Insight最新文献

The high-tech equipment industry brings complex industrial products to the market with high speed, enhanced functionality, a better cost-performance ratio, and greater integration into customer workflows. Driven by digitalization, the complexity of these systems continues to grow steeply. To manage this complexity, continuous innovation in systems engineering methodologies is needed. TNO-ESI targets to 1) create impactful and industrially applicable systems engineering methodologies and 2) provide innovation support to the industry to get these applied in an industrial context. The ESI research program is defined through a roadmapping process that follows two tracks: a roadmap that maps industry needs and related research and development requirements and a roadmap that describes the developments in the expertise areas necessary for addressing these industry needs. In this paper, we describe the ESI mission, our way of working and activities, and explain the roadmapping process and the roadmaps.

The research and development activities performed by the DLR Institute of Systems Engineering for Future Mobility (DLR-SE) are organized via so-called assets. We present a scenario-based verification and validation process and relate selected research activities.

Verification and validation approaches of automated transportation systems based on driving a certain number of kilometers are infeasible. Therefore, the DLR-SE asset “Scenario-based Verification and Validation of Automated Transportation Systems” investigates methods and prototyping tools for verifying and validating automated transportation systems employing scenarios as the main structuring element to capture complex traffic evolutions. While there are many different approaches, our focus is formally specifying relevant abstract scenarios that are readable by humans while also being machine-readable. This allows us to automatize the verification and validation process, which increases confidence in, for example, the safety of the systems due to a dramatically increased number of executed tests while reducing the manual effort from humans.

As industrial cyber-physical systems grow ever more complex, their software grows naturally and changes continuously. In order to make risk-free changes to their software, it is crucial to understand how the system behaves, and how software changes have an impact on system behavior. We propose a generic two-fold approach to infer state machine models capturing system behavior, and to compare these models to determine and visualize the impact of software changes on system behavior, in a way to make them easily understandable for engineers. Our approach has been applied in the industry at ASML to help prevent software regression problems during critical software redesigns. In that, our approach has been shown to reduce risk and to be valuable.

This paper discusses how digital signoffs can enable new operational paradigms for business operations, digital engineering reviews, and contracts. The paper explains what digital signoffs are in the context of their use with model-based systems engineering methods, which is how this concept and construct has evolved. The paper discusses how they are created in the current toolset. This paper explains the benefits of why digital signoffs are valuable, in addition to where they can be placed within models, and when they might be used. Finally, we discuss how digital signoffs might evolve as add-on capabilities for digital engineering more broadly.

The introduction of automation into technical systems promises many benefits, including performance increase, improved resource economy, and fewer harmful accidents. In particular, in the automotive sector, automated driving is seen as one key element in Vision Zero by eliminating common accident causes such as driving under the influence, reckless behavior, or distracted drivers. However, this is contrasted by new failure modes and hazards from the latest technologies. In this article, we address the problems of finding common sources of criticality for specific application classes and identifying and quantitatively assessing new sources of harm within particular automated driving systems.

Ever-increasing system complexity is challenging for development engineers and service personnel troubleshooting system failures in the field. This paper presents a systematic, scalable approach to attain a diagnostic model. Automatic transformation into computational models is used 1) at design time to improve the diagnosability of the system, and 2) during operation for guided root cause analysis by calculating the most probable failures and suggesting diagnostic procedures based on available data and observations. The approach combines nicely with model-based systems engineering, showing the added value of using diagnostic models both during the design of a system and during operation when the system needs to be diagnosed.

The new DLR Institute of Systems Engineering for Future Mobility (DLR SE) opened its doors at the beginning of 2022. As the new DLR institute emerged from the former OFFIS Division Transportation, it can draw on more than 30 years of experience in the research field on safety critical systems. With the transition to the German Aerospace Center (DLR), the institute has developed a new research roadmap focusing on technical trustworthiness for highly automated and autonomous systems, as described in the article “DLR Institute of Systems Engineering for Future Mobility – Technical Trustworthiness as a Basis for Highly Automated and Autonomous Systems” in this journal. In this paper, we describe how the Group Human Centered Engineering (HCE) contributes to this roadmap with our methods of “virtual test drivers” and “virtual co-drivers.”

TECoSA – a university-based research center in collaboration with industry – was established early in 2020, focusing on Trustworthy Edge Computing Systems and Applications. This article summarizes and assesses the current trends and drivers regarding edge computing. In our analysis, edge computing provided by mobile network operators will be the initial dominating form of this new computing paradigm for the coming decade. These insights form the basis for the research agenda of the TECoSA center, highlighting more advanced use cases, including AR/VR/Cognitive Assistance, cyber-physical systems, and distributed machine learning. The article further elaborates on the identified strategic directions given these trends, emphasizing testbeds and collaborative multidisciplinary research.