Rolling bearing fault diagnosis based on data-level and feature-level information fusion

作     者：Shu Yongdong Ma Tianchi Lin Yonggang 舒永东;马天池;林勇刚

作者机构：State Key Laboratory of Fluid Power&Mechatronic SystemsZhejiang UniversityHangzhou 310027China School of Mechanical EngineeringSoutheast UniversityNanjing 211189China 

基　　金：The National Natural Science Foundation of China(No.U22A20178) National Key Research and Development Program of China(No.2022YFB3404800) Jiangsu Province Science and Technology Achievement Transformation Special Fund Program(No.BA2023019) 

出 版 物：《Journal of Southeast University(English Edition)》 (东南大学学报（英文版）)

年 卷 期：2024年第40卷第4期

页      码：396-402页

摘      要：To address the limitation of single acceleration sensor signals in effectively reflecting the health status of rolling bearings,a rolling bearing fault diagnosis method based on the fusion of data-level and feature-level information was ***,according to the impact characteristics of rolling bearing faults,correlation kurtosis rules were designed to guide the weight distribution of multi-sensor *** rules were then combined with a weighted fusion method to obtain high-quality data-level fusion ***,a feature-fusion convolutional neural network(FFCNN)that merges the one-dimensional(1D)features extracted from the fused signal with the two-dimensional(2D)features extracted from the wavelet time-frequency spectrum was designed to obtain a comprehensive representation of the health status of rolling ***,the fused features were fed into a Softmax classifier to complete the fault *** results show that the proposed method exhibits an average test accuracy of over 99.00%on the two rolling bearing fault datasets,outperforming other comparison ***,the method can be effectively utilized for diagnosing rolling bearing faults.

主 题 词：fault diagnosis information fusion correlation kurtosis feature-fusion convolutional neural network 

学科分类：080202[080202] 08[工学] 0802[工学-机械学] 080201[080201] 

核心收录：

D　O　I：10.3969/j.issn.1003-7985.2024.04.008

馆 藏 号：203156297...