Simulation-assisted Training of Neural Networks for Condition Monitoring of Electrical Drives: Approach and Proof of Concept
Conference: IKMT 2022 - 13. GMM/ETG-Fachtagung
09/14/2022 - 09/15/2022 at Linz, Österreich
Proceedings: GMM-Fb. 103: IKMT 2022
Pages: 7Language: englishTyp: PDF
Authors:
Marth, Edmund; Zorn, Patrick; Amrhein, Wolfgang (Institute for Electrical Drives and Power Electronics, Johannes Kepler University Linz, Austria)
Schmid, Florian (Institute for Computational Perception, Johannes Kepler University Linz, Austria & LIT AI Lab, Linz Institute of Technology (LIT), Linz, Austria)
Masoudian, Shahed; Koutini, Khaled (LIT AI Lab, Linz Institute of Technology (LIT), Linz, Austria)
Abstract:
One crucial aspect of data based modeling is the availability of a sufficient amount of proper data. In the context of AI systems used for condition monitoring of electrical drives, to predict certain faulty conditions, also the corresponding faulty real world data has to be provided to teach an AI based condition monitoring system. But this is most likely linked to an enormous effort. In this paper an approach is presented, how such a condition monitoring system can be created by mainly using simulation data and mapping the simulation domain to the real world domain using fault-free measurements, which are usually easily accessible. After presenting the concept of simulation assisted training, prediction of a commutation angle error of a block-commutated 280W motor will serve to prove the concept.