Details
| Original language | English |
|---|---|
| Article number | 111142 |
| Journal | Mechanical Systems and Signal Processing |
| Volume | 210 |
| Early online date | 23 Jan 2024 |
| Publication status | Published - 15 Mar 2024 |
Abstract
Rolling bearings are the core components of rotating machinery, and their normal operation is crucial to entire industrial applications. Most existing condition monitoring methods have been devoted to extracting discriminative features from vibration signals that reflect bearing health status. However, the complex working conditions of rolling bearings often make the fault-related information easily buried in noise and other interference. Therefore, it is challenging for existing approaches to extract sufficient critical features in these scenarios. To address this issue, this paper proposes a novel CNN-Transformer network, referred to as Dconformer, capable of extracting both local and global discriminative features from noisy vibration signals. The main contributions of this research include: (1) Developing a novel joint-learning strategy that simultaneously enhances the performance of signal denoising and fault diagnosis, leading to robust and accurate diagnostic results; (2) Constructing a novel CNN-transformer network with a multi-branch cross-cascaded architecture, which inherits the strengths of CNNs and transformers and demonstrates superior anti-interference capability. Extensive experimental results reveal that the proposed Dconformer outperforms five state-of-the-art approaches, particularly in strong noisy scenarios.
Keywords
- Complex working conditions, Dconformer, Fault diagnosis, Noisy scenarios, Rolling bearing, Vibration signal
ASJC Scopus subject areas
- Engineering(all)
- Control and Systems Engineering
- Computer Science(all)
- Signal Processing
- Engineering(all)
- Civil and Structural Engineering
- Engineering(all)
- Aerospace Engineering
- Engineering(all)
- Mechanical Engineering
- Computer Science(all)
- Computer Science Applications
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In: Mechanical Systems and Signal Processing, Vol. 210, 111142, 15.03.2024.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Dconformer
T2 - A denoising convolutional transformer with joint learning strategy for intelligent diagnosis of bearing faults
AU - Li, Sheng
AU - Ji, J. C.
AU - Xu, Yadong
AU - Feng, Ke
AU - Zhang, Ke
AU - Feng, Jingchun
AU - Beer, Michael
AU - Ni, Qing
AU - Wang, Yuling
N1 - Funding Information: This work was partly supported by the Postgraduate Research & Practice Innovation Program of Jiangsu Province (Grant No. KYCX23_0647) and partly by the China Scholarship Council (CSC).
PY - 2024/3/15
Y1 - 2024/3/15
N2 - Rolling bearings are the core components of rotating machinery, and their normal operation is crucial to entire industrial applications. Most existing condition monitoring methods have been devoted to extracting discriminative features from vibration signals that reflect bearing health status. However, the complex working conditions of rolling bearings often make the fault-related information easily buried in noise and other interference. Therefore, it is challenging for existing approaches to extract sufficient critical features in these scenarios. To address this issue, this paper proposes a novel CNN-Transformer network, referred to as Dconformer, capable of extracting both local and global discriminative features from noisy vibration signals. The main contributions of this research include: (1) Developing a novel joint-learning strategy that simultaneously enhances the performance of signal denoising and fault diagnosis, leading to robust and accurate diagnostic results; (2) Constructing a novel CNN-transformer network with a multi-branch cross-cascaded architecture, which inherits the strengths of CNNs and transformers and demonstrates superior anti-interference capability. Extensive experimental results reveal that the proposed Dconformer outperforms five state-of-the-art approaches, particularly in strong noisy scenarios.
AB - Rolling bearings are the core components of rotating machinery, and their normal operation is crucial to entire industrial applications. Most existing condition monitoring methods have been devoted to extracting discriminative features from vibration signals that reflect bearing health status. However, the complex working conditions of rolling bearings often make the fault-related information easily buried in noise and other interference. Therefore, it is challenging for existing approaches to extract sufficient critical features in these scenarios. To address this issue, this paper proposes a novel CNN-Transformer network, referred to as Dconformer, capable of extracting both local and global discriminative features from noisy vibration signals. The main contributions of this research include: (1) Developing a novel joint-learning strategy that simultaneously enhances the performance of signal denoising and fault diagnosis, leading to robust and accurate diagnostic results; (2) Constructing a novel CNN-transformer network with a multi-branch cross-cascaded architecture, which inherits the strengths of CNNs and transformers and demonstrates superior anti-interference capability. Extensive experimental results reveal that the proposed Dconformer outperforms five state-of-the-art approaches, particularly in strong noisy scenarios.
KW - Complex working conditions
KW - Dconformer
KW - Fault diagnosis
KW - Noisy scenarios
KW - Rolling bearing
KW - Vibration signal
UR - http://www.scopus.com/inward/record.url?scp=85183457921&partnerID=8YFLogxK
U2 - 10.1016/j.ymssp.2024.111142
DO - 10.1016/j.ymssp.2024.111142
M3 - Article
AN - SCOPUS:85183457921
VL - 210
JO - Mechanical Systems and Signal Processing
JF - Mechanical Systems and Signal Processing
SN - 0888-3270
M1 - 111142
ER -