PhD on data-driven inspection management and risk assessment for safety critical systems

Are you eager to innovate risk assessment methods and reshape safety certification frameworks for safety critical systems leveraging AI and IoT technologies? This project aims at advancing inspection and data collection strategies optimization, and their integration within novel data-driven risk assessment methodologies and safety certification frameworks. We focus on vertical transport (lifts and escalators) as practical application domain: compared to the critical role that these systems play in ensuring reliable, safe, efficient and comfortable vertical transportation of people and goods, their inspection strategies, safety assessments, and certification approaches have not been updated in the era of IoT and AI. The overarching aim of this PhD research is to develop a novel data-driven safety assessment and certification framework, leveraging multiple data sources and probabilistic reliability analysis to predict both current and future safety levels. This project contributes to designing future standards and safer vertical transport.

Information
The availability of remote monitoring and big data opens up new opportunities to innovate traditional safety assessment and certification methods, which rely on visual inspections and reactive approaches. Future safety assessments can achieve the next level of proactiveness by collecting and analysing the right data in a timely manner, conducting data-driven probabilistic assessments of future failure risk, and optimizing inspection planning. The project will consider the application area of vertical transport (lifts and escalators). The operational data collected by the lift controller, along with records of past events, will be used to enhance the diagnostic and predictive capabilities of the safety level for the individual lift. Combining data-driven probabilistic risk assessment models and inspection planning balances cost of inspection and risk of failures, and accounts for reliability and safety. Furthermore, inspection decisions and safety assessments can be continuously updated based on new information using sequential decision-making frameworks.

Achieving such opportunities still requires creative ideas for novel methodologies based on quantitative analysis with sufficient validation. How should we collect right, timely, reliable data? How do we transfer models and data sourced from multiple, heterogeneous lifts? How do we model uncertainty and explainability of safety assessed by data and AI? How do we design appropriate certification requirements for such a safety-critical system? How do we balance cost, safety, reliability, and availability?

This project aims to find novel answers to such questions, thus developing novel safety assessment methodologies which leverage data-driven techniques and IoT technologies, and apply these to eventually support the design of future safety certification frameworks for the new generation of lifts.

The PhD candidate will design and lead the research project under the academic supervision of Prof. Claudia Fecarotti, Prof. Juseong Lee and Prof Rob Basten, and work in close collaboration with the Liftinstituut, one of the European leading certification and independent notified bodies for safety certification of lifts and escalators. You will publish the results in international journals and conferences to communicate with academia and other relevant stakeholders.

A meaningful job in a dynamic and ambitious university, in an interdisciplinary setting and within an international network. You will work on a beautiful, green campus within walking distance of the central train station. In addition, we offer you:

• Full-time employment for four years, with an intermediate assessment after nine months. You will spend a minimum of 10% of your four-year employment on teaching tasks, with a maximum of 15% per year of your employment.
• Salary and benefits (such as a pension scheme, paid pregnancy and maternity leave, partially paid parental leave) in accordance with the Collective Labour Agreement for Dutch Universities, scale P (min. € 3,059 max. € 3,881).
• A year-end bonus of 8.3% and annual vacation pay of 8%.
• High-quality training programs and other support to grow into a self-aware, autonomous scientific researcher. At TU/e we challenge you to take charge of your own learning process.
• An excellent technical infrastructure, on-campus children's day care and sports facilities.
• An allowance for commuting, working from home and internet costs.
• A Staff Immigration Team and a tax compensation scheme (the 30% facility) for international candidates.