1-D Residual Convolutional Neural Network coupled with Data Augmentation
and Regularization for the ICPHM 2023 Data Challenge
- URL: http://arxiv.org/abs/2304.07305v2
- Date: Wed, 24 May 2023 10:11:20 GMT
- Title: 1-D Residual Convolutional Neural Network coupled with Data Augmentation
and Regularization for the ICPHM 2023 Data Challenge
- Authors: Matthias Kreuzer, Walter Kellermann
- Abstract summary: We propose a residual Convolutional Neural Network that operates on raw three-channel time-domain vibration signals.
The network is still capable to accurately predict the condition of the gearbox under inspection.
- Score: 24.37696434579732
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In this article, we present our contribution to the 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
Convolutional Neural Network 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.
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