Scientific publications

Today’s complexity of high-tech systems makes diagnosing system failures a tough task for service engineers. Increasing product variability and fast market introduction of new generation systems prohibit the expertise build-up that served service engineers in the past. Traditionally, system knowledge is transferred to the service organization through service manuals and training.

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.

We are a leading applied research center for systems design and engineering in the high-tech equipment industry. We work in close collaboration with Dutch industry as well as in strong association with the fundamental research of academia, both nationally and internationally. We contribute to society and the economy by driving advances in high-tech systems technology through a strong shared research program, dedicated innovation support, a focused competence development program and various knowledge- and experience-sharing activities.

This document describes an initial version of the ASIMOV reference architecture, which consists of definitions of terms and multiple architectural views. In addition, this document contains a delimitation of the ASIMOV project tasks in terms of this reference architecture. The description of the reference architecture consists of both a generic architecture and applications to the use cases from the ASIMOV project.

This report describes the state of the art in reinforcement learning and digital twin-based learning, and the first ideas on their application in the ASIMOV use cases. It will be the foundation for further work on researching these techniques in the ASIMOV use cases, which will enable expansion of the knowledge in these fields for general application in the high-tech industry.

Development of high-tech systems is a complex task done by diverse specialists distributed across the globe. Reference architectures including a clear functional breakdowns can support them and support their decisions. This presentation proposes an approach to improve the development of advanced electron microscopes by using Capella as an authoritative source of information.

In a constantly changing world, developers need to analyze and transform their code to keep it up-to-date and valuable. Currently, analysis and transformation is a time-consuming and largely manual activity. The Renaissance approach aims to enhance insight by analysis and to reduce complexity by transformation.