1-D Residual Convolutional Neural Network coupled with Data Augmentation and Regularization Techniques for the ICPHM 2023 Data Challenge

Kreuzer M, Kellermann W (2023)

Publication Language: English

Publication Type: Conference contribution, Conference Contribution

Publication year: 2023

Pages Range: 232-238

Conference Proceedings Title: 2023 IEEE International Conference on Prognostics and Health Management (ICPHM)

Event location: Montreal, CA CA

URI: https://arxiv.org/abs/2304.07305

DOI: 10.1109/ICPHM57936.2023.10194183

Abstract

In this article, we present our contribution to the International Conference on Prognostics and Health Management (ICPHM) 2023 Data Challenge on Industrial Systems’ Health Monitoring using Vibration Analysis. For the task of classifying sun gear faults in a gearbox, we propose a residual Convolutive Neural Network (CNN) that operates on raw three-channel time-domain vibration signals. In conjunction with data augmentation and regularization techniques, the proposed model yields very good results in a multi-class classification scenario with real-world data despite its relatively small size, i.e., with less than 30,000 trainable parameters. Even when presented with data obtained from multiple operating conditions, the network is still capable to accurately predict the condition of the gearbox under inspection.

Authors with CRIS profile

Matthias Kreuzer Professur für Signalverarbeitung Walter Kellermann Professur für Signalverarbeitung

How to cite

APA:

Kreuzer, M., & Kellermann, W. (2023). 1-D Residual Convolutional Neural Network coupled with Data Augmentation and Regularization Techniques for the ICPHM 2023 Data Challenge. In 2023 IEEE International Conference on Prognostics and Health Management (ICPHM) (pp. 232-238). Montreal, CA, CA.

MLA:

Kreuzer, Matthias, and Walter Kellermann. "1-D Residual Convolutional Neural Network coupled with Data Augmentation and Regularization Techniques for the ICPHM 2023 Data Challenge." Proceedings of the IEEE Conference on Prognostics and Health Management, Montreal, CA 2023. 232-238.

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