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.

翻译：本文介绍了我们在ICPHM 2023数据挑战赛中基于振动分析的工业系统健康监测任务的贡献。针对变速箱太阳轮故障分类问题，我们提出了一种对原始三通道时域振动信号进行处理的残差卷积神经网络。结合数据增强与正则化技术，该模型在多类分类场景下（使用真实世界数据）取得了优异的结果，尽管其规模相对较小（即可训练参数少于30,000个）。即使在面对来自多种运行工况的数据时，该网络仍能准确预测被检测变速箱的健康状态。