Classification of Rotor Imbalance in Trains Using Airborne Sound With Real-World Data

Kreuzer M, Schmidt D, Wokusch S, Kellermann W (2024)

Publication Language: English

Publication Type: Conference contribution

Publication year: 2024

Publisher: Institute of Electrical and Electronics Engineers Inc.

Pages Range: 138-145

Conference Proceedings Title: 2024 IEEE International Conference on Prognostics and Health Management (ICPHM)

Event location: Spokane, WA US

ISBN: 9798350374476

DOI: 10.1109/ICPHM61352.2024.10626489

Abstract

In this paper, we address the task of classifying rotor imbalances in induction motors of trains by analysing airborne sound data. We propose the use of a residual-based Convolutional Neural Network (CNN) architecture that stands out for its small number of trainable parameters. The proposed model is evaluated with real-world data that was gathered from state-of-the-art metro trains during regular operation. The experiments show that with the proposed approach rotor imbalances can be reliably classified even when the model is confronted with new damages that were not included in training.

Authors with CRIS profile

Matthias Kreuzer Professur für Signalverarbeitung Walter Kellermann Professur für Signalverarbeitung

How to cite

APA:

Kreuzer, M., Schmidt, D., Wokusch, S., & Kellermann, W. (2024). Classification of Rotor Imbalance in Trains Using Airborne Sound With Real-World Data. In 2024 IEEE International Conference on Prognostics and Health Management (ICPHM) (pp. 138-145). Spokane, WA, US: Institute of Electrical and Electronics Engineers Inc..

MLA:

Kreuzer, Matthias, et al. "Classification of Rotor Imbalance in Trains Using Airborne Sound With Real-World Data." Proceedings of the 2024 IEEE International Conference on Prognostics and Health Management, ICPHM 2024, Spokane, WA Institute of Electrical and Electronics Engineers Inc., 2024. 138-145.

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