In the field of Maintenance, Repair and Overhaul (MRO), stakeholders such as operators or service providers have to keep track of the health status of fleets of complex systems. The ability to estimate the future health status of these systems and their components becomes more pivotal when seeking to efficiently operate and maintain these systems. Today, these stakeholders have access to a lot of different data sources regarding fleet, operation schedule, ambient condition, system and component information. Many different prognostic methods from different disciplines are available and will further improve henceforward. In many cases these data sources and methods function as isolated methods in their own field. This fragmentation makes a holistic prognosis very challenging in many cases. Therefore, stakeholders need information integrating methods and tools to gain an exhaustive insight into the health status development of the complex assets they are operating or maintaining, in order to make well-founded decisions regarding operation or maintenance planning. In this paper, a Python-based health index framework is presented. It enables users to integrate operation schedules of different detail levels with enriching data sources such as ambient condition data. Furthermore, it provides methods to design complex asset systems which are linked via their construction, function or degradation mechanisms/ health indices via transfer relations. It allows to monitor the asset’s condition based on operation data and to simulate different operation scenarios regarding the health index development.

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