In many conventional areas in mechanical engineering, such as mechanical design, there are process models for engineers like VDI 2221 that guide through the process with methodological support, provide criteria for evaluating the results and thus ensure quality. Generalized process models such as CRISP-DM, KDD and SEMMA already exist for Data Mining, as well as DMME, DAPLOM or ISO 17359 specifically for production engineering. However, these only focus on the sequence of the necessary tasks in several phases without naming adapted methods or without considering aspects of data analysis. Furthermore, the transferability to new use cases or the reuse of the developed solutions has not yet been addressed. In this paper, based on the stages of the DMME, adapted methodical guidelines for enabling machines to acquire data that is suitable for Data Mining are provided. The methods focus on the identification and prioritization of analysis goals and the design of measurement chains and experiments for the acquisition of training data based on the process and the machine structure. In terms of reusability, approaches to transfer the results into templates will be discussed. The methods are applied in a condition monitoring project for a concrete mixing machine.