In the context of digitalization and Industry 4.0, companies face increasing competitive pressure, emphasizing the need for efficient maintenance strategies. This thesis aims to develop a systematic method for determining characteristic measurement variables in existing plants to evaluate the condition of critical components and enable the implementation of condition-based maintenance. The proposed methodology integrates existing approaches with novel perspectives, systematically analyzing load cases and deriving relevant measurement variables. Mechanical stresses are evaluated through functional and structural analyses to identify physical influencing factors. A key focus is ensuring the method’s adaptability across various application scenarios. The methodology determines whether relevant variables can be measured directly or need to be derived indirectly from other parameters. To validate the approach, the method was applied to three industrial use cases: wind turbines, intralogistics systems, and bolted connections. Use case of wind turbine is throughoutly presented in a paper. The results demonstrate significant improvements in the accuracy of identifying critical components and enhanced efficiency in planning and executing condition-based maintenance strategies. This paper provides a substantial contribution to the sustainable optimization of industrial processes and establishes a foundation for future research in predictive maintenance.