## 4 Concept
### Planning and Installation
#### 4.1.2. Conception of the ML Application  
The objective definition is followed by the task \"Conception of the ML application\". This step initially involves developing ideas about the physical effects that can be used to implement the ML applications. For example, bearing wear can be indicated by the power consumption of the drive motors via bearing temperature or vibrations at the bearing. For the measurement of the physical effects, technical concepts must be developed with which the required data can be generated. Ideas for the algorithmic procedure and the mathematical modeling of the relationships sought should already be developed in the early phase since these also place demands on measurement methods and data acquisition. Here, it is essential to analyze the quality of the interactions (linear, nonlinear, number of influencing parameters, etc.). With the number of influencing parameters to be considered, it must be clarified whether further data sources are necessary, for example, software systems for material or tool management, hall climate management. For the use of multiple data sources, a reliable synchronization approach, e.g., timestamp or ID, must be developed. This determines the allocation of the data from the different sources and thus the data quality. Furthermore, the ML application has to be adapted to the constraints of the CPPS and its environment like network infrastructure. In short, the algorithmic concept must fit the technical concept. The algorithmic concept includes the mathematical workflow from data preprocessing to synchronization of data sources to models describing the effects. Usually, there are different solution principles, from which the best one is selected in this task--considering effort and benefit, integrability, and stability.  
This task also plans the experimental studies required to represent the effects on the CPPS [50]. In the experimental plan to be created, it will be determined which tests will be performed for commissioning the system and which series of tests, if any, will be performed for learning from the ML models.  
From a data quality point of view, fundamental decisions are already made here. The selected physical effect and the selected measurement approach provide the physical context information that has to be investigated with the data analyses. With the measurement approach, the physical effects are determined directly or indirectly. Thus, the required data quality is determined by the analysis task. That is, whether regression analyses or Fourier analyses are targeted will affect the required data quality. In addition, the distance of the sensor from the effective point is determined in principle during the design phase. Both determine the achievable measurement accuracy and the risk of interfering disturbances. The IT concept for data transmission determines its performance and thus limits the amount of data that can be transmitted and thus the usable resolution of the measurements. The context information created in this task allows a context-related plausibility check or correction of the acquired data in the model phase \"data processing\" (Section 4.3).