CN116090242A - Digital twin system for pipeline erosion prediction and safety evaluation - Google Patents
Digital twin system for pipeline erosion prediction and safety evaluation Download PDFInfo
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- CN116090242A CN116090242A CN202310128490.2A CN202310128490A CN116090242A CN 116090242 A CN116090242 A CN 116090242A CN 202310128490 A CN202310128490 A CN 202310128490A CN 116090242 A CN116090242 A CN 116090242A
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- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/02—Reliability analysis or reliability optimisation; Failure analysis, e.g. worst case scenario performance, failure mode and effects analysis [FMEA]
Abstract
The invention provides a digital twin system for predicting pipeline erosion and evaluating safety, which comprises a sensing layer, a transmission layer, a platform layer and an application layer, wherein the sensing layer is used for sensing the erosion of the pipeline; the sensing layer is used for collecting static data and dynamic data of the pipeline; the transmission layer is used for sensing data sharing among the layers, transmitting data to the platform layers and sharing the data among the platform layers; the platform layer is used for establishing a digital twin virtual entity; and the application layer performs intelligent decisions such as fault identification, failure prediction and the like on the erosion condition of the pipeline based on the analysis of the data of the platform layer and feeds back the intelligent decisions to the optimal virtual model of the platform layer. According to the digital twin system provided by the invention, from the perspective of pipeline erosion, the full life cycle sensing layer, the transmission layer, the platform layer and the application layer of the covered pipeline are built, so that the fault identification and failure prediction of the pipeline erosion condition are realized, and the safety performance of the pipeline is judged.
Description
Technical Field
The invention belongs to the technical field of pipeline systems, and particularly relates to a digital twin system for pipeline erosion prediction and safety evaluation.
Background
Erosion corrosion is a destructive behavior of a metallic material caused by flowing corrosive media, which is the result of a combination of mechanical and chemical erosion, which, due to the synergistic effect between the two, tends to result in a material loss greater than that caused by single erosion and mechanical erosion. Practice proves that the service life of some corrosion-resistant metal materials in an erosion corrosion environment is greatly shortened. Pipeline material damage caused by erosion corrosion is widely applied to the fields of petrochemical industry, ore exploitation, marine industry and the like. The long-distance oil and gas transportation pipeline has complex operation conditions, frequent accidents and erosion corrosion, and is one of the typical problems causing the local failure of the pipeline.
At present, pipeline erosion prediction and safety evaluation are based on a pipeline integrity management PIM (Pipeline Integrity Management) technology, a pipeline company identifies and technically evaluates risk factors faced in pipeline operation according to continuously-changed pipeline factors, corresponding risk control countermeasures are formulated, and identified adverse influence factors are continuously improved, so that the risk level of pipeline operation is controlled within a reasonable and acceptable range, and pipeline integrity information combined with professional management is obtained through various modes such as monitoring, detection and inspection, and main threat factors which can cause pipeline failure are detected and inspected, so that the pipeline applicability is evaluated, and the purposes of continuously improving, reducing and preventing pipeline accidents and guaranteeing pipeline safe operation are finally achieved. However, the pipeline integrity management PIM still depends on periodic inspection, and cannot display the running state of the whole pipeline in real time, so that the pipeline erosion prediction and safety evaluation have poor timeliness, and the safe running of the pipeline cannot be ensured.
The digital twin builds the virtual mapping of the physical entity in a digital mode, and achieves interactive feedback, data fusion and decision optimization of the physical entity and the virtual mapping in the whole life cycle. The digital twin has the advantages that the state of the physical entity is monitored in real time, dynamically simulated and information expanded by utilizing virtual mapping, dynamic and high-reliability rapid prediction of various parameters, behaviors and the like of the physical entity is realized by combining operation history data and an artificial intelligent algorithm, and finally, behavior correction or optimization guidance of the physical entity is completed through multisource information feedback and system evaluation decision. Digital twin research aiming at the key performance of the internal structure of a product is not available at present, and particularly, a solution for real-time analog simulation and dynamic prediction maintenance of the internal structural performance of important equipment or components based on digital twin is not available.
The digital twinning technique can track all information of the corrosion monitoring system, and use the information to assist in deciding the corrosion and protection process. In view of the huge loss caused by erosion corrosion, the importance of corrosion monitoring and corrosion protection is increasingly highlighted, and the high-new technologies such as high-throughput data acquisition and transmission, the Internet of things and the like are utilized to promote corrosion monitoring and protection research, so that the integrated utilization of various information is a trend. Therefore, it is necessary to design a digital twin prediction system and method for pipeline flush corrosion.
CN 114778425a discloses a pipeline charge corrosion digital twin prediction system and method, the system comprises a transparent water tank, a centrifugal pump, a check valve, a flow rate sensor, a pressure sensor, a test elbow, a mobile workstation, a temperature sensor, a visual pipeline, a database platform, a screw, a reference electrode, an auxiliary electrode, a working electrode, a nut block and an electrochemical workstation, wherein the water outlet end and the water inlet end of the transparent water tank form a closed loop waterway through the visual pipeline. The prediction system provided by the patent can only reflect the whole corrosion process condition according to the real-time and visual characteristics of digital twinning, but can not reflect real data based on an actual pipeline, so that a prediction result has a certain deviation.
Therefore, providing a model framework to realize real-time sharing of virtual data and actual pipeline data so as to accurately obtain erosion condition, early warning and other information and judge the safety performance of the pipeline has become one of the problems to be solved in the art.
Disclosure of Invention
The invention aims to provide a digital twin system for predicting pipeline erosion and evaluating safety, which is based on digital twin, realizes real-time sharing of virtual data and actual pipeline data, is more beneficial to realizing state monitoring and safety evaluation on pipeline erosion conditions and gives out residual service life evaluation.
In order to achieve the aim of the invention, the invention adopts the following technical scheme:
the invention provides a digital twin system for predicting pipeline erosion and evaluating safety, which comprises a sensing layer, a transmission layer, a platform layer and an application layer, wherein the sensing layer is used for sensing the erosion of the pipeline;
the sensing layer is used for collecting static data and dynamic data of the pipeline;
the transmission layer is used for sensing data sharing among the layers and transmitting data to the platform layer;
the platform layer is used for establishing a digital twin virtual entity, updating the virtual entity in real time according to the dynamic data of the sensing layer, and processing, storing, fusing and analyzing the data of the sensing layer, the data shared by an external system and the simulation analysis data of the virtual entity;
the application layer performs intelligent decisions such as fault identification, failure prediction and the like on the erosion condition of the pipeline based on the analysis of the data of the platform layer, and feeds back the decisions to the platform layer optimizing virtual model.
According to the digital twin system for predicting pipeline erosion and evaluating safety, the pipeline full life cycle sensing layer, the transmission layer, the platform layer and the application layer are built from the perspective of pipeline erosion, so that fault identification and failure prediction of pipeline erosion conditions are realized, and the safety performance of the pipeline is judged.
Preferably, the static data includes equipment registration codes, inspection conditions, design data, installation data, retrofit or major repair data, usage management data, and inspection data.
Preferably, the static data acquiring mode includes any one or a combination of at least two of two-dimensional code information extraction, RFID radio frequency identification, three-dimensional reconstruction and laser radar, and typical but non-limiting combination includes two-dimensional code information extraction, RFID radio frequency identification and three-dimensional reconstruction, two-dimensional code information extraction, RFID radio frequency identification and laser radar combination, RFID radio frequency identification, three-dimensional reconstruction and laser radar combination, or two-dimensional code information extraction, RFID radio frequency identification, three-dimensional reconstruction and laser radar combination.
Preferably, the dynamic data includes defect detection data, media status data, material test data, and environmental data.
Preferably, the defect detection data includes wall thickness measurement data, surface defect detection data, and buried defect detection data.
Preferably, the media status data includes flow rate, temperature, solid particle concentration, particle size, particle shape, particle material, impact velocity, impact angle, and liquid viscosity.
Preferably, the material test data includes pipe material hardness, material toughness, microstructure, and roughness.
Preferably, the environmental data includes pressure pipe ambient temperature, humidity and pressure.
Preferably, the obtaining mode of the wall thickness measurement data comprises any one of ultrasonic thickness measurement, pulsed eddy current or electromagnetic ultrasonic.
Preferably, the method for acquiring the surface defect detection data includes any one of magnetic powder, infiltration, ultrasonic guided wave, magnetic leakage, machine vision or ACFM detection.
Preferably, the acquisition mode of the buried defect detection data comprises any one of ultrasonic, electromagnetic ultrasonic, phased array, traditional ray or DR detection.
Preferably, the data communication technology in the transport layer includes OPC-UA between devices of the perception layer, MQTT protocol transmitted to the platform layer is combined, and data sharing is carried out between the platform layers through ODBC/JDBC and other interfaces.
Preferably, the use of the transport layer further comprises implementing bidirectional mapping, dynamic interaction and real-time connection between the digital twin virtual entity and the actual pipeline entity.
Preferably, the digital twin virtual entity comprises a geometric model, a physical model and a pipeline erosion state evaluation model.
Preferably, the geometric information in the geometric model includes an actual pipe appearance shape, a dimension size, and an assembly relationship.
Preferably, the physical properties in the physical model include actual pipe mechanical properties, fluid properties, and electromagnetic properties.
Preferably, the pipeline erosion state evaluation model comprises state monitoring and early warning and health state evaluation for pipeline erosion.
Preferably, the data analysis technology in the platform layer includes any one or a combination of at least two of an artificial intelligence algorithm engine, deep learning, weighted average method, D-S theory of arguments or rough set theory, and typical but non-limiting combinations include a combination of an artificial intelligence algorithm engine, deep learning, weighted average method and D-S theory of arguments, a combination of an artificial intelligence algorithm engine, deep learning and weighted average method, a combination of a deep learning, weighted average method, D-S theory of arguments and rough set theory, or a combination of an artificial intelligence algorithm engine, deep learning, weighted average method, D-S theory of arguments and rough set theory.
Preferably, the data analysis result in the platform layer comprises state monitoring, safety evaluation and residual service life evaluation of the pipeline erosion condition.
The numerical ranges recited herein include not only the above-listed point values, but also any point values between the above-listed numerical ranges that are not listed, and are limited in space and for the sake of brevity, the present invention is not intended to be exhaustive of the specific point values that the stated ranges include.
Compared with the prior art, the invention has the following beneficial effects:
(1) The digital twin system for predicting pipeline erosion and evaluating safety provided by the invention unifies communication protocols among different devices, and can display the running state of the whole pipeline in real time;
(2) The digital twin system for predicting pipeline erosion and evaluating safety provided by the invention realizes state monitoring and safety evaluation of pipeline erosion conditions and gives out residual service life evaluation;
(3) The digital twin system for predicting pipeline erosion and evaluating safety provided by the invention is based on digital twin, realizes real-time sharing of virtual data and actual pipeline data, and is more beneficial to realizing state monitoring and safety evaluation on pipeline erosion conditions.
Drawings
Fig. 1 is a frame diagram of a digital twin system for predicting pipeline erosion and evaluating safety according to embodiment 1 of the present invention.
Detailed Description
The technical scheme of the invention is further described by the following specific embodiments. It will be apparent to those skilled in the art that the examples are merely to aid in understanding the invention and are not to be construed as a specific limitation thereof.
Example 1
The embodiment provides a digital twin system for predicting pipeline erosion and evaluating safety, which can be applied to the field of petrochemical industry.
The digital twin system for predicting pipeline erosion and evaluating safety comprises a sensing layer, a transmission layer, a platform layer and an application layer;
the sensing layer is used for collecting static data and dynamic data of the pipeline;
(1) The static data includes equipment registration codes, inspection conditions, design data, installation data, retrofit or major repair data, use management data, and inspection data;
the static data acquisition mode comprises two-dimensional code information extraction, RFID radio frequency identification, three-dimensional reconstruction and laser radar;
(2) The dynamic data includes defect detection data, media status data, material test data, and environmental data;
(2.1) the defect detection data comprises wall thickness measurement data, surface defect detection data, and buried defect detection data;
the obtaining mode of the wall thickness measurement data comprises ultrasonic thickness measurement, pulse eddy current and electromagnetic ultrasonic;
the surface defect detection data are obtained by magnetic powder, permeation, ultrasonic guided wave, magnetic leakage, machine vision or ACFM detection;
the acquisition mode of the buried defect detection data comprises ultrasonic, electromagnetic ultrasonic, phased array, traditional ray or DR detection;
(2.2) the media status data includes flow rate, temperature, solid particle concentration, particle size, particle shape, particle material, impact velocity, impact angle, and liquid viscosity;
(2.3) the material test data comprises pipe material hardness, material toughness, microstructure, and roughness;
(2.4) the environmental data comprises pressure conduit ambient temperature, humidity, and pressure.
And (II) the transmission layer mainly realizes bidirectional mapping, dynamic interaction and real-time connection between the pipeline high-fidelity model and the actual pipeline entity.
The transmission layer collects and integrates the perception information of different devices of the perception layer through OPC-UA, reliable transmission is carried out to the platform layer through MQTT protocol, data sharing is carried out between the platform layers through ODBC/JDBC interfaces and the like, and the digital twin virtual entity is updated, so that the virtual model is iterated and evolved continuously; and simultaneously, feeding back the analysis decision result of the platform layer to the physical entity to realize detection, maintenance, replacement and the like to form an information flow closed loop.
And (III) the platform layer is used for establishing a digital twin virtual entity, updating the virtual entity in real time according to the dynamic data of the sensing layer, and processing, storing, fusing and analyzing the data of the sensing layer, the data shared by an external system and the simulation analysis data of the virtual entity.
The platform layer establishes a digital twin virtual entity, and comprises a geometric model for reflecting geometric information such as the appearance shape, the size and the assembly relation of an actual pipeline, a physical model for reflecting physical properties such as the mechanical property, the fluid property and the electromagnetic property of the actual pipeline, a state monitoring and early warning model and a health state assessment model which are formed on the basis of data relation, historical experience, big data, deep learning and the like and aim at pipeline erosion. Processing, storing, fusing and analyzing the data of the actual pipeline system, the shared data of the external system and the twinning result data of the virtual entity, which are transmitted by the perception layer.
And (IV) the application layer is a decision system for various analyses, and performs state monitoring and safety evaluation on erosion conditions of the pipeline according to fusion analysis of related data of physical entities and virtual entities of the platform layer and data analysis technologies such as development of an artificial intelligent algorithm engine, deep learning, a weighted average method, a D-S theory of discussion, a rough set theory and the like, so as to give residual service life evaluation.
In summary, the digital twin system for predicting pipeline erosion and evaluating safety provided by the invention builds the full life cycle sensing layer, the transmission layer, the platform layer and the application layer of the covered pipeline from the perspective of pipeline erosion, so that the fault identification and failure prediction of the pipeline erosion condition are realized, and the safety performance of the pipeline is evaluated.
The applicant declares that the above is only a specific embodiment of the present invention, but the scope of the present invention is not limited thereto, and it should be apparent to those skilled in the art that any changes or substitutions that are easily conceivable within the technical scope of the present invention disclosed by the present invention fall within the scope of the present invention and the disclosure.
Claims (10)
1. The digital twin system for predicting pipeline erosion and evaluating safety is characterized by comprising a sensing layer, a transmission layer, a platform layer and an application layer;
the sensing layer is used for collecting static data and dynamic data of the pipeline;
the transmission layer is used for sensing data sharing among the layers and transmitting data to the platform layer;
the platform layer is used for establishing a digital twin virtual entity, updating the virtual entity in real time according to the dynamic data of the sensing layer, and processing, storing, fusing and analyzing the data of the sensing layer, the data shared by an external system and the simulation analysis data of the virtual entity;
the application layer performs intelligent decisions such as fault identification, failure prediction and the like on the erosion condition of the pipeline based on the analysis of the data of the platform layer, and feeds back the decisions to the platform layer optimizing virtual model.
2. The digital twin system for pipeline erosion prediction and safety assessment of claim 1, wherein the static data comprises equipment registration codes, inspection conditions, design data, installation data, retrofit or major repair data, usage management data, and inspection data;
preferably, the static data acquisition mode includes any one or a combination of at least two of two-dimensional code information extraction, RFID radio frequency identification, three-dimensional reconstruction or laser radar.
3. The digital twin system for pipeline erosion prediction and safety assessment of claim 1 or 2, wherein the dynamic data comprises defect detection data, media status data, material test data, and environmental data;
preferably, the defect detection data includes wall thickness measurement data, surface defect detection data, and buried defect detection data;
preferably, the media status data includes flow rate, temperature, solid particle concentration, phase content, particle size, particle shape, particle material, impact velocity, impact angle, and liquid viscosity;
preferably, the material test data includes pipe material hardness, material toughness, microstructure, and roughness;
preferably, the environmental data includes pressure pipe ambient temperature, humidity and pressure.
4. The digital twin system for predicting pipeline erosion and evaluating safety according to claim 3, wherein the obtaining mode of the wall thickness measurement data comprises any one of ultrasonic thickness measurement, pulse eddy current or electromagnetic ultrasound;
preferably, the method for acquiring the surface defect detection data comprises any one of magnetic powder, permeation, ultrasonic guided wave, magnetic leakage, machine vision or ACFM detection;
preferably, the acquisition mode of the buried defect detection data comprises any one of ultrasonic, electromagnetic ultrasonic, phased array, traditional ray or DR detection.
5. The digital twin system for pipeline erosion prediction and security assessment according to any one of claims 1-4, wherein the data communication technology of the transport layer comprises OPC-UA between devices of the perception layer, the MQTT protocol transmitted to the platform layer is combined, and data sharing is performed between the platform layers through interfaces such as ODBC/JDBC.
6. The digital twin system for pipeline erosion prediction and safety assessment according to any of claims 1-5, wherein the use of the transport layer further comprises enabling bi-directional mapping, dynamic interaction and real-time connection between digital twin virtual entities and actual pipeline entities.
7. The digital twin system for pipeline erosion prediction and safety assessment according to any one of claims 1-6, wherein the digital twin virtual entity comprises a geometric model, a physical model, and a pipeline erosion status assessment model.
8. The digital twin system for pipeline erosion prediction and safety assessment according to claim 7, wherein the geometric information in the geometric model comprises actual pipeline form factor, dimensional size and assembly relationship.
9. The digital twin system for pipeline erosion prediction and safety assessment of claim 7, wherein the physical properties in the physical model include actual pipeline mechanical properties, fluid properties, and electromagnetic properties;
preferably, the pipeline erosion state evaluation model comprises state monitoring and early warning and health state evaluation for pipeline erosion.
10. The digital twin system for pipeline erosion prediction and safety assessment according to any one of claims 1-9, wherein the data analysis techniques in the platform layer comprise any one or a combination of at least two of artificial intelligence algorithm engines, deep learning, weighted averaging, D-S theory of asperities;
preferably, the data analysis result in the application layer includes status monitoring, safety evaluation and residual service life evaluation of the erosion condition of the pipeline.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116482227A (en) * | 2023-06-25 | 2023-07-25 | 北京英智数联科技有限公司 | Pipeline corrosion monitoring method, device and system |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116482227A (en) * | 2023-06-25 | 2023-07-25 | 北京英智数联科技有限公司 | Pipeline corrosion monitoring method, device and system |
| CN116482227B (en) * | 2023-06-25 | 2023-10-20 | 北京英智数联科技有限公司 | Pipeline corrosion monitoring method, device and system |
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