School of Mechanical Engineering and Automation, Zhejiang Sci-Tech University, Hangzhou 310018, China.
Sensors (Basel). 2021 Nov 3;21(21):7319. doi: 10.3390/s21217319.
Bearings are the key and important components of rotating machinery. Effective bearing fault diagnosis can ensure operation safety and reduce maintenance costs. This paper aims to develop a novel bearing fault diagnosis method via an improved multi-scale convolutional neural network (IMSCNN). In traditional convolutional neural network (CNN), a fixed convolutional kernel is often employed in the convolutional layer. Thus, informative features can not be fully extracted for fault diagnosis. In the proposed IMSCNN, a 1D dimensional convolutional layer is used to mitigate the effect of noise contained in vibration signals. Then, four dilated convolutional kernels with different dilation rates are integrated to extract multi-scale features through the inception structure. Experimental results from the popular CWRU and PU datasets show the superiority of the proposed method by comparison with other related methods.
轴承是旋转机械的关键和重要部件。有效的轴承故障诊断可以确保操作安全，降低维护成本。本文旨在通过改进的多尺度卷积神经网络（IMSCNN）开发一种新的轴承故障诊断方法。在传统的卷积神经网络（CNN）中，卷积层中通常采用固定的卷积核。因此，对于故障诊断来说，不能充分提取有价值的特征。在提出的 IMSCNN 中，使用一维卷积层来减轻振动信号中包含的噪声的影响。然后，通过 inception 结构集成四个具有不同扩张率的扩张卷积核，以提取多尺度特征。与其他相关方法相比，来自流行的 CWRU 和 PU 数据集的实验结果表明了该方法的优越性。
