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.

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.

The digitalization of industrial processes with e.g. the goal to increase the availability of production processes in general and to support the individualization of many products in particular leads to an increasing demand in sensor data. The state of the art for condition monitoring of involute gear trains is the measurement of structure-borne vibration with acceleration sensors. New approaches, such as measuring the instantaneous angular speed (IAS), are gaining in popularity. Machine manufacturers usually wish to use existing sensors or measurement points, where little or no effort is needed to implement the sensor concept. Magnetoresistive (MR) sensors fulfil this complex set of requirements to a high degree: They are comparatively easy to integrate, can be added as an optional component and provide sufficient accuracy.The authors have developed different sensor concepts using MR sensors for measuring IAS in a 1-stage helical gear box. Multiple tests with artificial tooth flank damages have been carried out to evaluate the damage detection potential of the sensor concepts. Finally, a spectrum analysis of the first gear mesh frequency and surrounding sidebands demonstrates the capability for detecting tooth flank damage with different MR sensor concepts.

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.

Gear condition monitoring is predominantly executed through the utilization of acceleration sensors positioned on the housing. However, recent advancements have identified measuring the instantaneous angular speed as a compelling alternative as it shortens the transmission path and therefore provides high-quality rotational angle information that can be used to increase damage prediction accuracy, particularly under transient operating conditions. Additionally, there are a variety of methodologies for integrating sensors into gears, which underscores the necessity for high-quality condition data. However, it should be noted that a significant amount of effort is required to successfully integrate these sensors into the rotating system. This publication uses a gear wheel sensor that employs the gear itself as a material measure to acquire rotational angle data and to deduce the damage condition. A magnetoresistive sensor is integrated into the gearbox housing radially facing a ferromagnetic gear and measures the rotational angle by the gear teeth. Various artificial tooth flank damages are applied to the pinion. The rotational angle is measured with the gear sensor, and the damage state is classified with a random forest classifier using established evaluations in the time and frequency domains. The tests are conducted under stationary operating conditions at an array of speed and torque levels. Additionally, they are performed under transient operating conditions, employing speed ramps at constant torque. The results of the classification are evaluated by means of classification accuracy and confusion matrices and compared with those obtained via a classic encoder at the pinion shaft and an acceleration sensor at the gearbox housing.