Towards digitalization of hydraulic systems using soft sensor networks

Today buzzwords like “smart machine” and “intelligent component” dominate the discussion about digitalization in the fluid power domain. However, the engineering fundamentals behind the words “smart” and “intelligent” often remain unclear. A common and target-oriented discussion needs transparent approaches including the applied technical system understanding. Therefore, t his paper presents new concepts of soft sensor networks which allow the aggregation of information about fluid systems from heterogeneous sources. Soft sensors presented in this paper are physical models of system components that ensure transparency. Soft sensors and soft sensor networks are applied on exemplary hydraulic systems on three different levels: (i) the sensor level, (ii) the component level and (iii) the system level

Die Nationale Forschungsdateninfrastruktur für die Ingenieurwissenschaften (NFDI4Ing)

Das Konsortium NFDI4Ing wurde 2017 gegründet und legt einen Schwerpunkt seiner Arbeit auf die Identifizierung und Harmonisierung spezifischer, datengenerierender Ingenieurtätigkeiten. Das Arbeitsprogramm des Konsortiums ist modular aufgebaut und zeichnet sich durch die Einführung sogenannter methodenorientierter Archetypen aus. Dabei werden fachspezifische Use Cases zusammen mit Nutzenden aus den Ingenieurswissenschaften entlang der Forschungsprozesse kontinuierlich ausbaut. NFDI4Ing besitzt drei Community-bezogene Schwerpunkte: Die Gewährleistung von Datenkompetenz durch Aus- und Weiterbildung, die Förderung von technologischen Lösungen und Methoden, sowie die Förderung von Data Governance und Kuration

Creating application-specific metadata profiles while improving interoperability and consistency of research data for the engineering sciences

Due to the heterogeneity of data, methods, experiments, and research questions and the necessity to describe flexible and short-lived setups, no widely used subject-specific metadata schemata or terminologies have been established for the field of engineering (as well as for other disciplines facing similar challenges). Nevertheless, it is highly desirable to realize consistent and machine-actionable documentation of research data via structured metadata. In this article, we introduce a way to create subject specific RDF-compliant metadata profiles (in the sense of SHACL shapes) that allow precise and flexible documentation of research processes and data. We introduce a hierarchical inheritance concept for the profiles that we combine with a strategy that uses composition of relatively simple modular profiles to model complex setups. As a result, the individual profiles are highly reusable and can be applied in different contexts, which, in turn, increases the interoperability of the resulting data. We also demonstrate that it is possible to achieve a level of detail that is sufficiently specific for most applications, even when only general terms are available within existing terminologies, avoiding the need to create highly specific terminologies that would only have limited reusability

Listing 6

Working example for listing 6 in the article titled "Creating application-specific metadata profiles while improving interoperability and consistency of research data in engineering", consisting of: one turtle file containing the shapes graph (metadata profiles), one turtle file containing the data graph (valid as well as invalid example data), the python file that runs the validation and returns the report, one text file containing the content of the report

Listing 4

Working example for listing 4 in the article titled "Creating application-specific metadata profiles while improving interoperability and consistency of research data in engineering", consisting of: one turtle file containing the shapes graph (metadata profiles), one turtle file containing the data graph (valid as well as invalid example data), the python file that runs the validation and returns the report, one text file containing the content of the report

Listing 1

Working example for listing 1 in the article titled "Creating application-specific metadata profiles while improving interoperability and consistency of research data in engineering", consisting of: one turtle file containing the shapes graph (metadata profiles), one turtle file containing the data graph (valid as well as invalid example data), the python file that runs the validation and returns the report, one text file containing the content of the report

Listing 2

Working example for listing 2 in the article titled "Creating application-specific metadata profiles while improving interoperability and consistency of research data in engineering", consisting of: one turtle file containing the shapes graph (metadata profiles), one turtle file containing the data graph (valid as well as invalid example data), the python file that runs the validation and returns the report, one text file containing the content of the report

Listing 3

Working example for listing 3 in the article titled "Creating application-specific metadata profiles while improving interoperability and consistency of research data in engineering", consisting of: one turtle file containing the shapes graph (metadata profiles), one turtle file containing the data graph (valid as well as invalid example data), the python file that runs the validation and returns the report, one text file containing the content of the report

Listing 7 & 8

Working example for listings 7 & 8 in the article titled "Creating application-specific metadata profiles while improving interoperability and consistency of research data in engineering", consisting of: one turtle file containing the shapes graph (metadata profiles), one turtle file containing the data graph (valid as well as invalid example data), the python file that runs the validation and returns the report, one text file containing the content of the report