Remaining useful life estimation is a research topic of high relevance in the area of structural mechanics. To predict the remaining useful lifetime of a motor, domain experts commonly employ physical simulations based on 3D-CAD models. However, this process is laborious and in many cases no 3D-CAD model is available. Also, setting up a simulation might require substantial efforts or might even be infeasible. This article focuses on the machine learning based estimation of the remaining useful life of unknown, derived motor types of an electric motor class based on simulations of known motor types, as well as data sheets and measurements. In particular, we propose the hybrid fusion method moSAIc that allows to transfer the knowledge inherent in physical degradation models of motors to unknown instances. Our experiments show that moSAIc outperforms other state-of-the-art methods by a large margin in terms of both accuracy and robustness. Furthermore, compared to purely data-driven methods such as neural networks, moSAIc is explainable allowing domain experts to understand the reason for the predictions.

Online condition monitoring services and predictive maintenance are becoming more and more a key for system operators to extend the system lifetime and detect faults in early stages. Therefore, system manufactures need to efficiently provide system operators so-called digital twins which can be executed during operation and give the system operator an impression of the health state of the system. Industrial Internet of Things (IIoT) platforms are enablers for such services and provide new possibilities to interact with the system running in the field. Furthermore, the traditional dashboard are becoming obsolete as user interface and are replaced by novel solutions that let the system operator experience the system health state. For example, health estimation and condition monitoring of electric motors is a topic of high interest nowadays. This article addresses an application which acquires machine data, processes it on an IIoT platform to get the system health and visualizes the results online in an augmented reality user interface.

In this paper, an integrated approach is presented to determine the suitable parameters of a magma-filled dyke, which causes observable deformation at the ground surface. By this approach, the finite element method (FEM) and metamodel techniques are combined. FEM is used to establish the numerical model of the dyke and to produce the data required to identify metamodel parameters. Parameter identification problems are also known as parameter estimation or inverse problems. The metamodel technique is employed to make the whole procedure efficient in the identification phase. The identification approach is carried out by a systematic routine based on particle swarm optimization (PSO) algorithm. The approach is tested with synthetic data generated by analytic models. Moreover, it has been also applied to Stromboli Volcano (Italy) as an example, and the ground deformation data is acquired by using interferometry SAR technique. With the approach, the parameters can be successfully estimated with acceptable degree of accuracy. The results also indicate that only one kind of geophysical data are not sufficient for solving such a complex problem.