Han Haoran, Wang Huan, Liu Zhiliang, Wang Jiayi
School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China; Glasgow College, University of Electronic Science and Technology of China, Chengdu, 611731, China.
School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China.
ISA Trans. 2022 Mar;122:13-23. doi: 10.1016/j.isatra.2021.04.022. Epub 2021 Apr 25.
Convolutional neural networks (CNNs) have been widely applied to machinery health management in recent years, whereas research on data-driven denoising methods is relatively limited. Therefore, this paper proposes a robust denoising method based on a non-local fully convolutional neural network (NL-FCNN). In this neural network, the Leaky-ReLU activation function is employed to maintain the information contained in the negative value of the signal. The wide kernel principle is also adopted to enlarge the receptive field. Lastly, the non-local means (NLM) is utilized to construct non-local block (NLB), which could efficiently enhance the long-range dependencies learning ability of the network. This block could enormously improve the denoising performance of the network. Moreover, the proposed method exhibits better performance compared with the three conventional denoising methods under multiple noise levels on the Case Western Reserve University (CWRU) motor bearing dataset. Ultimately, we also demonstrate its application to rolling bearing fault diagnosis.
近年来，卷积神经网络（CNN）已广泛应用于机械健康管理，而对数据驱动去噪方法的研究相对有限。因此，本文提出了一种基于非局部全卷积神经网络（NL-FCNN）的鲁棒去噪方法。在该神经网络中，采用Leaky-ReLU激活函数来保留信号负值中包含的信息。还采用宽内核原理来扩大感受野。最后，利用非局部均值（NLM）构建非局部块（NLB），这可以有效地增强网络的远程依赖学习能力。该模块可以极大地提高网络的去噪性能。此外，在凯斯西储大学（CWRU）电机轴承数据集上，所提出的方法在多个噪声水平下与三种传统去噪方法相比表现出更好的性能。最终，我们还展示了其在滚动轴承故障诊断中的应用。
