Abstract: A method and a calculation unit for calculating a load skewness result for a determination of service intervals for a turbine, includes: receiving a continuously measured load signal over time, indicating a load of the turbine during operation thereof; determining a load percentage signal over time as a deviation of the received measured load signal from a maximum capacity signal, wherein the maximum capacity signal represents a maximal possible load to be generated at a given time and is calculated from continuously measured sensor signals, representing operating parameters of turbine operation; calculating a load percentage distribution based on the determined load percentage signal; calculating a load skewness factor as a measure of an asymmetry of the calculated load percentage distribution; providing the load skewness result, including the calculated load percentage distribution and the calculated load skewness factor.

Abstract: A method and a calculation unit for calculating a load skewness result for a determination of service intervals for a turbine, includes: receiving a continuously measured load signal over time, indicating a load of the turbine during operation thereof; determining a load percentage signal over time as a deviation of the received measured load signal from a maximum capacity signal, wherein the maximum capacity signal represents a maximal possible load to be generated at a given time and is calculated from continuously measured sensor signals, representing operating parameters of turbine operation; calculating a load percentage distribution based on the determined load percentage signal; calculating a load skewness factor as a measure of an asymmetry of the calculated load percentage distribution; providing the load skewness result, including the calculated load percentage distribution and the calculated load skewness factor.

Abstract: Propagating data in a technical network by considering runtime requirements. A component tree data structure is generated for a probabilistic graph representing the technical network and its technical constraints. On the component tree a propagation algorithm is applied, which iteratively determines an optimal edge in the generated component tree, which maximizes an expected information flow to a query node to and/or from each network node by considering the technical network constraints and by executing a Monte-Carlo sampling for estimation of the expected information flow for the cyclic components and by computing the expected information flow of the non-cyclic components analytically and which updates the component tree iteratively with each determined optimal edge and re-estimates the expected information flow in the updated component tree for providing a result with nodes in the technical network for data propagation, so that information flow is maximized by considering technical network constraints.

Abstract: Computer-aided processing of data from a number of data sources is provided. The data is analyzed with respect to a plurality of data quality types based on respective analysis methods for each data quality type, resulting in a quality value for each data quality type. The quality values for the respective data quality types are visualized on a graphical user interface.