| dc.contributor.author | Chen, Yingjie | |
| dc.contributor.author | Xiong, Liuqi | |
| dc.contributor.author | Liu, Xiaofeng | |
| dc.date.accessioned | 2026-08-20T21:13:03Z | |
| dc.date.available | 2026-08-20T21:13:03Z | |
| dc.date.copyright | 2026/01/19 | |
| dc.date.issued | 2026 | |
| dc.identifier.other | JAEEEZ.ASENG-6182.pdf | |
| dc.identifier.uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4314129 | |
| dc.description.abstract | AbstractAccurate fault diagnosis plays a crucial role in implementing condition-based maintenance
for gas turbine engines, enhancing their operational reliability and mitigating associated
costs. Despite notable advancements achieved by deep learning–... | |
| dc.publisher | American Society of Civil Engineers | |
| dc.title | A Dual Transformer–Based Feature Fusion Network for Gas Turbine Engine Fault Diagnosis | |
| dc.type | Journal Article | |
| dc.journal.volume | 39 | |
| dc.journal.issue | 3 | |
| dc.journal.title | Journal of Aerospace Engineering | |
| dc.identifier.doi | 10.1061/JAEEEZ.ASENG-6182 | |
| dc.journal.fristpage | 04026002-1 | |
| dc.journal.lastpage | 04026002-15 | |
| dc.page | 15 | |
| dc.tree | Journal of Aerospace Engineering:;2026:;Volume ( 039 ):;issue: 003 | |
| dc.contenttype | Fulltext | |
