Increasing the robustness of fault detection for induction motors based on neural networks and the winding function method
Publication date
2024-01-31
Document type
Konferenzbeitrag
Author
Organisational unit
Conference
24th International Conference on the Computation of Electromagnetic Fields (COMPUMAG 2023) ; Kyoto, Japan ; May 22–26, 2023
Publisher
IEEE
Book title
2023 24th International Conference on the Computation of Electromagnetic Fields (COMPUMAG)
Part of the university bibliography
✅
Language
English
Abstract
This paper presents how a feed forward neural network can be trained on simulated data with high robustness for practical fault detection in induction motors. The basic methodology for the fault detection is a combination of model- and machine-learning-based approaches. The applied framework consists of a feed forward neural network and a multiple coupled circuit model based on the winding function method. The dataset of stator currents in healthy and faulty states simulated by the model allows a neural network-based classification of different faults without the need to measure currents under real fault conditions. However, this approach suffers from the difficulty of transferring the extracted fault characteristics from the simulated data to the measured stator currents. Therefore, the effect of ensemble learning on increasing the robustness of fault detection is investigated in detail. In addition, an analysis of the influence of the hyperparameters of the neural network on the transferability of the extracted fault characteristics from the simulated stator currents is carried out. As a result, it is found that both techniques increase the robustness of the methodology for fault detection.
Version
Published version
Access right on openHSU
Metadata only access