Health indication of electric motors using a hybrid modeling approach
- 3 September 2019
- journal article
- research article
- Published by Walter de Gruyter GmbH in TM - Technisches Messen
- Vol. 86 (11), 640-650
- https://doi.org/10.1515/teme-2019-0082
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.Keywords
This publication has 18 references indexed in Scilit:
- Remaining useful life estimation in prognostics using deep convolution neural networksReliability Engineering & System Safety, 2018
- Machinery health prognostics: A systematic review from data acquisition to RUL predictionMechanical Systems and Signal Processing, 2017
- A Review on Fatigue Life Prediction Methods for MetalsAdvances in Materials Science and Engineering, 2016
- A hybrid framework combining data-driven and model-based methods for system remaining useful life predictionApplied Soft Computing, 2016
- On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance PropagationPLOS ONE, 2015
- Extended Kalman Filtering for Remaining-Useful-Life Estimation of BearingsIEEE Transactions on Industrial Electronics, 2014
- Overview of Remaining Useful Life Prediction Techniques in Through-life Engineering ServicesProcedia CIRP, 2014
- Offline and online fault detection and diagnosis of induction motors using a hybrid soft computing modelApplied Soft Computing, 2013
- Ensemble of Data-Driven Prognostic Algorithms With Weight Optimization and K-Fold Cross ValidationPublished by ASME International ,2010
- Adaptive Mixtures of Local ExpertsNeural Computation, 1991