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Akademický článek

A comparative experimental study on damping properties of epoxy nanocomposite beams reinforced with carbon nanotubes and graphene nanoplatelets

Autor: Pan Shengqi, Feng Jigang, Safaei Babak, Qin Zhaoye, Chu Fulei, Hui David

Publikováno v: Nanotechnology Reviews, Vol 11, Iss 1, Pp 1658-1669 (2022)

Carbon nanotubes (CNTs) and graphene nanoplatelets (GNPs) are extremely ideal nanofillers for applications in damping polymer. This work explores the damping behavior of polymer nanocomposite beams made of epoxy resin reinforced with CNTs and GNPs ex

Externí odkaz: https://doaj.org/article/0af1e643ba0d4de285b1fdd2657709f5

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VSLLaVA: a pipeline of large multimodal foundation model for industrial vibration signal analysis

Autor: Li, Qi, Huang, Jinfeng, He, Hongliang, Zhang, Xinran, Zhang, Feibin, Qin, Zhaoye, Chu, Fulei

Large multimodal foundation models have been extensively utilized for image recognition tasks guided by instructions, yet there remains a scarcity of domain expertise in industrial vibration signal analysis. This paper presents a pipeline named VSLLa

Externí odkaz: http://arxiv.org/abs/2409.07482

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Classifier-Free Diffusion-Based Weakly-Supervised Approach for Health Indicator Derivation in Rotating Machines: Advancing Early Fault Detection and Condition Monitoring

Autor: Hu, Wenyang, Frusque, Gaetan, Wang, Tianyang, Chu, Fulei, Fink, Olga

Deriving health indicators of rotating machines is crucial for their maintenance. However, this process is challenging for the prevalent adopted intelligent methods since they may take the whole data distributions, not only introducing noise interfer

Externí odkaz: http://arxiv.org/abs/2409.01676

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