The information from Real Twins are increasingly used to construct Digital Twins. Acquisition of information from the Real Twin or in other words performing measurements on the Real Twin may lead to effects in the working of Real Twin. For instance, the introduction of sensors may impair certain functions of a Real Twin. Therefore, it is important to analyse the effect of any change that is performed on the Real Twin for achieving the Digital Twin. In this paper, a method for Digital Twin solution is presented that address these aspects as well as its use is demonstrated by a case example.

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.

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.

Sensor integration as close to the process as possible provides advantages in the quality of the measurement results as well as the possibility to implement completely new sensor principles and to measure novel quantities of interest. Sensor integration at positions close to the process can be made possible, for example, through the development and application of Sensing Machine Elements (SME). In the first part of this contribution, a general sensor system model is proposed. It is based on the concept of measuring chains and allows the uniform description of functions and uncertainties within a conventional sensor or SME application. For this purpose, essential quantities are defined, which are required for a uniform understanding. In the second part, the presented sensor system model is applied to a load measuring strain gauge on a drive shaft. This enables the condition monitoring of the shaft and drive train by measuring the electrical resistance of the strain gauge and thus allowing conclusions about the acting drive torque. The individual functions and uncertainties of the strain gauge integration are presented in the system model. This example shows the applicability of the presented system model for sensors and SME.