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In this article, we present our contribution to the ICPHM 2023 Data Challengeon Industrial Systems' Health Monitoring using Vibration Analysis. For the taskof classifying sun gear faults in a gearbox, we propose a residualConvolutional Neural Network that operates on raw three-channel time-domainvibration signals. In conjunction with data augmentation and regularizationtechniques, the proposed model yields very good results in a multi-classclassification scenario with real-world data despite its relatively small size,i.e., with less than 30,000 trainable parameters. Even when presented with dataobtained from multiple operating conditions, the network is still capable toaccurately predict the condition of the gearbox under inspection.

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