Remaining Useful Life Estimation for Unknown Motors Using a Hybrid Modeling Approach 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 3DCAD models. However, this process is laborious and in many cases no 3DCAD 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 stateoftheart methods by a large margin in terms of both accuracy and robustness. Furthermore, compared to purely datadriven methods such as neural networks, moSAIc is explainable allowing domain experts to understand the reason for the predictions.

Analysis of the applicability of fault detection and failure prediction based on unsupervised learning and Monte Carlo simulations for real devices in the industrial automobile production Machine learning and statisticalbased methods have been widely used to estimate the Remaining Useful Life of devices. However, in most of the cases, the transferability of these methods to production environments is complicated. The majority of these algorithms have been tested by using simulated data, or by the construction of testbeds. The complication is generated by some real conditions in such environments which are not considered during the algorithm development. This research addresses those drawbacks by considering real data delivered by a production device in order to construct the right condition monitoring parameter. This parameter will be obtained by analyzing the correlation among available data, and failure on the critical element on the machine. Moreover, the benefits and drawbacks of several datadriven approaches are analyzed, and a method is proposed based on the combination of the best properties of each one.