In this PhD project, we are trying to create reliable and agile digital twins of the industrial processes that enable data analytics with performance and safety guarantees. To this end, we will develop reconfigurable and self-tuning control systems by integrating system health-monitoring and real-time decision making using both model-based and data-driven approaches in a probabilistically robust framework. A set-based probabilistic approach to optimal fault detection will be pursued using non-convex scenario optimization (combining models and data). Eventually, the proposed control scheme will be implemented on industrial demonstrators and use cases.