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- 저자 : Xingwang Jiang (검색결과 2 건)
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Xingwang Jiang,Yupeng Hao,Huiyu Wang,Jieyun Tu,Guangyong Liu 한국고분자학회 2022 Macromolecular Research Vol.30 No.4
Three different rubbers, hydrogenated nitrile butadiene rubber (HNBR), ethylene vinyl acetate copolymer (EVM) and ethylene propylene diene rubber (EPDM), and two blends, HNBR/EVM and HNBR/EPDM were compounded both with and without fillers, and were vulcanized with the same peroxide curing system. Peppas- Sahlin model was used to explain the diffusion mechanism of solvents in rubber vulcanizates. Transport parameters including diffusion coefficient, sorption coefficient and permeation coefficient were calculated and correlated with Flory-Huggins interaction parameters (χ). The Peppas-Sahlin model dealing with diffusion behaviors of solvents shows high degree of fitting for both unfilled and filled rubber-solvent systems. With the addition of carbon black, the diffusion coefficient increases while the sorption and permeation coefficients decrease. The sorption coefficient increases linearly with the permeation coefficient. New Flory-Huggins interaction parameter (χN) calculated by threedimensional solubility parameters shows better predictive power in diffusion behaviors than the traditional one (χT). By mathematical fitting, a linear relationship can be obtained between the maximum swelling ratio and χN, while an exponential relationship is gained for the permeation coefficient. The discovery of this rule connects three-dimensional solubility parameters with the swelling of polymer in solvent, which provides experimental basis for the further study of the medium resistance of polymer.
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Huaiqian Bao,Lijin Song,Zongzhen Zhang,Baokun Han,Jinrui Wang,Junqing Ma,Xingwang Jiang 대한기계학회 2023 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.37 No.9
This study proposes a framework for bearing remaining useful life (RUL) prediction that uses multidomain features and a dual-attention mechanism (DAM). First, sparsity measures are introduced as new feature parameters to comprehensively and accurately extract the degradation features of bearings. Second, a long short-term memory network integrated with DAM is applied for RUL prediction. DAM simultaneously applies the attention mechanism to the time steps and feature dimension to increase the attention to important information and enhance the prediction performance of the network. Third, a pseudo-normalization method is proposed to solve the problem of unknown bearing test data in actual working conditions under the premise of retaining the original data characteristics and RUL prediction accuracy as much as possible. Lastly, the proposed framework is experimentally proven on public datasets and compared with other methods to prove its feasibility and effectiveness.
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