Linking digital twins to use time series for predictive maintenance at component level

Kurzfassung

In complex systems, the life cycle of components is often detached from the life cycles of the overlying systems. For example, individual systems or components are removed from the vehicle for maintenance and sent for overhaul and replaced with components that have already been overhauled. This also increases the complexity for the digital twins of these systems, since the data is often processed and stored in the overlying systems or at the overall system level. An example could be the landing gear for an aircraft, which is removed from aircraft A after its lifetime or usage limits have expired and is replaced by an already overhauled landing gear. After maintenance, the landing gear is installed on Aircraft B. (Figure 1-1) In addition to changing the system affiliation, the owner, operator or another stakeholder role can also change. This greatly increases the complexity of mapping the digital twin. Since there can be competitive situations between the various stakeholders, not all parameters should be available for every point in the life cycle of the higher-level asset. A concept is therefore required that ensures that only the relevant data is provided for the period of affiliation.