A Digital Twin of Life-Cycle Bearing Based on a Defect-Guided Conditional Diffusion Model for Predicting Remaining Life under Limited Degradation Data

dc.contributor.author	Deng, Jiewei
dc.contributor.author	Wen, Guangrui
dc.contributor.author	Lei, Zihao
dc.contributor.author	Xu, Quanning
dc.contributor.author	Su, Yu
dc.contributor.author	Zhang, Zhifen
dc.date.accessioned	2026-08-20T21:28:15Z
dc.date.available	2026-08-20T21:28:15Z
dc.date.copyright	2025/12/31
dc.date.issued	2026
dc.identifier.other	AJRUA6.RUENG-1786.pdf
dc.identifier.uri	http://yetl.yabesh.ir/yetl1/handle/yetl/4314508
dc.description.abstract	AbstractAccurate prediction of the remaining useful life (RUL) for rolling bearings was essential to ensure the operational reliability and safety of complex industrial equipment. However, the lack of high-quality life-cycle data in industry impeded the ...
dc.publisher	American Society of Civil Engineers
dc.title	A Digital Twin of Life-Cycle Bearing Based on a Defect-Guided Conditional Diffusion Model for Predicting Remaining Life under Limited Degradation Data
dc.type	Journal Article
dc.journal.volume	12
dc.journal.issue	1
dc.journal.title	ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
dc.identifier.doi	10.1061/AJRUA6.RUENG-1786
dc.journal.fristpage	04025114-1
dc.journal.lastpage	04025114-16
dc.page	16
dc.tree	ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering:;2026:;Volume ( 012 ):;issue: 001
dc.contenttype	Fulltext