Heuristic Investigation of the Required Amount of Data as Part of the Scalable Data-Based Diagnostic Concept

In this paper the amount of data required for reliable and efficient diagnostic development on an application-oriented basis is examined. Embedded in the scalable data-based diagnostic concept, the research question of the amount of data will be analyzed in a kind of Monte Carlo Simulation. The possible influences of the diagnostic methods (Principal Component Analysis (PCA), and Autoencoder (AE)) will also be considered. The evaluation is two-folded, for each of the methods individually and compared to each other. In addition, it will be evaluated whether data availability can be integrated as a further scalability measure and supports the selection of the diagnostic method.