ESI know-how: towards the next level of system engineering 

TNO-ESI know-how cycle

TNO-ESI know-how cycle drives

the innovation of high-tech product and service development

1. Define business value

Translate business goals to data applications

The TNO-ESI know-how starts with – iteratively – understanding and translating business goals into data-driven application areas and requirements. For instance, product customizations may lead to a need for better system usage insights and dedicated testing, which subsequently could lead to a better definition of product customization and the identification of new customization strategies.

2. Methodolgy development

Integrate data and knowledge into system-level reasoning

The next step is to support system engineers with methodologies that integrate knowledge and data into a system-level reasoning framework. For knowledge engineering, we use domain-specific languages to systematically model domain knowledge, often scattered across documents, or in the heads of experts. For data-driven business applications, knowledge engineering supports the effective analysis of operational data, by applying, for example, feature selection or constraints in learning algorithms. Exploitation of profiling, process and data mining techniques allow the generation of context-specific operational models that can support, among others, automated testing or customization of system operations.

We demonstrate the added value of our methodologies by bringing together appropriate techniques and tools in customer-specific prototypes. The integration of knowledge-driven and data-driven approaches enable continuous system evolution and operational support, thereby reusing and strengthening the company’s knowledge. 

3. Competence leverage

System Engineering support

TNO-ESI supports successful deployment of the results by improving companies’ processes and competencies. 

system engineering support

Overview data related projects

esi solutions

In an industrial demonstrator, we combine system engineering knowledge and various data analysis techniques to show the benefit of utilizing operational data in high-tech industry. Read more