METHOD FOR MODELING AND SIMULATING A SYSTEM OF SYSTEMS

Abstract

The invention relates to a method for modeling and simulating a system of systems, consisting of a technical system of interest and a plurality of systems outside the technical system of interest that form the environment in which the technical system of interest develops.

Description

FIELD OF THE INVENTION

[0001] The present invention relates to asset life cycle management and, more particularly, to planning analysis for asset life cycle management.

[0002] Asset management within the meaning of this patent refers to the management of the assets of a technical entity, such as an infrastructure or an industrial site, which is to be considered as a complex technical system consisting of multiple interacting pieces of equipment and resources, with the aim of maximising the value of the assets throughout their life cycle and optimising their performance and operation, reducing the capital costs of the assets and the operating costs associated with the assets, and extending the life of the assets.

[0003] To achieve this, we proposed creating digital twins of the infrastructures and industrial sites to be managed, based on a model that allows to carry out predictive simulations or synchronized supervision that integrate the actual development of the associated system.

[0004] The development of the Industrial Internet of Things (IIoT) and cloud platform technologies also facilitates the provision of industrial value-added services such as remote monitoring, intelligent operation and maintenance, professional optimization, and more.

[0005] The development of next-generation information technologies offers powerful support for these industrial services, and in particular, a digital twin provides a technical means for the interactive merging of an information domain and a physical domain. Digital twins are virtual, digital technical objects associated with physical entities whose behavior is simulated in a real-world environment using data. The technical capabilities of physical entities are modified using virtual-real interaction feedback, data fusion analysis, iterative decision optimization, etc.

[0006] The use of digital twins enables the oversight of the entire life cycle process of a technical system and its components, as the current state of the technical system can be reflected in the digital twin in real time, and ensures real-time optimization and maintenance of decisions and forecasts based on perceptual information.

[0007] The bidirectional, real-time dynamics of a closed-loop digital twin makes it possible to monitor, verify, simulate, predict, and optimize the technical activities of the physical entity thus supervised according to specific objectives, ensuring the synchronization of the interaction between physical entities and digital artifacts.

[0008] State parameters of physical entities can be reliably and efficiently measured and transmitted to the digital twin, allowing it to accurately reflect the actual technical state of a physical entity. When state variables and parameter setpoints change in the digital twin, the associated information is fed back to the physical entity, allowing the behavior and technical state of the physical entity to be adjusted.

[0009] In the industrial sector, the use of fieldbus technology to collect data from technical assets is mature and widespread. However, the Industrial Internet of Things (IIoT) has expanded beyond the industrial scope and is connected to the internet more broadly. Wireless communication technologies (GPRS, 4G, 5G, LPWAN, etc.) are widespread, and the stability and bandwidth of the connections have a major impact on the degree of virtual-real interaction, making it more difficult to ensure overall security, for example. Consequently, the process of virtual-real synchronization of the physical entity and the digital twin is a problem that deserves in-depth investigation. STATE OF THE ART

[0010] Known in the state of the art is the article by Jie Zhang et al., Supply-Chain Digital Twin Framework Design: An Approach of Supply-Chain Operations Reference Model and System of Systems, Cornell University Library Ithaca, July 19, 2021.

[0011] This document describes the general principle of using digital twins in the supply chain. It proposes general rules for creating such digital twins in the context of supply chains.

[0012] The article presents two concepts for digital twins: - a limited concept of the geometric digital twin, which focuses on the visualization of systems, and - a more comprehensive concept of digital twins for the simulation, control, evaluation, regulation, and monitoring of systems. This second concept of the digital twin is characterized by 1) the physical space, 2) the virtual space, 3) the data, 4) the services offered, and 5) the connection mechanisms between these components.

[0013] The article then briefly introduces the concept of a system of systems, highlighting one of the fundamental characteristics of this type of system, namely the autonomy of its components, and explains that a supply chain is in fact a system of systems.

[0014] The reference business model for supply chain operations defined by the American Supply Chain Association is then presented. This model is organized around five business processes (planning, procurement, production, delivery, returns management), which are divided into four levels of detail.

[0015] On this basis, the article presents a framework for digital twins for the supply chain based on four steps for creating a digital twin for a supply chain: 1. Modularization: The first step is to analyze the supply chain in a modular way and divide it into a hierarchy with four levels: the supply chain system, parts of the system, modules of these parts and blocks of these modules; 2. Digitisation of core activities: Every core activity related to the management of a supply chain must be digitized, and the article recommends carrying out this digitization work using existing components of a company's information system covering the activities in question; 3. Development of digital sub-twins for parts of the system: Digital sub-twins must then be developed ad hoc for each part of the supply chain system (in the sense of the modular decomposition introduced in step 1) by constructing the analysis mechanisms to be carried out and the indicators to be returned based on the digitalization of the basic activities (in the sense of step 2); 4. Integration: In the final step, all digital sub-twins obtained in step 3 are integrated (an illustration can be seen in the following diagram).

[0016] The remainder of this article presents a set of relatively general business and technical principles to ensure successful integration of a digital twin using the proposed method.

[0017] A case study of a small supply chain (2 suppliers, 1 freight forwarder, 1 factory, 2 retailers) then illustrates the proposed supply chain digital twin framework.

[0018] Also known is the article by Arrichiello Vincenzo et al, Systems engineering and digital twin: a vision for the future of cruise ships design, production and operations, International Journal on Interactive Design and Manufacturing, Vol. 14, No. 1, pp. 115-122, 2019.

[0019] This article demonstrates how model-based systems engineering techniques can be used to improve the design of cruise ships, which must balance extremely high operational performance and reliability requirements. It presents a systems engineering presentation, focusing on model-based systems engineering. This leads it to introduce the concept of a model, which is divided into descriptive and analytical models, and then to outline the problem of matching a model with the physical system it models, allowing it to introduce the concept of a digital twin of a given physical system.

[0020] This state-of-the-art document then analyzes the value of the digital twin concept for capturing the entire life cycle of a cruise ship. This section begins with purely business considerations and then demonstrates the possibility of feeding a digital twin with data from sensors attached to the real system it models, allowing the digital twin to acquire predictive capabilities to minimize risk and improve a cruise ship's profitability. The article then explains how a digital twin can be used to achieve user-centered design, which is indeed important in the context of a cruise ship, as the needs of passengers, crew, and the owner play a key role in the design of such a system. The four characteristics of the digital twin that the article highlights at this level are: 1. Ability to validate a system model with real data, 2. Ability to provide users with decision support and alerts, 3. Ability to predict changes in a physical system over time, 4. Ability to discover new opportunities in terms of services and revenue streams.

[0021] The benefits of a digital twin for improved design of the structure, machinery (powertrain), passenger compartment organization, and operational safety are then briefly discussed. Finally, the difference between the computer-aided design approach and the digital twin approach is presented, which, in particular, allows for capturing the evolution of a cruise ship over its lifetime in the specific context of this article. In particular, this document does not present architectural principles for a digital twin, but merely identifies business reasons that justify the benefits of a digital twin in the specific context of cruise ship design.

[0022] Finally, the article by Madni Azad M. et al., "Digital Twinenabled MBSE Testbed for Prototyping and Evaluating Aerospace Systems: Lessons Learned," IEEE Aerospace Conference 2021, pp. 1-8, March 6, 2021, is also well-known. It presents a model-based systems engineering approach for the design of drone swarms. This is based on a set of independent tools—referred to in the article as "test benches"—that offer various types of simulation mechanisms (agent-oriented, behavior-based, Monte Carlo, discrete-event, continuous, state machines).

[0023] This article then explains the features that distinguish the proposed approach from a classic "hardware-in-the-loop" approach, namely the ability to obtain different views of the system under study, manage dynamically reconfigurable systems through agent-oriented modeling, compose systems, collect evidence, leverage feedback from a system, explicitly address the temporal dimension of systems, consider the impact of changes, and validate models, without going into detail. This is followed by a brief description of the components of the proposed "test bench," which will serve as a digital twin for the drone swarms under study.The article then briefly describes how the proposed test bench was implemented at the IT level, as well as the structure of the intelligent dashboards that allow this test bench to be controlled through the simulation of scenarios. A scenario is characterized here by one or more objectives, the physical space in which the scenario takes place, the events that can occur, and the structure of the interface for user feedback. It also explains how digital twins—purely geometric in nature—of the terrain and a quadcopter (the basic component of a swarm) can be integrated into the proposed test bench. DISADVANTAGES OF THE STATE OF THE ART

[0024] Previous digital twin solutions primarily focus on digital geometric models and data feeds via the Internet of Things (IoT). This approach is insufficient because it cannot capture the behavior of the underlying industrial systems, especially the functional end-to-end behavior of these systems, which cannot be observed locally or geometrically (e.g., the total yield of a production line). In particular, a digital twin must be able to model and simulate these behaviors globally, starting with operational data and feeding them into dashboards for strategic decision-making. Existing solutions that work with raw data without being able to abstract it into synthetic, systemic data cannot achieve this.

[0025] In addition, existing solutions specifically dedicated to asset management generally do not offer the richness of digital twins, as the physical and digital models they process are generally local to the management of the assets they oversee, without taking into account their technical, business and external environment in its entirety (e.g., evolution of requirements, budgetary constraints, strategic decisions, evolution of regulations, ecological footprint, weather, physics of input / output flow, safety and security, etc.) and the influence of this environment on the evolution of assets: a digital twin must be able to model and simulate the impact of the environment on assets, something that existing solutions cannot do because they do not integrate this dimension. OVERVIEW OF THE INVENTION

[0026] The present invention enables optimal lifecycle management of industrial and technical assets, taking into account both their functional behavior, particularly end-to-end, and the environmental constraints associated with the assets in question. The objective of the invention is to provide a tool that helps operators of complex industrial infrastructures better manage the lifecycle of their assets and optimize their overall operations by leveraging new-generation decision support based on systemic approaches: systemic digital twins. A typical problem to be addressed in this way relates to maximizing the overall performance of an industrial system while minimizing its environmental footprint.

[0027] Embodiments include a systemic modeling language and methodology, a set of computer programs, and an associated hardware architecture for implementing a systemic digital twin manager for the assets under consideration. The objective of this systemic digital twin manager is twofold: first, to investigate the potential development of assets due to internal causes or changes in their environment, including the design and management of systemic development scenarios, the calculation of their probability, and the risk they pose to the assets; second, to issue systemic alerts when the identified systemic development scenarios, their probability, or their risk deviate from what was initially considered acceptable.

[0028] In its most general sense, the invention relates to a method for modelling and simulating a system of systems, consisting of a technical system of interest and a plurality of systems outside the technical system of interest, which form the environment in which the technical system of interest develops, and which comprises the following steps: • a step of designing one or more scalable digital models of the system of systems, which consists in recording the following elements in digital files: • a description of the system of interest in a system hierarchy comprising, for each system, a plurality of descriptors of the components of the system (individual states of the components, inventories managed by the components, performance indicators of the components, etc.) and of parameters associated with each of these systems, each of these components being itself modelable as a system down to the level of an elementary object; • a description of the environment of the system of interest, which includes, for each external system with which the system of interest interacts, a hierarchical description similar to that of the latter; • a description of the actions performed by each of the systems involved and the interactions between these systems, including: • a variety of descriptors of the laws of internal development of its components and the laws of dynamic interactions between the system and the other systems with which it interacts; • these interaction laws can modify, create, destroy or move one or more systems in the hierarchy, they can have deterministic or stochastic effects (determined by probabilistic laws) and are temporally bounded, that is, they are linked to execution times, which in turn are deterministic or stochastic, and each system can have its own temporality. • a step of configuring this digital model, comprising the following steps: • Collecting data sets corresponding to the description of the static and / or dynamic parameters for the development of the systems in question, using internal or external databases; • Feeding these data sets into the digital model(s) in the appropriate format; • Define the key performance indicators to be evaluated to support decision-making during the design, deployment, operation, maintenance and even dismantling of the system of interest. • a numerical simulation step that allows for an effective evaluation of the above-mentioned performance indicators, this step comprising the following sub-steps: • Defining simulation scenarios with a large number of iterations (so-called Monte Carlo simulation) and statistical processing of the calculated values ​​of the performance indicators; • The actual execution of these simulation scenarios on a number of control units, taking into account the load of each of these control units; • Where appropriate, triggering alarms if the simulations reveal development scenarios of the system of interest that pose a material, economic or environmental risk to the system of interest, its operators or the systems with which it interacts, as well as to the operators of those systems.

[0029] For the purposes of this invention, “model” means a formal specification of the considered system of systems with unambiguous mathematical semantics. Detailed description of a non-limiting exemplary embodiment

[0030] The present invention will be better understood by reading the following description, which refers to a non-limiting, exemplary embodiment illustrated in the accompanying drawings, in which: [ Fig. 1] Fig. 1 shows a schematic view of the technical architecture according to the invention.

[0031] The present invention relates generally to the strategic management of assets in industrial enterprises and public organizations, and more particularly to the immediate detection of any potentially risky developments in these assets, resulting either from internal problems or drastic changes in the environment, or both.

[0032] The invention is based on two interlocking technical subassemblies: 1. the systemic digital twin, as described, to improve the technical performance of its physical twin. 2. the proposed architecture to improve the performance of the digital twin creation and maintenance process.

[0033] The strategic management of assets consists of managing the assets of an entity, such as an industrial production company, a technical infrastructure operator or a public body, by establishing possible scenarios of systemic evolution, classifying these scenarios according to their probability and the risk they represent for the assets, their owners and their environment, evaluating them through computer simulations, monitoring environmental conditions and, finally, issuing alerts when the identified scenarios, their probability or their risk deviate from what was initially considered acceptable.

[0034] The invention enables the immediate detection of serious deviations in operational processes that are due either to internal problems, drastic changes in the environment, or both.

[0035] The objectives of the invention generalize those of US patent 2014 / 0330747 A1. However, the underlying technologies and areas of application differ significantly. architecture

[0036] [ Fig. 1] shows the hardware architecture of a systemic digital twin designed according to an embodiment of the invention.

[0037] It comprises a system (100) for managing the systemic twin, which includes a systemic data extraction server (110), a server (120) for managing systemic models, a systemic evidence manager (130), and a systemic simulation server (140). The invention implements a modeling language accessible in the cloud in the form of scripts (210). The system (100) for managing the systemic twin communicates with the systemic trajectory simulation equipment (220). The data is stored in transaction data servers (230) and in the form of evidence scripts (240).

[0038] The inventive solution for strategic asset management is based on the design and simulation of systemic digital twins of the assets under consideration. These systemic digital twins aim to describe scenarios for the use of assets and their potential systemic development depending on internal and external changes.

[0039] The scenarios incorporate operational data from practice, which is continuously updated. After being described and updated, these usage scenarios and systemic developments can be simulated to classify them according to their probability and the risk they pose to the assets. Based on the simulation results, systemic alerts can be issued, prompting measures to preserve the assets or adjustments to asset management policies to account for the corresponding internal and external developments. The architecture of the embodiments of the invention thus makes it possible to combine and integrate descriptive, predictive, and prescriptive analyses.

[0040] The implementation modes are based on four pillars: the design and management of system models describing scenarios using the Σ (Sigma) modeling language, the continuous updating of the operational field data integrated into these system models using a dedicated system data consolidation server, the design and management of scripts, the so-called system proofs, which describe which performance indicators are to be calculated using computer-aided system simulations, and finally, a system simulation technology for distributing the simulations and collecting their results. • Systemic data extraction services

[0041] In one embodiment, systemic models include data distributions and time series, such as market prices for barrels of oil or reliability data for electronic components.

[0042] To enable an accurate assessment of the probability of scenarios and their ranking in terms of the risks they pose to assets, this data is continuously updated. Data mining scripts are executed on a dedicated server (110) to extract data from internal or external databases and convert it into a form that can be integrated into system models. • Design and management of Σ models (sigma models)

[0043] In one embodiment, systemic models are designed in the object-oriented modeling language Σ (Sigma) [Krob & Rauzy, 2022, A Guided Tour of the Modeling Language Σ, CESAMES Systemic Intelligence Pte. Ltd.], which belongs to the S2ML+X family [Rauzy & Haskins, 2019, Foundations for Model-Based Systems Engineering and Model-Based Safety Assessment, Journal of Systems Engineering, Wiley Online Library, Vol. 22, pp. 146-155; Batteux, Prosvimova & Rauzy, 2018, From Models of Structures to Structures of Models, IEEE International Symposium on Systems Engineering (ISSE 2018), Rome, Italy] and implemented via a dedicated server (120) for managing systemic models.

[0044] The implementation of Σ-modeling (sigma modeling) relies on a specific framework and procedure for enterprise architecture [Krob & Rauzy, 2021, Managing the Systemic Digital Twin of an Industrial Enterprise with WordLab & Σ, CESAMES Systemic Intelligence Pte. Ltd.], derived from the CESAM systems architecture framework and procedure [Krob, 2017, CESAM: CESAMES Systems Architecting Method- A Pocket Guide, CESAM Community; see https: / / cesam.community / le-guide-cesam / ], which involves the analysis of industrial assets in three dimensions—operational, functional, and organic—with the specific capabilities offered by assets in their various operating modes being assigned to the organic architecture.

[0045] The Σ (Sigma) language is used to describe usage scenarios for complex technical and socio-technical systems. The associated models consist of two essential components: a description of the organic architecture of the system under study and a description of the functional behavior of the system components.

[0046] The description of functional behavior consists of processes, called activities, that are executed by components. The start of each activity depends on the system state, takes a certain time, and has a certain effect on the system under investigation. Both the time required to execute an activity and its outcome can be subject to uncertainties, which are described using integrated or empirical probability distributions.

[0047] In this respect, Σ technology (Sigma technology) benefits from the research and development work carried out around the design of the AltaRica 3.0 modeling language [Batteux, Prosvirnova, and Rauzy, 2019, AltaRica 3.0 in 10 Modeling Patterns, International Journal of Critical Computer-Based Systems, Inderscience Publishers, Vol. 9, Nos. 1-2, pp. 133-165; see also the AltaRica Association website https: / / www.altarica-association.org]. • Design and management of systemic evidence

[0048] In one embodiment, a dedicated systemic evidence manager (130) is tasked with demonstrating that assets are not currently at risk, or, conversely, with identifying one or more development scenarios that could potentially put them at risk. The systemic evidence considered is pragmatic in the sense that it is based on practical knowledge and requires simulations aimed at calculating the key performance indicators of assets. The calculations to be performed are described in so-called evidence scripts.

[0049] In one embodiment, the systemic evidence manager is responsible for executing these scripts to complete the evidence. To this end, the systemic evidence manager maintains a dialogue with both the systemic model server and the systemic data extraction server to compile the models and data from which the key performance indicators are calculated. It also communicates with the system simulation server, which is responsible for performing these calculations. • Management of system simulation

[0050] In one embodiment, the simulations to be performed are managed by a dedicated system simulation server (140). These simulations can be performed locally or distributed across a network of internal and external computers. The system simulation server is responsible for distributing a simulation according to the workload of the computers in the network. Furthermore, it is responsible for collecting and merging the results before sending them back to the systematic evidence manager. QUOTES CONTAINED IN THE DESCRIPTION

[0000] This list of documents submitted by the applicant was generated automatically and is included solely for the convenience of the reader. This list is not part of the German patent or utility model application. The DPMA assumes no liability for any errors or omissions. Cited patent literature

[0000] US 2014 / 0330747 A1

[0035] Zitierte Nicht-Patentliteratur

[0000] Jie Zhang et al., Supply-Chain Digital Twin Framework Design: An Approach of Supply-Chain Operations Reference Model and System of Systems, Cornell University Library Ithaca, 19. Juli 2021

[0010] Arrichiello Vincenzo et al, Systems engineering and digital twin: a vision for the future of cruise ships design, production and operations, International Journal on Interactive Design and Manufacturing, Vol. 14, Nr. 1, S. 115-122, 2019

[0018] Madni Azad M. et al. bekannt, Digital Twinenabled MBSE Testbed for Prototyping and Evaluating Aerospace Systems: Lessons Learned, IEEE Aerospace-Konferenz 2021, S. 1-8, 6. März 2021

[0022] Krob & Rauzy, 2022, A Guided Tour of the Modeling Language Σ, CESAMES Systemic Intelligence Pte. Ltd.

[0043] Rauzy & Haskins, 2019, Foundations for Model-Based Systems Engineering and Model-Based Safety Assessment, Journal of Systems Engineering, Wiley Online Library, Vol. 22, S. 146-155

[0043] Batteux, Prosvimova & Rauzy, 2018, From Models of Structures to Structures of Models, IEEE International Symposium on Systems Engineering (ISSE 2018), Rom, Italien

[0043] Krob & Rauzy, 2021, Managing the Systemic Digital Twin of an Industrial Enterprise with WordLab & Σ, CESAMES Systemic Intelligence Pte. Ltd.

[0044] Krob, 2017, CESAM: CESAMES Systems Architecting Method- A Pocket Guide, CESAM Community; siehe https: / / cesam.community / le-guide-cesam /

[0044] Batteux, Prosvirnova und Rauzy, 2019, AltaRica 3.0 in 10 Modeling Patterns, International Journal of Critical Computer-Based Systems, Inderscience Publishers, Vol. 9, Nr. 1-2, S. 133-165

[0047]

Claims

[1] A method for modelling and simulating a system of systems consisting of a technical system of interest and a plurality of systems external to the technical system of interest which form the environment in which the system of interest develops, comprising the following steps: • a step of designing one or more scalable digital models of the system of systems, which consists in recording the following elements in digital files: • a description of the system of interest in a system hierarchy comprising, for each system, a plurality of descriptors of the components of the system (individual states of the components, inventories managed by the components, performance indicators of the components, etc.) and of parameters associated with each of these systems, each of these components being itself modelable as a system down to the level of an elementary object; • a description of the environment of the system of interest, which includes, for each external system with which the system of interest interacts, a hierarchical description similar to that of the latter; • a description of the activities performed by each of the systems involved and the interactions between these systems, including: • a variety of descriptors of the laws of internal development of its components and the laws of dynamic interactions between the system and the other systems with which it interacts; • these interaction laws can modify, create, destroy or move one or more systems in the hierarchy, they can have deterministic or stochastic effects (determined by probabilistic laws) and are temporally bounded, that is, they are linked to execution times, which in turn are deterministic or stochastic, and each system can have its own temporality. • a step of configuring this digital model, comprising the following steps: • Collecting data sets corresponding to the description of the static and / or dynamic parameters for the development of the systems in question, using internal or external databases; • Feeding these data sets into the digital model(s) in the appropriate format; • Define the key performance indicators to be evaluated to support decision-making during the design, deployment, operation, maintenance and even dismantling of the system of interest. • a numerical simulation step that allows for an effective evaluation of the above-mentioned performance indicators, this step comprising the following sub-steps: • Defining simulation scenarios with a large number of iterations (so-called Monte Carlo simulation) and statistical processing of the calculated values ​​of the performance indicators; • The actual execution of these simulation scenarios on a number of control units, taking into account the load of each of these control units; • Where appropriate, triggering alarms if the simulations reveal development scenarios of the system of interest that pose a material, economic or environmental risk to the system of interest, its operators or the systems with which it interacts, as well as to the operators of those systems.

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