Sensorauswahl für Bestandsanlagen

Löpelt, Martin; Wilsky, Philipp; Ruffert, Jörg; Göhlert, N.; Prielipp, Riccardo; Riedel, Ralph

doi:10.3139/104.112087

Cited by 8 publications

(8 citation statements)

References 5 publications

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“…Constraints denote necessary properties of the system, which are required for the occurrence of an effect. An example therefore is a material dependency [18]. Likewise, if the effect can quantitatively be described by an equation, this equation is shown.…”

Section: User Interfacementioning

confidence: 95%

“…An equation in which variables have a linear correlation and others do not, is described as not linear. According to Löpelt et al [18], transmission characteristics or measurement ranges are relevant requirements for sensors. The selection of effects according their magnitude is therefore important and the characteristic value ranges are suplemented, e.g., wether occurring stresses are in the MPa or kPa range.…”

Section: User Interfacementioning

confidence: 99%

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Utilizing a graph data structure to model physical effects and dependencies between different physical variables for the systematic identification of sensory effects in design elements

Kraus¹,

Matzke²,

Kirchner³

2022

DS 119: Proceedings of the 33rd Symposium Design for X (DFX2022)

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Gaining accurate data from technical systems has become of interest, particularly in the context of condition monitoring and predictive maintenance. Hereby it is important to gather precise and reliable data. To accomplish this task, various sensors with different physical effects are used. Depending on the sensor's position and measurand, different models are necessary to describe the path from the desired variable of interest to the actual measured one. To support designers, a physical effect catalog was digitalized using a graph data structure, which uses the inherent properties of a graph to represent physical variables, physical effects and their relationships. This graph structure together with its applicability in a sensor selection process will be shown in this paper.

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Section: User Interfacementioning

confidence: 95%

Section: User Interfacementioning

confidence: 99%

Utilizing a graph data structure to model physical effects and dependencies between different physical variables for the systematic identification of sensory effects in design elements

Kraus¹,

Matzke²,

Kirchner³

2022

DS 119: Proceedings of the 33rd Symposium Design for X (DFX2022)

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show abstract

“…The research topic of sensor-integrating machine elements and their development is an interdisciplinary challenge by definition Anderl. [26][27][28][29][30][31][32] touching basic machine elements, mechatronic product development, as well as metrology. Hence, the terminology may be initially ambiguous and controversial; a definition of the most important idioms is required.…”

Section: Terminology: From Sensor Principles To Measuring Proceduresmentioning

confidence: 99%

A Review on Sensor‐Integrating Machine Elements

Kirchner,

Wallmersperger,

Gwosch

et al. 2024

Advanced Sensor Research

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This contribution summarizes the current state of research regarding so‐called sensor‐integrating machine elements as an enabler of digitalization in mechanical engineering and——if available—their application in industry. The focus is on the methodical aspects of the development of these machine elements in general as well as specific sensor‐integrating machine elements that are either already in use or currently under development. Developmental aspects include the robust design of initially evaluated concepts for sensor‐integrating machine elements as well as their modularization. Smart materials with sensory functions are included in the analysis as well as the differentiation with regard to add‐on sensors. The aim of the authors interlinked by a special research program funded by the German Research Foundation (DFG) is to facilitate the exchange with other researchers with the help of the comprehensive overview given in this contribution. The contribution concludes with a brief discussion of open challenges, such as the energy supply and data transfer in rotating systems and also data security.

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“…Based on these considerations, Classe (1988), Zeller (1995) and Löpelt et al (2019) have further developed the sensor selection procedure. In addition to the main procedure steps, they also introduce subdivisions of the procedure steps, thereby contributing to the systematization of sensor selection.…”

Section: Fundamentals Of Systematic Approaches To Sensor Selection An...mentioning

confidence: 99%

A Procedure Model for the Systematic Sensor Selection and Integration into Technical Systems

2022

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New sensor solutions are under development in the context of digitalization in order to integrate sensory functions into systems. When integrating sensors, the three domains of mechanical, electrical and information engineering must be considered. This results in complex development processes that require suitable procedure models. However, specific procedure models for sensor selection and integration are missing. This contribution proposes a procedure model for sensor selection and integration on the basis of the Munich Procedure Model (MPM) and gives an outlook on open research questions.

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Sensorauswahl für Bestandsanlagen

Cited by 8 publications

References 5 publications

Utilizing a graph data structure to model physical effects and dependencies between different physical variables for the systematic identification of sensory effects in design elements

Utilizing a graph data structure to model physical effects and dependencies between different physical variables for the systematic identification of sensory effects in design elements

A Review on Sensor‐Integrating Machine Elements

A Procedure Model for the Systematic Sensor Selection and Integration into Technical Systems

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