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Pattern Strabismus: Where Does the Brain's Role End and the Muscle's Begin?
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102
BEARING FAULT DIAGNOSIS METHOD BASED ON MULTI⁃SCALE AND MULTI⁃PATH ENSEMBLE NETWORK
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
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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105
Immature skulls of the theropod dinosaur Coelophysis bauri from Ghost Ranch, New Mexico
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106
ROLLING BEARING FAULT DIAGNOSIS BASED ON VMD-CWT-CNN
Published 2023-12-01“…Using the method proposed, the average accuracy of multiple experiments on 10 types of bearing fault data from Case Western Reserve University is 99. 86%, which can effectively complete the feature extraction of rolling bearing signal and the accurate diagnosis of damage degree.…”
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Bearing Fault Diagnosis of End-to-End Model Design Based on 1DCNN-GRU Network
Published 2022-01-01“…The performance of the proposed method is validated using the bearing data collected by Case Western Reserve University (CWRU), and the results showed that the proposed model had recognition accuracy higher than 99%.…”
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A BEARING DEEP LEARNING TRANSFER DIAGNOSIS METHOD BASED ON OPTIMIZATION OF SYMMETRIC POLAR COORDINATES
Published 2022-01-01“…The bearing dataset of Case Western Reserve University which includes different rotational speeds and load is used to verify this method and a good recognition effect has been achieved.…”
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110
Bearing fault diagnosis method based on SAVMD and CNN
Published 2024-06-01“…Finally, the proposed method was verified by numerical simulations with the open bearing data of Case Western Reserve University. The accuracy rate is 99.28%. …”
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111
FAULT DIAGNOSIS OF ROLLING BEARINGS BASED ON SDP AND IMPROVED SAM⁃MobileNetv2
Published 2024-08-01“…Traditional fault diagnosis methods for rolling bearings are difficult to accurately and efficiently achieve fault classification.A method of rolling bearing fault classification based on symmetrized dot pattern(SDP)and improved SAM⁃MobileNetv2 was proposed.Firstly,the bearing vibration signal was transformed into two⁃dimensional images with rich characteristic information by SDP algorithm.Secondly,the two⁃dimensional images were fed into the SAM⁃MobileNetv2 network model,which extracted and classified fault feature information.Improved SAM⁃MobileNetv2 networks used the adaptive activation function ACON to replace the ReLU6 activation function in SAM⁃MobileNetv2 to improve model classification performance.Finally,this model was compared with various models.The experimental results show that this model can accurately and efficiently realize the classification of rolling bearing faults,using Case Western Reserve University bearing fault data with an accuracy rate of 99.5%,using the University of Ottawa bearing failure data with an accuracy rate of 97.2%.…”
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The Discovery of Novel Experimental Therapies for Inflammatory Arthritis
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Research on unsupervised domain adaptive bearing fault diagnosis method
Published 2024-06-01“…In order to verify the effectiveness of the proposed method, relevant comprehensive experiments were carried out on the bearing dataset of Case Western Reserve University of American and the bearing dataset of the University of Paderborn in Germany. …”
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115
Study on the Rolling Bearing Fault Diagnosis based on the Hilbert Envelope Spectrum Singular Value and IPSO-SVM
Published 2017-01-01“…And by using the bearing data of Case Western Reserve University,the validity of the method is verified. …”
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116
Rolling-Element Bearing Fault Data Automatic Clustering Based on Wavelet and Deep Neural Network
Published 2018-01-01“…The proposed method is tested with the bearing data provided by the Case Western Reserve University (CWRU) Bearing Data Center. …”
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Esophageal Squamous Cell Carcinoma Presenting with Streptococcus intermedius Cerebral Abscess
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Deep Transfer Learning Method Based on 1D-CNN for Bearing Fault Diagnosis
Published 2021-01-01“…Finally, based on the bearing datasets of Case Western Reserve University and Jiangnan University, seven transfer fault diagnosis comparison experiments are carried out. …”
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FAULT DIAGNOSIS OF ROLLING BEARINGS BASED ON CHANNEL AND SPATIAL RECONSTRUCTION NETWORKS
Published 2024-01-01“…Since the fault vibration data collected in real engineering may be accompanied by noise，traditional diagnostic models are difficult to identify fault categories，to address this problem，a rolling bearing fault diagnosis research method based on channel and spatial reconstruction and progressive convolutional neural networks （CSRP-CNN） was proposed.The model utilizes channel and spatial reconstruction convolution （CSConv） to reduce the redundant information of channels and space in fault features，and reduces the complexity and computation to improve the performance； using convolutional block attention module （CBAM），attention enhancement operation was carried out in the channel and spatial dimensions to make the model pay attention to important fault feature information； and progressive convolutional network structure was used in the shallow layer of the network，which will fuse the previous fault feature information fused with the current input to obtain richer feature information.The performance of CSRP-CNN was evaluated by two different datasets of Case Western Reserve University（CWRU）and machinery fault simulator magnum（MFS-MG）.After the noise and ablation tests，it is verified that CSRP-CNN has strong robustness and the effects of CSConv，CBAM and progressive convolutional neural network（PCNN） on the model noise immunity performance.…”
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