Gao Shuzhi, Li Tianchi, Zhang Yimin, Pei Zhiming
Equipment Reliability Institute, Shenyang University of Chemical Technology, Shenyang 110142, China.
Equipment Reliability Institute, Shenyang University of Chemical Technology, Shenyang 110142, China; School of Mechanical Engineering, Shenyang University of Technology, Shenyang 110027, China.
ISA Trans. 2023 Sep;140:309-330. doi: 10.1016/j.isatra.2023.05.014. Epub 2023 Jun 6.
The working environment of rolling bearings is highly complex and often the vibration signal of the bearing is mixed with noise, which makes fault diagnosis challenging. As such, it is imperative to denoise the vibration signal of rolling bearings, extract effective vibration features, and improve classification accuracy. In this research, we propose a rolling bearing fault diagnosis model based on adaptive modified complementary ensemble empirical mode decomposition (AMCEEMD) and a one-dimensional convolutional neural network (1DCNN). Firstly, the AMCEEMD method is proposed. This algorithm is an improved signal processing technique based on CEEMD, which introduces fuzzy entropy and kurtosis values to remove noise and identify impulse signals. The purpose of AMCEEMD is to obtain standard Intrinsic Mode Functions (IMFs) while removing noise. Secondly, we introduce the energy ratio, fuzzy entropy, and kurtosis as selection indices for IMFs. The selection of IMFs is adapted, and the selected IMF features are inputted into 1DCNN for fault classification. Finally, it was validated by two bearing experiments and compared with other classification methods. The classification accuracy of AMCEEMD-1DCNN method in this study is higher than other methods. The effectiveness of the AMCEEMD-1DCNN fault diagnosis model was verified.
滚动轴承的工作环境极为复杂，轴承的振动信号常常与噪声混合在一起，这使得故障诊断颇具挑战性。因此，对滚动轴承的振动信号进行去噪、提取有效的振动特征并提高分类准确率势在必行。在本研究中，我们提出了一种基于自适应改进互补总体经验模态分解（AMCEEMD）和一维卷积神经网络（1DCNN）的滚动轴承故障诊断模型。首先，提出了AMCEEMD方法。该算法是一种基于CEEMD的改进信号处理技术，它引入模糊熵和峭度值来去除噪声并识别脉冲信号。AMCEEMD的目的是在去除噪声的同时获得标准的本征模态函数（IMF）。其次，我们引入能量比、模糊熵和峭度作为IMF的选择指标。对IMF的选择进行了自适应调整，并将所选的IMF特征输入到1DCNN中进行故障分类。最后，通过两个轴承实验进行了验证，并与其他分类方法进行了比较。本研究中AMCEEMD - 1DCNN方法的分类准确率高于其他方法。验证了AMCEEMD - 1DCNN故障诊断模型的有效性。
