As the emerging digitalization of technical systems offers immense opportunities to be exploited by means of big data analysis, ubiquitous computing and largely networked systems, the digital twin comes into focus to combine all these aspects to an attendant model of an individual system during design phase as well as during operation. Since state-of-art technical systems are growing increasingly complex due to inherent intelligence and increasing functionality, i. e. autonomous behavior so far, it becomes considerably challenging to ensure reliability for those systems. Many methods were developed to support a reliability focused design or reliability-by-design approaches to tackle this challenge during design process. In field, data-based methods, i. e. condition monitoring enabled by the rise of machine learning approaches, are exploited to ensure a reliable operation based on the current condition of the monitored system. In order to take advantage of existing models of system reliability during design phase and condition monitoring systems during operation, a method is proposed to combine both approaches in order to set up a digital twin with focus on system reliability. The base model of the digital twin is taken from the system reliability model from the design phase and is used during operation and therein updated to the current reliability based on the state estimation of the condition monitoring system. The approach is illustrated with a case study of a rolling bearing test rig.