Work

Rolling bearing performance degradation trend prediction based on RCMFDE and STAGCN

Year: 2025

Type: article

Source: Transactions of the Institute of Measurement and Control

DOI: 10.1177/01423312251369911

Authors: Yaping Wang, Shengbiao Li, Fen Wei, Renquan Dong, Zunshan Xu

Institutions: Harbin University of Science and Technology, Hunan University of Science and Technology

Language: English

Topic: Machine Fault Diagnosis Techniques

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: closed

Funders: National Natural Science Foundation of China, Natural Science Foundation of Hunan Province, Natural Science Foundation of Heilongjiang Province

Awards: G2251945184, G5547296908, G5959541805

OpenAlex ID: W4414514287

FWCI:

Cites: 28

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