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RISS 인기검색어
- 검색키워드
- 저자 : Changqing Shen (검색결과 4 건)
- 무료
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- 유료
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Changqing Shen,Dong Wang,Yongbin Liu,Peter W. Tse,Fanrang Kong 국제구조공학회 2014 Smart Structures and Systems, An International Jou Vol.13 No.3
The fault diagnosis of rolling element bearings has drawn considerable research attention in recent years because these fundamental elements frequently suffer failures that could result in unexpected machine breakdowns. Artificial intelligence algorithms such as artificial neural networks (ANNs) and support vector machines (SVMs) have been widely investigated to identify various faults. However, as the useful life of a bearing deteriorates, identifying early bearing faults and evaluating their sizes of development are necessary for timely maintenance actions to prevent accidents. This study proposes a new two-layerstructure consisting of support vector regression machines (SVRMs) to recognize bearing fault patterns and track the fault sizes. The statistical parameters used to track the fault evolutions are first extracted to condense original vibration signals into a few compact features. The extracted features are then used to train the proposed two-layer SVRMs structure. Once these parameters of the proposed two-layer SVRMs structure are determined, the features extracted from other vibration signals can be used to predict theunknown bearing health conditions. The effectiveness of the proposed method is validated by experimental datasets collected from a test rig. The results demonstrate that the proposed method is highly accurate in differentiating between fault patterns and determining their fault severities. Further, comparisons are performed to show that the proposed method is better than some existing methods.
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Shen, Changqing,Wang, Dong,Liu, Yongbin,Kong, Fanrang,Tse, Peter W. Techno-Press 2014 Smart Structures and Systems, An International Jou Vol.13 No.3
The fault diagnosis of rolling element bearings has drawn considerable research attention in recent years because these fundamental elements frequently suffer failures that could result in unexpected machine breakdowns. Artificial intelligence algorithms such as artificial neural networks (ANNs) and support vector machines (SVMs) have been widely investigated to identify various faults. However, as the useful life of a bearing deteriorates, identifying early bearing faults and evaluating their sizes of development are necessary for timely maintenance actions to prevent accidents. This study proposes a new two-layer structure consisting of support vector regression machines (SVRMs) to recognize bearing fault patterns and track the fault sizes. The statistical parameters used to track the fault evolutions are first extracted to condense original vibration signals into a few compact features. The extracted features are then used to train the proposed two-layer SVRMs structure. Once these parameters of the proposed two-layer SVRMs structure are determined, the features extracted from other vibration signals can be used to predict the unknown bearing health conditions. The effectiveness of the proposed method is validated by experimental datasets collected from a test rig. The results demonstrate that the proposed method is highly accurate in differentiating between fault patterns and determining their fault severities. Further, comparisons are performed to show that the proposed method is better than some existing methods.
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Changqing Shen,Yuminghao Xiao,Liangshan Xiong 한국정밀공학회 2022 International Journal of Precision Engineering and Vol.9 No.4
In the present study, a novel grinding wheel design method is proposed to design a wheel for grinding different small helical grooves on the existing helical rake faces of the tool. The proposed method can be applied to manufacture tools with uneven helical rake face like zigzag-edge twist drills. In the proposed method, the processing of the non-wedge groove is initially converted into a superposition of processing steps of several wedge grooves. Then, based on the simplified non-thickness grinding wheel parametric design and envelop graphic design, the grinding wheel capable of processing small wedge grooves with various specified shapes can be obtained. Moreover, through establishing quantitative evaluation criteria, the determination of design parameters concerning the non-thickness grinding wheel is transmitted into an optimization problem. Finally, helical grooves with trapezoid truncation and wedge truncations are simulated on a virtual five-axis machine tool to verify the effectiveness of the grinding wheel design method.
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Emulsifier-Free Synthesis of Crosslinkable ABA Triblock Copolymer Nanoparticles via AGET ATRP
Chuanjie Cheng,Liang Shen,Quanlei Fu,Zhongbin Liu,Yongluo Qiao,Changqing Fu 한국고분자학회 2011 Macromolecular Research Vol.19 No.10
A crosslinkable ABA triblock copolymer was synthesized from allylic methacrylate (AMA) monomer using a poly(ethylene oxide)-based macromolecule as both an initiator and emulsifier. The “two-step” controlled/living radical polymerization can be conducted via activator generated by electron transfer (AGET) atom transfer radical polymerization (ATRP). Cheap and commercially available N,N-dimethyldodecylamine (DMDA) was found to be a good ligand for the AGET ATRP reaction. The linear ABA triblock copolymer that contains pendent allylic groups can undergo crosslinking under UV irradiation conditions, which indicates that the environmentally friendly method has potential applications in UV curing coatings and UV curing inks, etc..
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