E5
Digital Twins 1

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08:35
conference time (CEST, Berlin)
Digital Twin Based Predictive Maintenance
26/10/2021 08:35 conference time (CEST, Berlin)
Room: E
B. Kieß (Anhalt University of Applied Sciences, DEU); C. Schulz (Hochschule Anhalt, DEU)
B. Kieß (Anhalt University of Applied Sciences, DEU); C. Schulz (Hochschule Anhalt, DEU)
Maintenance strategies of drivetrains either focus on cases of failure or act preventively. The case of failure of a single element within a drivetrain can lead to the failure of the whole machinery or even an entire production line. A maintenance strategy, which focuses only on the case of failure, holds therefore economic risks. A preventive maintenance strategy can lead to replacements of intact components, because one strictly relies on fix maintenance intervals. The useful life of these replaced components is not fully utilized. A further development is the combination of one or two maintenance strategies mentioned above with condition monitoring (CM) in addition. CM monitors crucial components of the machinery. If CM indicates first signs of maloperation, unusual signals or amplitudes, one can initiate maintenance. The significance of a CM relies on the amount, placement and positioning of sensors. In reality, placement and positioning is restricted by design space and operation conditions. Current Research at HSA aims on the development of a predictive maintenance strategy, which combines CM with a realtime-capable digital twin. The latter bases on a multibody system and can be considered as a virtual CM. Within this, the CM provides boundary conditions and realtime validation for the digital twin. The virtual CM suffers no restrictions such as design space and operation conditions. It is able to predict the behavior of the drivetrain at any time, at any place, in any resolution. The data provided by the virtual CM can be used to reevaluate maximum utilization of components and therefore it can be used to set up a predictive maintenance strategy. To investigate the possibilities of this predicitve maintenance strategy further, a drivetrain test bench has been developed at HSA from scratch. The main component is a modular, three-stage gearbox which allows different setups of parameters in which e.g. center distance, bearing concepts and transmission ratio can be changed and modified. Several high fidelity sensors provide CM in realtime. The Paper will focus on the comparison of real and virtual signals and shows first results of validations in frequency domain.
Multibody Dynamics, Predictive Maintenance, Condition Monitoring
08:55
conference time (CEST, Berlin)
Real-time Digital Twins
26/10/2021 08:55 conference time (CEST, Berlin)
Room: E
J. Lorenzi, C. Heinrich, D. Hartmann (Siemens AG, DEU)
J. Lorenzi, C. Heinrich, D. Hartmann (Siemens AG, DEU)
Digital Twins are a major trend in digitization with a high potential to unlock model-based value-streams beyond R&D. For example, advanced monitoring and diagnostics solutions leveraging real-time models allow for gaining insights beyond available sensor information and, thus, ultimately enable more efficient operations and higher availability. However, often digital twins live in silos with a limited usability beyond their tools, domains, or creators. The concept of Executable Digital Twins (xDT) aims at overcoming this limitation. Executable digital twins are specific encapsulated realizations of Digital Twins together with their execution engines. They enable the reuse of simulation models from R&D and unlock new digital value streams and business models towards selling availability instead of the product itself. To overcome the manual efforts in today’s toolchains, we present a novel workflow framework leveraging simulation standards like FMU/FMI, containerization technologies like Docker, advanced calibration techniques like multi-fidelity gaussian processes, or model order reduction technologies like operator inference. These allow for addressing the different requirements with respect to their accuracy including uncertainty quantification, execution time, continuous calibration, or usage out of the original context. A particular focus will be on real-time execution capability since this opens up new application opportunities for digital twins. Alongside introducing the next generation concept of digital twins and corresponding technologies enabling this, we will highlight the potential of real-time digital twins alongside several real-life Siemens use cases. For example, for rotary machinery and especially electric motors xDT-based virtual sensors allow for the monitoring of temperatures and vibrations with so far unmatched accuracy in real time. Moreover, the reliable online detection of faults and the prediction of their evolution becomes possible which is key for any x-as-service business. This was not possible so far with purely data-based approaches, but due to the combination of physics-based models from simulation and AI-based approaches, a reliable prediction of system states is feasible.
digital twin, model order reduction, simulation@operation, fast simulation
09:15
conference time (CEST, Berlin)
Simulation Based on Distributed Digital Twins - Today and Tomorrow
26/10/2021 09:15 conference time (CEST, Berlin)
Room: E
D. Penner (EKS InTec GmbH, DEU); E. Bayrhammer (Fraunhofer IFF, DEU); H.C. Avgoustinos, Robin Thrift (EXPO21XX GmbH, DEU)
D. Penner (EKS InTec GmbH, DEU); E. Bayrhammer (Fraunhofer IFF, DEU); H.C. Avgoustinos, Robin Thrift (EXPO21XX GmbH, DEU)
The industrial installation of the future represents a paradigm shift: From a conventionally developed, static configuration, to a network of self-organising decentralised components. The industrial installation exists only as long as it is needed for the process. The components can leave the network and join a new one if necessary to implement a new system for a different process. A required aspect for successful reconfiguration, as well as operation and maintenance of a component network are simulations, such as behavioural simulations, control simulations, and 3D-simulations. The self-organising decentralised components must be able to perform such simulations themselves or provide information and interfaces so that distributed simulations can be performed with contextual data. To enable the described scenario, component data and operational data needs to be collected and made available throughout the components life-cycle. Formally this is referred to as the Distributed Digital Twin (DDT). A Distributed Digital Twin is a requirement for the concrete design of a Digital Twin (DT). It is explicitly designed for components and their integration and coupling into component networks to build complete systems. The Distributed Digital Twin includes real time data, constructive data and different sets of relations describing their possible interaction and integration into higher level systems. This paper presents a manufacturing use case in order to derive the requirements for simulations of distributed components (co-simulation) and for their Digital Twins. Based on this, the concept of the distributed digital twin is introduced. We discuss how it helps to support the digital continuity and integration of components, their data and simulation models from several stakeholders. It shows how simulations can be implemented based on Distributed Digital Twins and the utilisation of standardised methods like FMI/FMU. It is discussed which fundamental possibilities this creates for future manufacturing. The manufacturing use case shows in a practical way which parts of this concept can already be implemented in today's industrial environment.
Distributed Digital Twin, Simulation, FMI/FMU, Docker, Asset Administration Shell, Edge Devices, Micro Services, Co-simulation
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