Hier finden Sie wissenschaftliche Publikationen aus den Fraunhofer-Instituten.

A hybrid framework for bearing fault diagnosis using physics-guided neural networks

: Krupp, Lukas; Hennig, Andreas; Wiede, Christian; Grabmaier, Anton


Institute of Electrical and Electronics Engineers -IEEE-:
27th IEEE International Conference on Electronics Circuits and Systems, ICECS 2020. Conference Proceedings : Virtual conference, November 23-25, 2020
Piscataway, NJ: IEEE, 2020
ISBN: 978-1-7281-6045-0
ISBN: 978-1-7281-6044-3
2 S.
International Conference on Electronics, Circuits and Systems (ICECS) <27, 2020, Online>
Fraunhofer IMS ()
deep learning; fault diagnosis; hybrid models; machine learning; physics-guided neural networks (PGNN); rolling-element bearings

Emerging smart sensor systems are the main driver of innovation in many fields of application. A prominent example is condition-based monitoring and especially its subdomain fault diagnosis. The integration of advanced machine and deep learning-based signal processing into sensor systems enables new intelligent condition monitoring solutions. However, the data-based nature of machine and deep learning methods still impedes their applicability in many cases, due to a severe lack of data. In this paper, we introduce a new hybrid physics- and data-based framework aiming to solve the issue of small datasets for vibration-based fault diagnosis applied to rolling-element bearings. The framework combines a vibration simulation model and a neural network with embedded physics-based knowledge into a physics-guided neural network. Our approach aims to generate physically consistent data for the training of fault classifiers without extensive data acquisition.