Industrial maintenance is playing an increasingly important role in production systems. The interconnections between machines and systems are becoming more complex, partly due to increasing digitization and automation. Malfunctions and failures not only need more time to be resolved but also have greater consequences on the production system. To respond to these increasing demands, maintenance must make intelligent use of the available resources. One of the most important factors that determines the use of resources and the resulting availability is the selection of the maintenance strategy for the production machines. In literature, there are many approaches to select a maintenance strategy. For complex machines consisting of different components, each of which can have several failure causes, maintenance strategy selection based on failure mode and effect analysis (FMEA) is particularly suitable. The combination of failure probability and failure effect of each component allows an evaluation of different maintenance actions. The expected risk reduction of an action can be compared to its cost. The aim of this publication is to introduce a new approach allowing failure probabilities of the components to be time dependent. With this approach the expected time and usage dependent wear of the components and the respective failure probabilities of each wear state can be introduced into the risk prognosis, leading to a time dependent risk for each failure possibility. This in turn can lead to different ideal maintenance strategies, based on the expected wear state of a component. This new time dependent approach is validated in a real production environment with maintenance experts.