In this paper, a data-driven approach is utilized for bearing condition monitoring involving the classification of different operating states by processing the raw vibration data. The vibration responses are analyzed and preprocessed before input to 1D-RCNN (one-dimensional residual convolutional neural network). The comparison results are based on commonly implemented evaluation indices such as precision, recall, F1-score, and ROC plots. Hence, the results revealed the superiority of the proposed methodology and its efficacy in segregating the bearing lifetime data into different operating conditions. Furthermore, t-SNE (t-distributed stochastic neighbor embedding) technique is implemented to represent the precise discriminative learning ability of different layers of the network. The overall classification accuracy values are obtained as 97.2% for 1D-RCNN, 95.31% for 1D-CNN, 86.2%, 86.42%, and 87.4% for SVM, KNN, and DNN, respectively. Hence, the proposed methodology may be effectively implemented for bearing health monitoring utilizing deep learning networks as classifiers.

在本文中，数据驱动的方法用于轴承状态监测，涉及通过处理原始振动数据对不同运行状态进行分类。在输入到 1D-RCNN（一维残差卷积神经网络）之前，对振动响应进行分析和预处理。比较结果基于常用的评估指标，例如精度、召回率、F1 分数和 ROC 图。因此，结果揭示了所提出的方法的优越性及其在将轴承寿命数据分成不同的操作条件方面的有效性。此外，实现了 t-SNE（t-分布式随机邻居嵌入）技术来表示网络不同层的精确判别学习能力。总体分类准确度值为 97。1D-RCNN 为 2%，1D-CNN 为 95.31%，SVM、KNN 和 DNN 分别为 86.2%、86.42% 和 87.4%。因此，所提出的方法可以有效地用于利用深度学习网络作为分类器的轴承健康监测。