Managing the uncertainty in data-acquisition by in situ measurements: a review and evaluation of sensing machine element-approaches in the context of digital twins

Hausmann, Maximilian; Koch, Yanik; Kirchner, Eckhard

doi:10.1504/ijplm.2021.115700

Cited by 19 publications

(10 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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

Self Cite

View full text Add to dashboard Cite

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.

show abstract

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

Self Cite

View full text Add to dashboard Cite

show abstract

“…distant from it (cf. Hausmann et al, 2021a). One possible methodical tool for this task is the Load Path and Node Model proposed by Vogel (2021).…”

Section: Determination Of Measurement Principlesmentioning

confidence: 99%

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

2022

View full text Add to dashboard Cite

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.

show abstract

“…However, despite the minimization of the transmission path of the quantity to be measured, the transmission path can still be subject to uncertainty, e.g., due to disturbance factors, which jeopardizes the reliability of the information provided by SME (cf. Hausmann et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

“…"in the original location" (cf. Hausmann et al, 2021). The latter results in a reduction of the transmission path of the quantity to be measured and thus in reduced uncertainty due to the minimization of conversions and transformations.…”

Section: Introductionmentioning

confidence: 99%

A Follow-Up on the Methodical Framework for the Identification, Analysis and Consideration of Uncertainty in the Context of the Integration of Sensory Functions by Means of Sensing Machine Elements

et al. 2023

Self Cite

View full text Add to dashboard Cite

When integrating sensing machine elements for in-situ measurements in technical systems, special attention must be paid to uncertainty to ensure the reliability of the provided information. Therefore, a methodical framework for the identification, analysis and consideration of uncertainty was already developed in prior research, which still offers room for improvement regarding the included methods and tools. Therefore, in this contribution, the initially proposed methods and tools are adapted and extended to enhance their efficiency and applicability and to reduce their error proneness in order to increase the acceptance of the framework in practice. First, the identification of uncertainty is improved by means of an extended effect graph for an automated identification of disturbance factor induced data and model uncertainty. Second, the significance of the subsequent evaluation of uncertainty is enhanced by replacing the initially proposed local sensitivity analysis with a global sensitivity analysis. Finally, a flowchart is proposed that supports the identification of applicable and promising strategies for the development of measures to consider critical disturbance factor induced uncertainty.

show abstract

Managing the uncertainty in data-acquisition by in situ measurements: a review and evaluation of sensing machine element-approaches in the context of digital twins

Cited by 19 publications

References 0 publications

A Review on Sensor‐Integrating Machine Elements

A Review on Sensor‐Integrating Machine Elements

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

A Follow-Up on the Methodical Framework for the Identification, Analysis and Consideration of Uncertainty in the Context of the Integration of Sensory Functions by Means of Sensing Machine Elements

Contact Info

Product

Resources

About