Peter Welzbacher

Disturbance factors from the use context of sensory functions can have a significant impact on the reliability of the measurement data provided. To prevent costly and time-consuming iterations due to insufficient robustness in their development, a methodology for analysis and synthesis of robust sensory functions is presented. The methodology enables the systematic and comprehensive identification of critical disturbance factors and their subsequent consideration by means of suitable measures based on established approaches from Robust Design and Measurement Engineering. In the analysis part of the methodology, disturbance factors are first identified by systematically analyzing the use context of the sensory function with a disturbance factor matrix. The identified disturbance factors are then analyzed and selected in regard of their impact on the sensory function. Therefore, an abstraction on the level of physical effects is carried out. The identified disturbance factors are then evaluated in regard of their severity and significance in a modified FMEA. Based on the evaluation results, a reasoned and objective decision regarding the criticality of disturbance factors and their respective impacts on a sensory function is drawn. In the subsequent synthesis part of the methodology, measures for consideration of critical disturbance factors are first developed systematically and comprehensively. Therefore, a flow chart is used. The developed measures are then evaluated using a utility analysis and a standardized evaluation target system. Based on the evaluations results, a reasoned decision regarding the measures to be implemented is drawn. To facilitate the application of the methodology, an existing effect graph tool is taken up and functionally extended. Finally, the effectiveness of the methodology is experimentally verified using the example of an inventive sensory function for radial offset and rotational frequency measurement of a disk pack coupling. Furthermore, a user study is conducted to assess the methodology’s efficiency and usability.

As digitalization progresses, the development and integration of sensory functions in technical systems become increasingly important. Managing uncertainty, especially in the early phase of this process, is crucial to ensure the reliability of the data provided. Therefore, a methodical framework for the identification, analysis and consideration of uncertainty was presented in prior works. In this contribution, the effectivity of the framework is evaluated by applying it to a sensory function for rotational speed and offset measurement of a disk pack coupling using sensor integrating bolts.

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.

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.

Digital Twins are being used more and more frequently and provide information from the Real Twin for different applications. Measurements on the Real Twin are required to obtain information, which in many cases requires the installation of supplementary sensors. For their conception and design, it is particularly important that the measuring principles are selected purposefully and the appropriate sensors are integrated at the goal-oriented measuring positions without impairing the functions and other properties of the Real Twin by the integration of these sensors. In this article, a “Design for Digital Twin” approach is discussed for the systematic procedure and demonstrated using a multi-staged gearbox as a concrete example. The approach focuses on the mechanical and hardware side of the Real Twin. For the systematic conception and design of the Digital Twin solution, an understanding of the stakeholder demands and the expected use cases is necessary. Based on the stakeholder demands and use cases, the relevant product properties can be determined. Using the relevant properties, an iterative process of conception, design, and analysis takes place. The conception is carried out by means of target-oriented cause–effect analyses, taking into account systemic interrelations of the Real Twin components and systematics for the selection of measurement principles. Systemic considerations, combined with an effect graph, allow for the analysis and evaluation of disturbing factors.

Failures of machine elements can cause critical machine breakdowns, e.g. bearing damage accounts for almost 20% of all machine failures. Hence, monitoring machine elements is of particular importance for condition monitoring of gearboxes. Therefore, new approaches based on electrical impedance analysis are proposed in literature, e.g. for the measurement of rolling bearing load or damage condition with so-called sensory utilizable machine elements (SuME). For this approach, an electrical signal is transmitted through the structure to be observed and the resulting impedance is measured. With this technique it is possible to detect loads and damages not only for bearings, but also for the entire gearbox. Another application of this method can be the transmission of electrical signals through machine elements and the gearbox themselves. However, uncertainty arises with regard to the distinct signal transmission in these processes, mainly due to parasitic capacitances. In this contribution, the disturbing factors on the measured impedance of a gearbox at different working points are investigated. For this purpose, disturbance factors are systematically identified and used as basis for a full factorial experimental design for screening the operational characteristics of a gearbox. The data obtained from the tests is analyzed to identify correlations between the disturbance factors and parasitic capacitances. The experiments show a speed- and torque-dependent behavior of the parasitic capacitances in a gearbox. These results can be used for further research of SuME and structure-integrated energy supply and signal paths as well as the evaluation of related measurement data by reducing the associated uncertainty.

The integration of Sensing Machine Elements (SME) is a promising approach to obtain reliable data about relevant process and state variables of technical systems. However, the quality and reliability of the provided data strongly depends on the corresponding calculation model of the SME and the therein included uncertainty. Consequently, in this contribution, the calculation model of a sensory utilized rolling bearing, as exemplary SME, is systematically analyzed using existing methods and tools to identify uncertainty that critically affects the quality and reliability of the data provided.

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.

Sensing machine elements offer the potential to upgrade conventional machine elements by extending their primary function to be able to measure a quantity of interest at its point of origin in a technical system, the so-called in situ measurement. To ensure the functionality of these next generation machine elements, special attention must be paid to uncertainty in terms of modelling to be able to correctly evaluate the provided signal and obtain reliable information. Consequently, this contribution describes an approach to classify uncertainty in sensing technology, especially in SMEs, based on the amount of available information, which can be used as a point of departure to reduce the impact of occurring uncertainty to improve the robustness of the obtained signal. Starting from the understanding of uncertainty and the corresponding classification scheme as well as its linkage to robust design from the Collaborative Research Centre 805, a quantitative model is presented to determine the impact of uncertainty on a measuring signal. The applicability of the proposed approach is demonstrated using the example of a sensing timing belt by taking into account the uncertainty from the SME itself and also from the surrounding technical system.