Abstract:Close to process measuring improves the data quality of a condition monitoring process. A possibility to access such measurements comes with the addition of a sensory function in machine elements. For a systematic development of sensing machine elements, an approach is presented for the identification of possible measurands to determine a variable of interest. Based on a modelling of physical causeeffect- relationships by using an effect matrix and an effect catalogue it allows to consider both direct and indi… Show more
“…The results of this contribution were assessed on the basis of the main objectives described in section Initial validation through an exemplary application of the results independent of the authors in a cooperative project [Harder et al (2021)].…”
Caused by the constantly progressing digitalization in engineering, a significant demand for information about characteristic process and state variables of technical systems arises, e.g. as basis for digital twins or predictive maintenance. However, since it is oftentimes neither obvious what nor how to measure, the integration of measuring functions, in particular in terms of a retrofit, represents a current major challenge in mechanical engineering. In order to overcome this challenge, an approach for the systematic identification of potential measurands is provided in this contribution. For this purpose, the approach of physical effect catalogs is taken up and used for the systematic identification of potential measurands, starting from a physical variable to be determined. Existing catalog systems have two major limitations with respect to the intended identification of cause-effect relationships: on the one hand, existing effect catalogs assume an effect to be realized and not a cause and, on the other hand, a consideration of design parameters of a technical system is not intended. These limitations are overcome by linking the basic ideas of existing catalog systems with the fundamentals of multipole-based modeling. In this way, a multipole-based effect catalog system is developed. The developed catalog system creates the basis to systematically include the changes and transformations of a process or state variable to be determined in a technical system into the identification of potential measurands.
“…The results of this contribution were assessed on the basis of the main objectives described in section Initial validation through an exemplary application of the results independent of the authors in a cooperative project [Harder et al (2021)].…”
Caused by the constantly progressing digitalization in engineering, a significant demand for information about characteristic process and state variables of technical systems arises, e.g. as basis for digital twins or predictive maintenance. However, since it is oftentimes neither obvious what nor how to measure, the integration of measuring functions, in particular in terms of a retrofit, represents a current major challenge in mechanical engineering. In order to overcome this challenge, an approach for the systematic identification of potential measurands is provided in this contribution. For this purpose, the approach of physical effect catalogs is taken up and used for the systematic identification of potential measurands, starting from a physical variable to be determined. Existing catalog systems have two major limitations with respect to the intended identification of cause-effect relationships: on the one hand, existing effect catalogs assume an effect to be realized and not a cause and, on the other hand, a consideration of design parameters of a technical system is not intended. These limitations are overcome by linking the basic ideas of existing catalog systems with the fundamentals of multipole-based modeling. In this way, a multipole-based effect catalog system is developed. The developed catalog system creates the basis to systematically include the changes and transformations of a process or state variable to be determined in a technical system into the identification of potential measurands.
“… Figure 2. Development of a model of evaluation based on a cause–effect relationship [cf. Harder et al 2021; based on Wilson 2005 and Weckenmann, Sommer & Siebert 2006]. …”
Caused by the progressing digitalization in mechanical engineering, a significant demand for information about characteristic process and state variables of technical systems arises. However, since it is oftentimes neither obvious what nor how to measure, the integration of measuring functions, in particular in terms of a retrofit, represents a current challenge in mechanical engineering. In order to overcome this challenge, an approach for the systematic identification of potential measurands is provided in this contribution. For this purpose, the approach of physical effect catalogs is taken up and used for the systematic identification of potential measurands, starting from a physical variable to be determined. Existing catalog systems have two major limitations with respect to the intended identification of cause–effect relationships: They assume an effect to be realized and a consideration of design parameters of a technical system is not intended. These limitations are overcome by linking the fundamental idea of existing catalog systems with the basics of multipole-based modeling. In this way, a multipole-based effect catalog system is developed. It creates the foundation to systematically include the changes and transformations of a process or state variable to be determined into the identification of potential measurands.
“…The latter must necessarily be satisfied in the system and thus form a necessary condition for further consideration of an effect. An example of using the effect catalogue is given by (Harder et al, 2021), but doesn't include a formalised process.…”
Section: Effect Matrix and Catalogue By Vorwerk-handingmentioning
confidence: 99%
“…Both categories share similar benefits regarding the ability to provide in-situ measured data for the user and face similar challenges during their implementation (Kraus et al, 2021). An example of a SuME are sensor utilizable ball or plain bearings, which use the physical properties of a ball bearing and respectively a sliding bearing to measure loads applied on the system directly from within it (Schirra et al, 2021;Harder et al, 2021). A formalised method of enhancing components with an additional sensory function by utilising their physical properties will be shown in chapter 3 and is the main concern of this work.…”
Section: Sensing Machine Elements (Sme) and Their Sub-category Sensor...mentioning
confidence: 99%
“…While there are plentiful ideas for those measurement systems, for example presented by Petko and Uhl (2004), Groche and Brenneis (2014), Jung and DeSmidt (2018), Großkurth and Martin (2019), Schirra et al (2021) etc., a comprehensive methodology to implement sensor functions into a system is still missing, especially concerning SuCs. To present such a method for developing central SuCs, based on the short rough outline of the development process given by (Kraus et al, 2021), and the not formalised process shown by (Harder et al, 2021) for SMEs is the aim of this paper.…”
In the context of condition monitoring and predictive maintenance, collecting accurate data from technical systems is an important corner stone of the advancing digitalization. For gathering precise data of the current state of a system, measurements from within the process can be utilised. To measure in process without disrupting the system is a challenge that can be tackled by using the physical properties of the components of the system. In this paper a method to systematically find such possible sensory utilizable components (SuC), based on their inherent physical effects is presented.
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