## 4 Concept  
The solution approach consists of merging various methods--previously largely applied separately--with which the quality of the data can be planned, controlled, and produced into a holistic methodology. The approach to quality assurance for ML applications presented in the following, especially in cyberphysical production systems, is based on the V-model of software development [48] and the V-model for mechatronic systems [49]. The approach is holistic. This means that all areas of influence on data quality and consequently on analysis quality are included. This means that the entire chain of data processing must be taken into account. The chain of data processing begins with data generation and the creation of the analysis-capable database continues through the gain of knowledge using applied analysis algorithms. Finally, it extends to the decision or the initiation of the actions that are justified from the analysis results--at first glance, this tends to address software issues. But in actual when it comes to the question of generating the data that must be suitable for answering the analysis task and transferring it to the database in a robust and performant manner, it becomes clear that hardware development plays an equally important role. Under the focus of CPPS, hardware development includes the design of the system from actuators, sensors to mechanical components, from transmission elements and interfaces in IT to humans. One crucial thing for the quality of the data is the \"data-fair design\" of the CPPS. Data-fair design means that during the system development the later data workflow must be thought of already, which is usually possible only after the commissioning of the CPPS with the data won from it. For this reason, the viewpoint of the DMME [33; 34] was included in the concept presented here and integrated into the contents of the individual workflows.  
Figure 3 shows the concept based on the V-model. The approach is divided into the spheres of influence \"Planning and installation of a CPPS\" (left-hand path), \"Operation of Production system\" or CPPS (base of the V), and \"Data processing\" (right-hand path)--equivalent to the phases of the classic V models. Under \"Planning and Installation\", the left branch describes the relevant subtasks of the engineering activity from conceptual design to the implementation of task-appropriate data acquisition in the executable CPPS. The executable CPPS (including the influencing periphery) generates the required data during its operation in the required production process. With the provision of sensor and actuator data, the workflow of data processing begins up to the interpretable analysis result, as this is illustrated in the right branch of the V-model with \"Data processing\".  
Both workflows pass through four action levels. The action levels are at the same time influence levels on the data quality, thus on the analysis quality, and finally on the effectiveness of the ML application. In the levels, there are different types of influences on the data quality, which are to be worked out and assigned here systematically. In this way, the influences are to be separated from each other so that they can be identified as quality characteristics and treated individually. In this way, specific measures to ensure data quality can be developed and implemented. The complexity can be mastered in this way.