Ton Peijnenburg, Fellow HTSC and Deputy Director VDL-ETG
Systems Engineering (SE) is gaining traction at the university and in the high-tech industry.
Systems Engineering (SE) is gaining traction at the university and in the high-tech industry. Although it cannot match the current buzz around a topic like Artificial Intelligence, interest is genuinely on the rise. Driven by the desire to better manage the research and development of increasingly complex high-tech systems, SE and its more recent extensions to Model Based SE (MBSE) are coming under broad consideration in high-tech NL. At HTSC, we are now driving the creation of a first TU/e original SE training for PDEng programs based on existing TU/e SE courses and aiming to create a common SE language. That common language can act as a reference for related courses for the bachelor and graduate schools, enabling a common interpretation of SE in the high-tech industry. In a way, such a de facto standard for SE is not unlike those for design principles and control engineering that have evolved over decades in Dutch universities and industry. In the HTSC spirit, the SE training will combine formal education with industrial practice. The training will extend to the PDEng final assignment, and the PDEng trainee coaches will be involved in the SE methodology as well. We hope the same also goes for the company coaches!
With HTSC and the Eindhoven AI Systems Institute (EAISI) becoming integrated, research attention will shift to the overlay between AI and SE. In a previous artical Marc Hamilton discussed Digital Engineering, whereby digital tools evolve to better support the engineering process. One of the research topics in EAISI is the Thinking Assistant – it is even articulated as a moonshot project. Such a thinking assistant, when tasked with manipulating design-related data, may be able to support design engineers and systems engineers in their jobs. The thinking assistant may be able to access all relevant current and historic data for a certain subset of a system, consider all relevant parameters for a design decision, involve historic field performance data to drive design improvements, and generate lots of failure modes for an (automated) FMEA analysis of the system under development. The term assistant is well chosen in the context of design support, much as in automotive applications where a driver assistant is more realistic than an autopilot as a feasible target and desirable goal.