WO2019211288A1 - Procédé et système pour découvrir et visualiser des problèmes opérationnels potentiels de processus s'exécutant dans de l'équipement et des systèmes dans une installation - Google Patents

Procédé et système pour découvrir et visualiser des problèmes opérationnels potentiels de processus s'exécutant dans de l'équipement et des systèmes dans une installation Download PDF

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Publication number
WO2019211288A1
WO2019211288A1 PCT/EP2019/061080 EP2019061080W WO2019211288A1 WO 2019211288 A1 WO2019211288 A1 WO 2019211288A1 EP 2019061080 W EP2019061080 W EP 2019061080W WO 2019211288 A1 WO2019211288 A1 WO 2019211288A1
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WO
WIPO (PCT)
Prior art keywords
data
systems
installation
equipment
physical
Prior art date
Application number
PCT/EP2019/061080
Other languages
English (en)
Inventor
Kaare Johan FINBAK
Thomas Hammer
Terje HEIERSTAD
Karl-Petter Lindegaard
Kenneth NAKKEN
Roar NILSEN
Tore RAGNHILDSTVEIT
Roger SKOGMO
Jeppe SVERDRUP
Trond WAAGE
Original Assignee
Kongsberg Digital AS
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Publication date
Application filed by Kongsberg Digital AS filed Critical Kongsberg Digital AS
Publication of WO2019211288A1 publication Critical patent/WO2019211288A1/fr

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Classifications

    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00—Testing or monitoring of control systems or parts thereof
    • G05B23/02—Electric testing or monitoring
    • G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
    • G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
    • G05B23/0243—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults model based detection method, e.g. first-principles knowledge model
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00—Testing or monitoring of control systems or parts thereof
    • G05B23/02—Electric testing or monitoring
    • G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
    • G05B23/0259—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterized by the response to fault detection
    • G05B23/0283—Predictive maintenance, e.g. involving the monitoring of a system and, based on the monitoring results, taking decisions on the maintenance schedule of the monitored system; Estimating remaining useful life [RUL]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/10—Interfaces, programming languages or software development kits, e.g. for simulating neural networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computing arrangements using knowledge-based models
    • G06N5/02—Knowledge representation; Symbolic representation
    • G06N5/022—Knowledge engineering; Knowledge acquisition
    • G06N5/025—Extracting rules from data

Definitions

  • the present invention concerns data processing systems, and more specifically a method and system for monitoring and optimizing the design and operations of physical systems by representing the systems by digital twins producing
  • One way of foreseeing or predicting a problem or a need for maintenance is to simulate the behaviour of the physical system using a digital twin.
  • a digital twin is a digital duplicate of a real physical system, facility or equipment. When running on a computer it will behave like the physical system it is
  • the digital representation of a physical system can be used for various purposes, such as for instance early simulation of the system’s operational behaviour or maintenance friendliness or real-time surveillance.
  • potential failures may be detected by comparing expected performance data with simulated and historical data.
  • US 20170091791 Al describes a digital power plant and a digital twin model of this comprising visualization a software tool where critical events are visualized for an operator.
  • the present invention comprises a method and system for visualizing potential performance problems, critical events, equipment and suggestion of solutions to avoid and fix problems.
  • Kongsberg Digital has developed a digital platform named kognifaiTM. It is a complex ecosystem interconnecting networks of organizations, applications, and assets. It supports collaboration and knowledge -sharing between all organizations that are part of it, enabling them to interact at a new level to provide new reach and business value.
  • the digital platform acts as a single digital platform for all data produced by different physical systems across the technology spectrum.
  • This digital ecosystem is as a key player in the present invention for retrieving and processing data from different installations.
  • Digital twins of different installations are connected to this ecosystem as well as user interfaces for presenting updated information for an operator.
  • a few designers can simulate and optimize the behaviour of an entire system and one or a few operators can monitor and control operations of different types at installations located at remote sites. The number of designers and operators needed can thus be reduced or eliminated.
  • the solution is well suited for both optimizing design and operating low- and unmanned installations.
  • a designer or operator is presented with a visualization of a possible problem at an installation, as well as suitable tools for investigating the specific problem for finding a solution. This is a guided investigation and one is given access to information and tools required to analyse events and fixing possible problems needing maintenance, such as for instance faulty component, equipment, leakage or offset due to wear and tear, etc.
  • a problem may for instance be stuck or offset equipment operation in a processing plant.
  • a designer or operator will then get access to a software tool (App) that will guide him to set the correct diagnostic for fixing the problem.
  • App software tool
  • Type of App enabled will depend on type of problem.
  • the designer will get access to software tools related to this for investigating the problem and improving the design of the plant. Bottlenecks are factors contributing to a suboptimal operation, e.g. not optimal design, unfit pipe dimensions and/or valve type etc. It may also be that a better set point should be set for a controller.
  • An operator monitoring a system in an operational phase will get access to software tools (Apps) for identifying the root cause of the problem and for eliminating it by for instance controlling valves at a processing plant at a remote site.
  • Apps software tools
  • the present invention enables visualization of different types of installations with different levels of detail.
  • Several remote located installations can be monitored and controlled from one location.
  • An operator will be presented with a visual overview of the different installations, and if there is a problem with one or more of the installations they will be indicated and visualised in the overview showing the installations.
  • the operator can then zoom in on an indicated installation for viewing more details of systems, sub -systems and components comprised in an installation where a problem is detected. If several problems are detected, they will be ranked. Minor problems can then be corrected by the system, either manually or
  • the present invention comprises a method for discovering and visualizing potential operational problems of processes running in physical equipment and systems comprised in at least one installation.
  • the method comprises the steps of:
  • the invention is also defined by a device with means for performing the method defined in the claims, as well as a computer program product for performing the method when executed on a computer.
  • a purpose of the present invention is to enable optimization during a design phase of an instal lation. Early detection and elimination of potential operational problems during the design phase is vital for running installations and systems without facing problems. Potential failures are detected and visualized by comparing expected performance data with simulated and historical data.
  • Another purpose is to enable remote monitoring and controlling of installations in an operational phase, without having to use personnel with detailed knowledge of the installations. This is enabled by visualizing possible problems for an operator. The operator can then investigate further by zooming in on indicated problems in a visualization of the installations. The operator may further be given access to suggested software tools for adjusting and optimizing parameters of components and systems, or fixing indicated problems.
  • Fig. 1.1 shows a flowchart of a decision support model according to the invention
  • Fig. 1.2 shows a flowchart of a use case example
  • Fig. 2 shows an overview of the dataflow to and from physical systems and databases providing different types of data about equipment, systems and processes of at least one installation;
  • Fig. 3 shows an example of screen dump from the user interface at an operator station visualizing details of a production well
  • Fig. 4 show an example of a visualization of a warning of suspect Production Riser
  • Fig. 5 shows the state of a gas lift control valve
  • Fig. 6 shows an example of automatic browsing and display of faulty equipment
  • Fig. 7.1 shows one example of Equipment localization in 3D model and CCTV - automatic position correlation
  • Fig. 7.2 shows another example of Equipment localization in 3D model and CCTV- automatic position correlation
  • Fig. 8.1 shows an example of visualization of walk to work inspector
  • Fig. 8.2 shows an example of a screen dump walk to work inspector.
  • the Digital Twin according to the present invention is utilizing a hybrid modelling approach by combining estimated data from a physical model, machine learning model and measured field data.
  • the physical model is a high fidelity dynamic model produced with sound basis in first principles physics, chemistry and engineering, which is used together with a Machine Learning Model using neural networks, regression methods and statistics.
  • the Digital twin implemented by the hybrid modelling approach enables equipment and process performance monitoring in real-time by constantly measuring data from a field and constantly comparing and monitoring the data with data from the hybrid model.
  • the Machine Learning Model is using these data to build knowledge of process behaviour and dependencies, i.e. how different equipment and processes are related to each other.
  • the hybrid modelling approach will not only be based on historical conditions but also enabl es to predict future states of equipment as well as detecting equipment degradation and suggesting production optimization.
  • the Digital Twin also integrates information and status from a maintenance planning system.
  • Figure 1.1 shows a flowchart of a decision support model according to the invention.
  • the flowchart shows the different data, processes and decisions comprised in the method for discovering and visualizing potential operational problems of processes running in physical equipment and systems comprised in at least one installation.
  • the first step of the method is simulating the physical equipment, systems and processes in a physical model established from installation design basis and data characterizing the physical equipment, systems and processes comprised in the installation.
  • a Hybrid Model comprises a mix of several different types of data models. In this case it combines a physical model and a machine learning model and utilizes their strengths in order achieve a robust and fast solution with high accuracy.
  • a prediction model is yet another instance of the physical model for modelling physical processes where the methodology is based on mathematical models predicting a state or output from a system.
  • the next step of the method is establishing a first set of data characterizing the processes running in the physical equipment and systems comprised in the at least one installation, and generating a second set of data from the physical model characterizing the processes running in the physical equipment and systems comprised in the at least one installation.
  • Said first set of data can be acquired in different ways.
  • the first set of data is established from equipment data, expected performance data and/or historical data characterizing processes running in equipment and systems comprised in the at least one installation. This method is used for detecting and visualizing potential operational problems during a design phase of the at least one installation. In this way layout and design of an installation can be optimized prior to operating the installation.
  • the first set of data is established by measured production data acquired from the physical equipment and systems. These data are used to synchronize the physical model in an online prediction mode. This method is generating the second set of data which is used for detecting and visualizing potential operational problems during an operational phase of the at least one installation.
  • the next step of the method is inputting the first and second set of data to the Machine Learning Model. These data are both used to train the Machine Learning model in operational behaviors and dependencies, and further to propose optimization settings to improve the production. Both measured data and model prediction data are used together with additional prediction data that is not measured or not possible to measure in the field. Optimization algorithms are using these data to find a more optimized operation. Proposed optimization settings are sent to the validation module as a third set of data.
  • the validation module is initialized with the current status from the prediction module.
  • the proposed production optimizations from the Machine Learning Model will be tested and validated to see if the proposed settings are feasible.
  • the third set of data is then assessed and validated. This is necessary since the Machine Learning Model do not necessarily know the physical limitations or boundary conditions of simulated equipment and processes.
  • the optimized data will provide decision basis for optimizing a design.
  • the optimized data will provide decision basis for correcting a possible problem.
  • the last step of the method is visualizing possible problems in equipment and systems of the at least one installation , based on results from the validation module thereby indicating which equipment or systems should be further investigated for optimization and correction purposes.
  • the visualization can for instance be presented on an interactive screen operated by a user, e.g. operator or engineer.
  • the visualization may initially show an overview of an installation, and the user can then zoom in on any area of interest until an indicated problem is displayed along with information of the problem. Zooming in on an area of an installation can also be done automatically, showing for instance equipment that is not performing as expected.
  • suitable software tools are in one embodiment of the invention provided for adjusting and optimizing parameters of components and systems in a design phase.
  • software tools for fixing an indicated problem is provided in an operational phase of an installation.
  • An operator can in one embodiment select an indicated component or system from the visualization of the screen, and then select a suggested software tool for adjusting or correcting a problem. In a design phase this may be that another type of pump is needed, and in an operational phase this may be that a new set point must be set for a controller.
  • problems can be defined and sorted according to degree of severity. Less severe problems can be fixed automatically, and more severe problems can be presented in said visualization.
  • the physical model further comprises a modification module, and a training module.
  • the modification module is used to secure safe and efficient modifications to the existing design and operations.
  • the starting point for the modification module is a copy of the online prediction model which is further updated with planned modifications. The system will then be ready to test out the new design.
  • the training module is used to train the operational team and validate the operational procedures.
  • This model instance can be copied from any of the other modules depending on the training requirements. As an example, one can train on current running conditions by using the online prediction module, or copy the status from the modification module to prepare the operators on the future modification on the field.
  • the invention further comprises a device for discovering and visualizing potential operational problems of processes running in physical equipment and systems comprised in at least one installation.
  • the device comprises different modules and means enabling the method described above.
  • the device further comprised input means for inputting a first set of data
  • the device further comprises a machine learning model with input means for receiving the first and second set of data and means for training the model in operational behaviours and dependencies, and generation means for generating and outputting a third set of data comprising proposed improvements and optimized solutions derived from the first and second set of data, and a validation module with validation means for validating and testing the third set of data received from the machine learning model via input means, and means for determining if the proposed improvements and optimized solutions are feasible.
  • the device further comprises means for generating visualization data of possible problems in equipment and systems of the at least one installation based on results from the validation module thereby indicating which equipment or systems should be further investigated for optimization and correction purposes.
  • the device may be a computer with input- and output means as well as memory means.
  • the computer further comprises a computer program for performing the method above, and where the program has access to a database with data defining physical equipment and systems comprised in at least one installation.
  • the invention is also defined by a computer program product that when run on a computer executes the method described above for discovering and visualizing potential operational problems of processes running in physical equipment and systems comprised in at least one installation.
  • Figure 1.2 shows a flowchart illustrating a use case example where modification and training modes of the physical model are included.
  • Figure 1.2 illustrates how operational teams can use the physical model for training purposes. This is performed in a training mode of the physical model. The operational procedures are validated in this mode where flaws are removed and corrections are fed back to the design basis which implements the new procedures in the Field.
  • Figure 2 shows an example of the digital twin according to the invention.
  • Different physical systems are connected to the kognifaiTM platform.
  • the platform acts as a single source for all data produced by different physical systems across the technology spectrum, providing data about equipment, systems and processes to the tools (Apps) operators use to visualize and improve the design basis and detect and solve operational problems, and well as providing access to models and services for rapid development of new tools (Apps) through open APIs.
  • the invention can visualize both 2D and 3D design data enriched with all sets of data mentioned above for giving the user additional insight knowledge of the status.
  • Multiple instances of the digital twin can be started in parallel, all gathering data from the same data source hosted in the cloud, e.g. kognifaiTM. This enables multidi s cip linary collaboration workflows which again increase the common understanding across disciplines, as exemplified in Figure 2.
  • the control room operator opens the well from the operator station, ref. Figure 3, to initiate the startup and monitor the change in production. The following procedure is performed:
  • a maintenance team decides to replace the valve on the next service and uses the Digital Twin to collect all relevant data to plan the replacement.
  • the Digital Twin integrates information from many sources and provides a compiled picture to enhance the situational awareness and ease multidisciplinary collaboration for fault localization, diagnostic and decision making. This includes providing:
  • Maintenance Pl anning Team submits the maintenance task to one of the roving teams.
  • the onshore support team can monitor the docking and walk-2-work operation by opening the lnspector view for the supply vessel, Figure 8.1.
  • the walk-2 -work inspector includes critical parameters related to marine gangway operations, ref. Figure 8.2, such as: • Gangway stroke
  • the invention provides a one -stop- solution for both optimizing design when planning and designing an installation, as well as discovering, visualizing and suggesting how a detected problem should be solved in an operational phase of an installation.
  • the solution is well suited for low- and unmanned installations.

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Abstract

L'invention concerne un procédé, un dispositif et un produit programme d'ordinateur pour découvrir et visualiser des problèmes opérationnels potentiels de processus s'exécutant dans de l'équipement physique et des systèmes compris dans au moins une installation, comprenant la simulation de l'équipement physique, des systèmes et des processus dans un modèle physique établi à partir d'une base de conception d'installation et de données caractérisant l'équipement physique, les systèmes et les processus compris dans la ou les installations. Sur la base de résultats de validations, des problèmes possibles dans l'équipement physique et les systèmes de la ou des installations sont visualisés.
PCT/EP2019/061080 2018-05-02 2019-04-30 Procédé et système pour découvrir et visualiser des problèmes opérationnels potentiels de processus s'exécutant dans de l'équipement et des systèmes dans une installation WO2019211288A1 (fr)

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NO20180628A NO20180628A1 (en) 2018-05-02 2018-05-02 Digital twin and decision support for low or unmanned facilities
NO20180628 2018-05-02

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CN111208759A (zh) * 2019-12-30 2020-05-29 中国矿业大学(北京) 矿井无人化综采工作面数字孪生智能监控系统
EP3822718A1 (fr) * 2019-11-18 2021-05-19 Siemens Aktiengesellschaft Dispositif de système et procédé de gestion d'actifs industriels basé sur un modèle intégré
EP3836051A1 (fr) * 2019-12-13 2021-06-16 Basf Se Optimisation d'une installation industrielle
CN114019827A (zh) * 2021-10-19 2022-02-08 中国舰船研究设计中心 一种基于数字孪生的无人艇虚拟化平台
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EP3822718A1 (fr) * 2019-11-18 2021-05-19 Siemens Aktiengesellschaft Dispositif de système et procédé de gestion d'actifs industriels basé sur un modèle intégré
CN111008502B (zh) * 2019-11-25 2021-07-13 北京航空航天大学 一种数字孪生驱动的复杂装备故障预测方法
CN111008502A (zh) * 2019-11-25 2020-04-14 北京航空航天大学 一种数字孪生驱动的复杂装备故障预测方法
EP3836051A1 (fr) * 2019-12-13 2021-06-16 Basf Se Optimisation d'une installation industrielle
WO2021116126A1 (fr) * 2019-12-13 2021-06-17 Basf Se Optimisation d'usine industrielle
CN111208759A (zh) * 2019-12-30 2020-05-29 中国矿业大学(北京) 矿井无人化综采工作面数字孪生智能监控系统
CN111208759B (zh) * 2019-12-30 2021-02-02 中国矿业大学(北京) 矿井无人化综采工作面数字孪生智能监控系统
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US11703827B2 (en) 2020-09-03 2023-07-18 Rockwell Automation Technologies, Inc. Industrial automation asset and control project analysis
US11899434B2 (en) 2020-09-09 2024-02-13 Rockwell Automation Technologies, Inc. Industrial automation project code development guidance and analysis
US11899412B2 (en) 2020-09-09 2024-02-13 Rockwell Automation Technologies, Inc. Industrial development hub vault and design tools
US11561517B2 (en) 2020-09-09 2023-01-24 Rockwell Automation Technologies, Inc. Industrial development hub vault and design tools
US11415969B2 (en) 2020-09-21 2022-08-16 Rockwell Automation Technologies, Inc. Connectivity to an industrial information hub
US11762375B2 (en) 2020-09-21 2023-09-19 Rockwell Automation Technologies, Inc. Connectivity to an industrial information hub
US11796983B2 (en) 2020-09-25 2023-10-24 Rockwell Automation Technologies, Inc. Data modeling and asset management using an industrial information hub
CN114019827A (zh) * 2021-10-19 2022-02-08 中国舰船研究设计中心 一种基于数字孪生的无人艇虚拟化平台
EP4246264A1 (fr) * 2022-03-15 2023-09-20 Claritrics Inc d.b.a Buddi AI Système analytique pour technologie de montage en surface (smt) et procédé associé

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