|Table of Contents|

[1] Shu Yongdong, Ma Tianchi, Lin Yonggang,. Rolling bearing fault diagnosis based on data-level and feature-level information fusion [J]. Journal of Southeast University (English Edition), 2024, 40 (4): 396-402. [doi:10.3969/j.issn.1003-7985.2024.04.008]
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Rolling bearing fault diagnosis based on data-level and feature-level information fusion()
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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:
40
Issue:
2024 4
Page:
396-402
Research Field:
Mechanical Engineering
Publishing date:
2024-12-03

Info

Title:
Rolling bearing fault diagnosis based on data-level and feature-level information fusion
Author(s):
Shu Yongdong1;  Ma Tianchi2;  Lin Yonggang1
1State Key Laboratory of Fluid Power & Mechatronic Systems, Zhejiang University, Hangzhou 310027, China
2School of Mechanical Engineering, Southeast University, Nanjing 211189, China
Keywords:
fault diagnosis;  information fusion;  correlation kurtosis;  feature-fusion convolutional neural network
PACS:
TH165;TH17
DOI:
10.3969/j.issn.1003-7985.2024.04.008
Abstract:
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 proposed. First, according to the impact characteristics of rolling bearing faults, correlation kurtosis rules were designed to guide the weight distribution of multi-sensor signals. These rules were then combined with a weighted fusion method to obtain high-quality data-level fusion signals. Subsequently, 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 bearings. Finally, the fused features were fed into a Softmax classifier to complete the fault diagnosis. The 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 methods. Thus, the method can be effectively utilized for diagnosing rolling bearing faults.

References:

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Memo

Memo:
Biographies: Shu Yongdong(1976—), male, doctor, senior engineer; Lin Yonggang(corresponding author), male, doctor, professor, Lin_YG_ZJU@163.com.
Foundation items: 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).
Citation: Shu Yongdong, Ma Tianchi, Lin Yonggang. Rolling bearing fault diagnosis based on data-level and feature-level information fusion[J].Journal of Southeast University(English Edition), 2024, 40(4):396-402.DOI:10.3969/j.issn.1003-7985.2024.04.008.
Last Update: 2024-12-20