | Digital twinning-based safety monitoring system for construction of rain and sewage pipe ditches of adjacent existing buildings |
| CN114325081A (en) * | 2021-12-29 | 2022-04-12 | 润建股份有限公司 | Non-invasive load identification method based on multi-modal characteristics |
| CN114143742A (en) * | 2021-12-30 | 2022-03-04 | 交通运输部水运科学研究所 | General gantry crane intelligent monitoring system based on digital twin |
| CN114757088A (en) * | 2022-03-08 | 2022-07-15 | 北京中电飞华通信有限公司 | Energy equipment monitoring system and method based on digital twinning |
| CN114997466B (en) * | 2022-05-06 | 2023-04-07 | 广东无限阵列科技有限公司 | Digital twinning technology-based water and soil loss prevention and control responsibility range failure prediction method |
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