Ship Rolling Bearing Fault Identification Under Complex Operating Conditions: Multi-Domain Feature Extraction-Based LCM-HO Enhanced LSSVM Approach.
- DOI
- 10.3390/s25175400
- Published
- 2025 Sep 1
- Container
- Sensors (Basel, Switzerland)
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.3390/s25175400,
title = {Ship Rolling Bearing Fault Identification Under Complex Operating Conditions: Multi-Domain Feature Extraction-Based LCM-HO Enhanced LSSVM Approach.},
author = {Yuan Q and Peng J and Wen X and Liu Z and Zhou R and Ye J},
year = {2025},
journal = {Sensors (Basel, Switzerland)},
doi = {10.3390/s25175400},
url = {https://doi.org/10.3390/s25175400}
}RIS
TY - JOUR TI - Ship Rolling Bearing Fault Identification Under Complex Operating Conditions: Multi-Domain Feature Extraction-Based LCM-HO Enhanced LSSVM Approach. AU - Yuan Q AU - Peng J AU - Wen X AU - Liu Z AU - Zhou R AU - Ye J PY - 2025 JO - Sensors (Basel, Switzerland) DO - 10.3390/s25175400 UR - https://doi.org/10.3390/s25175400 ER -
APA
Q, Y., J, P., X, W., Z, L., R, Z., & J, Y. (2025). Ship Rolling Bearing Fault Identification Under Complex Operating Conditions: Multi-Domain Feature Extraction-Based LCM-HO Enhanced LSSVM Approach.. Sensors (Basel, Switzerland). https://doi.org/10.3390/s25175400
Source records
- pubmed · retrieved 2026-09-25T23:17:04.924Z