Zhou Yajing, Long Xinyu, Sun Mingwei, Chen Zengqiang
College of Artificial Intelligence, Nankai University, Tianjin 300350, China.
Math Biosci Eng. 2022 Sep 26;19(12):14086-14101. doi: 10.3934/mbe.2022656.
Rolling bearings are the core components of mechanical and electrical systems. A practical fault diagnosis scheme is the key to ensure operational safety. There are excessive characteristic parameters with remarkable randomness and severe signal coupling in the rolling bearing operation, which makes the fault diagnosis to be challenging. To deal with this problem, the Gramian angular field (GAF) and DenseNet are combined to perform feature extraction and fault diagnosis. The GAF can convert 1-dimensional time series into an image, which can guarantee the completeness of feature information without temporal dependence. The GAF images are then trained by using the DenseNet to generate a data set network. In this process, the transfer learning (TL), which can solve the problem of insufficient samples, is integrated to the DenseNet to enhance its extensibility. The comparative simulations are carried out to illustrate the effectiveness of the proposed method.
滚动轴承是机电系统的核心部件。切实可行的故障诊断方案是确保运行安全的关键。滚动轴承运行过程中存在过多具有显著随机性和严重信号耦合的特征参数，这使得故障诊断具有挑战性。为解决这一问题，将格拉姆角场（GAF）和密集连接卷积网络（DenseNet）相结合进行特征提取和故障诊断。GAF可以将一维时间序列转换为图像，能够保证特征信息的完整性而不受时间依赖性影响。然后使用DenseNet对GAF图像进行训练以生成数据集网络。在此过程中，将能够解决样本不足问题的迁移学习（TL）集成到DenseNet中以增强其扩展性。通过对比仿真来说明所提方法的有效性。
