Song Jiahao, Nie Xiaobo, Wu Chuang, Zheng Naiwei
College of Mechanical Engineering, Inner Mongolia University of Technology, Hohhot 010051, China.
Sensors (Basel). 2024 Dec 11;24(24):7909. doi: 10.3390/s24247909.
Rolling bearings are critical rotating components in machinery and equipment; they are essential for the normal operation of such systems. Consequently, there is a pressing need for a highly efficient, applicable, and reliable method for bearing fault diagnosis. Currently, one-dimensional data-driven fault diagnosis methods, which rely on one-dimensional data, represent a mainstream approach in this field. However, these methods exhibit weak diagnostic capabilities in noisy environments and when confronted with insufficient sample sizes. In order to solve these limitations, a new fault diagnosis method for rolling bearings is proposed, which combines the ConvNeXt network and improved DenseBlock into a parallel network with a feature fusion function. The network can fully extract the global feature and the detail feature of the signal and integrate them, which shows a good diagnostic ability in the face of a strong noise environment. Additionally, the Dy-ReLU function is introduced into the network, which enhances the generalization ability of the network and improves the convergence speed. Comparative experiments show that this method still has strong fault diagnosis capability under the condition of noise pollution and insufficient training samples.
滚动轴承是机械设备中的关键旋转部件；它们对于此类系统的正常运行至关重要。因此，迫切需要一种高效、适用且可靠的轴承故障诊断方法。目前，依赖一维数据的一维数据驱动故障诊断方法是该领域的主流方法。然而，这些方法在噪声环境中以及面对样本量不足时，诊断能力较弱。为了解决这些局限性，提出了一种新的滚动轴承故障诊断方法，该方法将ConvNeXt网络和改进的DenseBlock组合成一个具有特征融合功能的并行网络。该网络能够充分提取信号的全局特征和细节特征并将它们整合起来，在面对强噪声环境时表现出良好的诊断能力。此外，将Dy-ReLU函数引入网络，增强了网络的泛化能力并提高了收敛速度。对比实验表明，该方法在噪声污染和训练样本不足的情况下仍具有很强的故障诊断能力。
