Showing 81 - 100 results of 200 for search '"Case Western Reserve University"', query time: 0.04s Refine Results
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    The Dead by George Pavlakis

    Published 2025-01-01
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    ROTATING MACHINERY FAULT DIAGNOSIS BASED ON TWO-DIMENSIONAL CONVOLUTION NEURAL NETWORK by ZHANG LiZhi, XU WeiXiao, JING LuYang, TAN JiWen

    Published 2020-01-01
    “…The method is applied to the fault diagnosis of rolling bearing and gearbox,and has achieved good results in the data of the Case Western Reserve University and the PHM2009 dataset.The correct rate is better than the direct comparison of the original signal into CNN,which verifies the superiority of the method.…”
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    RESEARCH ON ROLLING BEARING FAULT DIAGNOSIS BASED ON IMPROVED HHO-LSTM by SHAO LiangShan, ZHU SiJia

    Published 2024-02-01
    “…The rolling bearing experimental data of Case Western Reserve University were used for fault diagnosis experiments. …”
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    BEARING FAULT DIAGNOSIS METHOD BASED ON MULTI⁃SCALE AND MULTI⁃PATH ENSEMBLE NETWORK by QI BoWei, LI YuanYuan, SONG LiYuan

    Published 2024-08-01
    “…To address the problems such as complex convolutional neural network parameters,deep layers and weak generalization performance,a multi⁃scale multi⁃path convolutional neural network(MCS⁃CNN)based bearing fault diagnosis method cas proposed.Firstly,a multi⁃scale convolution block was proposed to reduce the number of network parameters and increase the network width by using depthwise convolution and pointwise convolution,so as to extract multi⁃scale features effectively.Secondly,an ensemble block was proposed to increase the network depth by connecting low⁃level and high⁃level features through multiple paths,thereby improving the diagnostic accuracy of the model.Finally,the effectiveness of the method was verified on the Case Western Reserve University bearing dataset.The results show that the fault diagnosis accuracy of the proposed method can reach 975%and 9825%in high⁃noise and cross⁃load scenarios,and the accuracy in mixed scenarios is improved by more than 15%compared to existing diagnosis methods,which prove the robustness and generalisability of the proposed method.…”
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    ROTATING MACHINERY DEGRADATION STATUS IDENTIFICATION BASED ON BI-OBJECTIVE OPTIMIZATION GENETIC ALGORITHM AND SVM by PEI MoChao, ZHANG JianJun, LI HongRu, YU He

    Published 2021-01-01
    “…The performance of proposed method was verified by the experiment on the data set of hydraulic pump degradation state and the comparison with FRESH<sub>P</sub>CAa, ReliefF and JMIM on the case western reserve university bearing data set.…”
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