希尔伯特-黄变换
峰度
模式识别(心理学)
人工智能
振动
计算机科学
方位(导航)
人工神经网络
熵(时间箭头)
断层(地质)
工程类
白噪声
数学
声学
统计
物理
地质学
电信
量子力学
地震学
作者
Shuzhi Gao,Tianchi Li,Yimin Zhang,Zhiming Pei
标识
DOI:10.1016/j.isatra.2023.05.014
摘要
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.
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