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Peer-reviewedCC BYScientific Reports

Wavelet-based subdomain-enhanced multi-source domain adaptive network for bearing fault diagnosis

  • Lianzhai Duan1
  • Yulyani Arifin1
  • Muhammad Asrol2
  • Widodo Budiharto3
  1. 1Computer Science Department,BINUS Graduate Program - Doctor of Computer Science, Bina Nusantara University
  2. 2Industrial Engineering Department, BINUS Graduate Program - Master of Industrial Engineering, Bina Nusantara University
  3. 3Computer Science Department, School of Computer Science, Bina Nusantara University
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Abstract

Rolling bearing fault diagnosis is crucial for ensuring industrial production safety. However, traditional methods exhibit limited generalization under varying loads, speed fluctuations, and multi-source domain distribution shifts. To address the challenges of source domain conflict, contribution imbalance, and global-local alignment in multi-source domain adaptive fault diagnosis, this paper proposes a wavelet-based subdomain-enhanced multi-source domain adaptive network. The network comprises three core modules: a shared feature extraction module, a private feature enhancement module, and an adaptive feature fusion module. The shared feature extraction module employs multi-scale wavelet convolution to capture multi-scale frequency characteristics, thereby enhancing feature representation capability. The private feature enhancement module integrates residual connections with a hybrid attention mechanism to emphasize critical fault-related information, while a domain discriminator extracts domain-invariant features. The adaptive feature fusion module achieves dynamic weight allocation and collaborative integration of multi-source domain knowledge. Experimental validations based on the bearing datasets from the University of Ottawa in Canada and Huazhong University of Science and Technology (HUST) demonstrate that the proposed method can achieve efficient inter-domain alignment, robust feature extraction, and accurate fault diagnosis. Additionally, it has fewer model parameters and outstanding advantages in training efficiency.

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Cite

BibTeXRIS
BibTeX
@article{duan2026waveletbased,
  title = {{Wavelet-based subdomain-enhanced multi-source domain adaptive network for bearing fault diagnosis}},
  author = {Lianzhai Duan and Yulyani Arifin and Muhammad Asrol and Widodo Budiharto},
  journal = {Scientific Reports},
  year = {2026},
  month = aug,
  doi = {10.1038/s41598-026-67811-4},
  url = {https://doi.org/10.1038/s41598-026-67811-4},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - Wavelet-based subdomain-enhanced multi-source domain adaptive network for bearing fault diagnosis
AU  - Lianzhai Duan
AU  - Yulyani Arifin
AU  - Muhammad Asrol
AU  - Widodo Budiharto
PY  - 2026
DA  - 2026/08/01
JO  - Scientific Reports
DO  - 10.1038/s41598-026-67811-4
UR  - https://doi.org/10.1038/s41598-026-67811-4
AB  - Rolling bearing fault diagnosis is crucial for ensuring industrial production safety. However, traditional methods exhibit limited generalization under varying loads, speed fluctuations, and multi-source domain distribution shifts. To address the challenges of source domain conflict, contribution imbalance, and global-local alignment in multi-source domain adaptive fault diagnosis, this paper proposes a wavelet-based subdomain-enhanced multi-source domain adaptive network. The network comprises three core modules: a shared feature extraction module, a private feature enhancement module, and an adaptive feature fusion module. The shared feature extraction module employs multi-scale wavelet convolution to capture multi-scale frequency characteristics, thereby enhancing feature representation capability. The private feature enhancement module integrates residual connections with a hybrid attention mechanism to emphasize critical fault-related information, while a domain discriminator extracts domain-invariant features. The adaptive feature fusion module achieves dynamic weight allocation and collaborative integration of multi-source domain knowledge. Experimental validations based on the bearing datasets from the University of Ottawa in Canada and Huazhong University of Science and Technology (HUST) demonstrate that the proposed method can achieve efficient inter-domain alignment, robust feature extraction, and accurate fault diagnosis. Additionally, it has fewer model parameters and outstanding advantages in training efficiency.
SN  - 2045-2322
ER  -