Fault diagnosis method of rolling bearings based on adaptive modified CEEMD and 1DCNN model

Shuzhi Gao; Tianchi Li; Yimin Zhang; Zhiming Pei

doi:10.1016/j.isatra.2023.05.014

Fault diagnosis method of rolling bearings based on adaptive modified CEEMD and 1DCNN model

ISA Trans. 2023 Sep:140:309-330. doi: 10.1016/j.isatra.2023.05.014. Epub 2023 Jun 6.

Authors

Shuzhi Gao¹, Tianchi Li², Yimin Zhang³, Zhiming Pei¹

Affiliations

¹ Equipment Reliability Institute, Shenyang University of Chemical Technology, Shenyang 110142, China.
² Equipment Reliability Institute, Shenyang University of Chemical Technology, Shenyang 110142, China; School of Mechanical Engineering, Shenyang University of Technology, Shenyang 110027, China.
³ Equipment Reliability Institute, Shenyang University of Chemical Technology, Shenyang 110142, China; School of Mechanical Engineering, Shenyang University of Technology, Shenyang 110027, China. Electronic address: zhangyimin_126163@126.com.

PMID: 37353365
DOI: 10.1016/j.isatra.2023.05.014

Abstract

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.

Keywords: 1DCNN; AMCEEMD; Fault diagnosis; Fuzzy entropy; Kurtosis values; Rolling bearing.