Abstract Digital twins couple computational models with physical systems to enable services such as predictive reasoning and runtime analysis that support decision–making for maintenance, optimisation and system evolution. They create a technically rich setting for applying formal methods throughout the system lifecycle beyond design-time. This tutorial introduces the elements of digital twin technology, identifying emerging opportunities for formal techniques, including the verification of evolving models under uncertainty, integration of heterogeneous models and rigorous composition of digital twin-enabled systems.

We propose a novel event-trigger threshold (ETT) regulation mechanismETTρbased on Signal Temporal Logic (STL) properties significantly extending recent work on ETT regulation for Propositional Logic properties. We utilize the quantitative semantics of STL to construct a method for computing and merging suitable ETTs for different requirements in complex STL specifications. Contrary to related work on event-triggered control for STL properties, we apply the event-triggering logic to the measured signals individually rather than the control output and assume that an existing periodic controller is defined. By exploiting the early satisfaction detection capabilities of STL and analyzing the property structure, our method aims to reduce the number of triggered events, while maintaining satisfaction of system properties. To evaluateETTρ, we consider a simulated adaptive cruise control case-study where STL is used to encode complex safety and performance properties and the ETTs of measured signals are regulated accordingly. We test three different properties in two different scenarios to showcase how STL andETTρcan identify intricate circumstances where it is possible to significantly reduce the number of triggered events relative to a constant ETT.

Abstract It is often the case that a robot must adapt to unexpected changes in its environment. It is, however, important that these changes can be demonstrated to maintain the safe operation of the robot. The adaptive systems community has developed the MAPE-K pattern as a widely recognised conceptual architecture. We propose extending MAPE-K to incorporate runtime verification, resulting in an architecture we call MAPLE-K. In this paper, we capture and formalise both the MAPE-K and MAPLE-K architectures using a domain-specific language. Additionally, we provide support for translation from architectural models to software models and code to facilitate the deployment of verified applications. MAPE-K is rarely maintained at the implementation level, but our work ensures traceability between the code and its design, enabling the use of architectural information to verify the correctness of the software.

We introduce a novel event-trigger threshold (ETT) regulation mechanism based on the quantitative semantics of propositional logic (PL). We exploit the expressiveness of the PL vocabulary to deliver a precise and flexible specification of ETT regulation based on system requirements and properties. Additionally, we present a modified ETT regulation mechanism that provides formal guarantees for satisfaction/violation detection of arbitrary PL properties. To validate our proposed method, we consider a convoy of vehicles in an adaptive cruise control scenario. In this scenario, the PL operators are used to encode safety properties and the ETTs are regulated accordingly, e.g., if our safety metric is high there can be a higher ETT threshold, while a smaller threshold is used when the system is approaching unsafe conditions. Under ideal ETT regulation conditions in this safety scenario, we show that reductions between 41.8% and 96.3% in the number of triggered events is possible compared to using a constant ETT while maintaining similar safety conditions.

When different simulation units are coupled together there are different choices to take, in particular regarding the granularity of such a co-simulation. When prototyping systems, it is typically favourable to get an initial idea of how a collection of simulation units work together without spending too much time setting up the orchestration. However, the granularity of such a simulation may be far away from what is needed in relation to the purpose of the simulation. In order to enable more flexibility and control over the co-simulation it is necessary to be able to steer the orchestration in a more detailed manner. This paper presents an open source co-simulation orchestration engine based on the Functional Mockup Interface standard but with a Domain Specific Language (DSL) enabling detailed control between the individual simulation units. The same tool can thus be used right out of the box for low-granularity co-simulation, and for high-granularity simulation the DSL enable a significant flexibility.

Abstract Digital twin (DT) technology has been a topic with academic and industrial coverage in recent years. DTs are intended to be a virtual high‐fidelity representation of a physical counterpart. Its complex nature requires several components to create and run a DT, and that is why many DT frameworks have been proposed in the literature. There are also many surveys of DTs, but none that is bottom‐up with concrete examples and focused on open‐source software. This survey analyzes 14 open‐source DT frameworks in 10 different dimensions, which are then categorized in six different groups according to their modeling and technological domain, to present the reader different options for creating and managing DT applications, and to understand potential combinations, uses, and limitations of the tools. It also presents a case study with five of the explored DT frameworks, describing the process on how the DT is set up and comparing their capabilities based on the services to be provided by the DT. Finally, it discusses advantages and limitations of the tools according to domain, requirements, and scope, relevant aspects regarding built‐in simulations and data analytics, theory‐to‐practice transition, and advantages/disadvantages of using open‐source software instead of commercial. Main limitations of the study due to its narrow niche, conclusions, and opportunities for future research regarding the potential room for improvement in terms of out‐of‐the‐box features and services for DTs, are also shown.

Evaluating the product quality in an assembly machine is critical yet time-consuming since, in product assessment in batch manufacturing, a certain amount of products should be investigated in an invasive manner. However, continuous manufacturing ensures product quality assessment during assembly with high efficiency and traceability. This paper proposes a quality assessment method for an industrial use case. First, the data is prepared based on two indicators and expert knowledge. Then two data classification approaches (one-class classification and binary classification) are applied to evaluate the products’ quality by analysing the related data. Finally, the most efficient model is selected to predict the product labels and deviate anomalies from normal products. For the studied use case and the limited number of products, the binary classifier guarantees to detect 100% of defective products. The proposed approach can provide the engineers and operators with understandable extracted process knowledge, and can therefore be adapted to a high-speed manufacturing line where large data volume and process complexity can be problematic.

A digital twin for a Cyber-Physical System includes a simulation model that predicts how a physical system should behave. We show how to quantify and characterise violation events for a given safety property for the physical system. The analysis uses the digital twin to inform a runtime monitor that checks whether the noise and violations observed fall within expected statistical distributions. The results allow engineers to determine the best system configuration through what-if analysis. We illustrate our approach with a case study of an agricultural vehicle.

Simulation-based analyses of Cyber-Physical Systems are fundamental in industrial design and testing approaches. The utility of analyses relies on the correct configuration of the simulation tools, which can be highly complicated. System engineers can normally judge the results, and either evaluate multiple simulation algorithms, or change the models. However, this is not possible in a co-simulation approach. Co-simulation is a technique to perform full-system simulation, by combining multiple black-box simulators, each responsible for a part of the system. In this paper, we demonstrate the difficulty of correctly configuring a co-simulation scenario using an industrial case study. We propose an approach to tackle this challenge by allowing multiple engineers, specialized in different domains, to encode some of their experience in the form of hints. These hints, together with state-of-the-art best practices, are then used to semi-automatically guide the configuration process of the co-simulation. We report the application of this approach to a use case proposed by our industrial partners, and discuss some of the lessons learned.