Remaining Useful Life Estimation for Unknown Motors Using a Hybrid Modeling Approach

• Published in: IEEE International Conference on Industrial Informatics (INDIN) Vol. 1; pp. 1327 - 1332
• Main Authors: Hildebrandt, Marcel, Khalil, Mohamed, Bergs, Christoph, Tresp, Volker, Wuchner, Roland, Bletzinger, Kai-Uwe, Heizmann, Michael
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.07.2019
• Subjects: Accuracy; Degradation; Ensemble Methods; Hybrid Modeling; Life estimation; Machine Learning for Fleet; Monitoring; Motors; Neural networks; Particle measurements; Predictive models; Robustness; RUL Estimation; Structural Health Monitoring; Structural Mechanic Simulation; Training
• ISSN: 2378-363X
• DOI: 10.1109/INDIN41052.2019.8972200

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.