Health indication of electric motors using a hybrid modeling approach

Abstract

Health assessment of electric motors is a research topic of high relevance in the area of structural mechanics. In the early days, the health state of an electric motor was mainly determined by empirical knowledge. But this paradigm is shifting to advanced methods of predicting the health of single components of an electric motor using its physical simulation models from the design phase. However, the process of creating the models to become usable during operation is laborious and in many cases no simulation or even 3D-CAD models from the design phase are available. This article focuses on a combination of a physics-based and data-driven estimation of the motor health, especially for motors where no information from the design phase is available. In particular, the advancements of the development of the hybrid fusion method moSAIc are presented. moSAIc allows to transfer the knowledge inherent in physical degradation models of motors to unknown derivatives. The experiments show that the accuracy and robustness of moSAIc is significantly better compared to results of earlier stages.