Machine health monitoring has become increasingly important in modern manufacturers because of its ability to reduce downtime of the machine and cut down the production cost. Enormous signals acquired from machinery are capable of reflecting current working conditions by in-depth analysis with various data-driven methods. Hand-crafted feature extraction and representation from the traditional methods are essential but daunting tasks, and these methods may not be suitable for these massive data. Compared with traditional methods, deep learning ones are able to extract the best feature combination during model training without any artificial intervention, which makes it easier, more efficient, and more effective to monitor machine health, but the training cost and training time hamper its application. The short-time Fourier transform is adopted as the data preprocessing method to cut down the training cost and boost the training procedure. Inspired by the great achievements of ResNet, the new optimized model based on ResNet has been proposed with layer-by-layer dimension reduction of the feature maps. The proposed model is also able to avoid information loss in the conventional pooling layer. All the potential candidate model blocks are introduced and compared, and the best one is selected as the final one. Repeated model block layers are adapted for the best feature combinations, followed by a two-layer full connection layer for the final targets. The proposed method is validated by conducting experiments on bearing fault diagnosis and tool wear prediction dataset. The final results show that the proposed model achieves the best accuracy rate in the classification task and the lowest root mean squared error in the prediction task.

由于机器健康状况监控功能可以减少机器的停机时间并降低生产成本，因此它在现代制造商中变得越来越重要。通过使用各种数据驱动方法进行深入分析，从机械获得的巨大信号能够反映当前的工作条件。传统方法的手工特征提取和表示是必不可少的，但任务艰巨，这些方法可能不适用于这些海量数据。与传统方法相比，深度学习方法能够在模型训练过程中提取最佳特征组合，而无需任何人工干预，这使其更容易，更有效，更有效地监控机器健康，但是训练成本和训练时间限制了其应用。 。采用短时傅立叶变换作为数据预处理方法，可以降低训练成本，提高训练过程。在ResNet的巨大成就的启发下，提出了基于ResNet的新的优化模型，该模型具有特征图的逐层降维功能。所提出的模型还能够避免传统池化层中的信息丢失。介绍并比较所有潜在的候选模型块，并选择最佳的一个作为最后一个。重复的模型块层适用于最佳特征组合，然后是最终目标的两层完整连接层。通过对轴承故障诊断和刀具磨损预测数据集进行实验，验证了该方法的有效性。