1-D Residual Convolutional Neural Network coupled with Data Augmentation and Regularization for the ICPHM 2023 Data Challenge
This work addresses health monitoring for industrial systems, but it is incremental as it applies known techniques like data augmentation and regularization to a specific challenge.
The authors tackled the problem of classifying sun gear faults in a gearbox using vibration analysis, achieving very good results in a multi-class classification scenario with real-world data using a small residual CNN with less than 30,000 parameters.
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.