Scientific publications

One of the goals of the CareFree 2025 project has been the development of a methodology that connects Model-Based Systems Engineering (MBSE) to diagnostics, which is the process of identifying faulty hardware components in case of a system failure. For this purpose, we started with a methodology developed earlier in de SD2Act project, and applied it to a large and complex module in a realistic industrial system: a high-volume printer developed by Canon Production Printing.

For industry, including high-tech manufacturing industry and OEMs, it is foreseeable that new regulations will be stricter on environmental sustainability and circularity indicators. The cus-tomers (who are businesses themselves) will face the same regulations and will therefore increasingly push their suppliers to comply.

This document presents TNO-ESI’s Vision on System Qualities Reasoning for the Dutch high tech equipment industry. It is written for industry leaders, system architects, R&D strategists, and researchers who need to understand how system qualities such as productivity, energy efficiency, resource usage, and cost can be systematically engineered across increasingly complex, interconnected, and software-defined systems.

Synthesis based engineering (SBE) is an approach in which supervisor synthesis is utilized to automatically generate a correct-by-construction supervisor for a cyber-physical system. The Eclipse Supervisory Control Engineering Toolkit (ESCET—) project develops a state-of-the-art toolkit supporting the entire SBE workflow mainly through the CIF language and tools.

This project investigates the application of Synthesis-Based Engineering (SBE) to the TNO Embedded Systems Innovation (TNO-ESI) demo system, a modular cyber-physical training factory. The objective is to demonstrate the power of SBE in the development of industrial control software by automatically synthesizing correct-by-construction supervisory controllers from formal specifications.

The high-tech manufacturing industry, dominated by large multinational corporations, faces pressure to innovate faster to remain competitive, producing more sophisticated machines with better performance and quality. In recent years, besides the push for better performance, sustainability and circularity have also gained importance, due to (1) regulations that are becoming broader in scope, stricter in thresholds, and more complex in requirements, (2) customer demands and expectations and (3) internal strategic objectives.

High-tech systems are growing more complex due to mass customization, integration of diverse technologies, and long lifecycle demands. Customers increasingly expect service contracts based on performance and availability, yet diagnostics remain largely reactive and reliant on human expertise. This position paper proposes a Pervasive Intelligent Diagnostics (PID) framework that integrates pervasive sensing, model-based digital twins, and hybrid AI for predictive diagnostics and sustainable lifecycle management.

This report introduces a methodology for diagnosing performance issues in high-tech production systems. The approach uses probabilistic graphical models to combine machine data with expert and design knowledge. By reasoning across multiple representations of the data, multiple resolutions, and across time, the approach can interpret defectivity patterns and infer their actionable root causes.

Actuator faults greatly affect the performance and stability of control systems, an issue that is even more critical for systems required to operate autonomously under adverse environmental conditions, such as unmanned vehicles. To this end, passive fault-tolerant control (PFTC) systems can be employed, namely fixed-gain control laws that guarantee stability both in the nominal case and in the event of faults.

This report examines the challenge of strengthening and formalizing a Software Maintenance and Evolution (SMEvo) ecosystem for the Dutch high-tech industry. Maintenance and evolution of long-lived, software-intensive systems account for a large part, estimated between 75% and 90%, of software lifecycle costs.