Výsledky vyhľadávania - "adaptive batch normalization algorithm"

Development of Deep Convolutional Neural Network with Adaptive Batch Normalization Algorithm for Bearing Fault Diagnosis Autor Fu, Chao, Lin, Hsiung-Cheng, Lv, Qing

“…It is crucial to carry out the fault diagnosis of rotating machinery by extracting the features that contain fault information. Many previous works using a…”
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Ball Screw Fault Diagnosis Based on Wavelet Convolution Transfer Learning Autor Xie, Yifan, Liu, Chang, Huang, Liji, Duan, Hongchun

“…The ball screw is the core component of the CNC machine tool feed system, and its health plays an important role in the feed system and even in the entire CNC…”
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Adaptive diagnosis of DC motors using R-WDCNN classifiers based on VMD-SVD Autor Qin, Huabin, Liu, Mingliang, Wang, Jian, Guo, Zijian, Liu, Junbo

“…Traditional fault diagnosis methods of DC (direct current) motors require high expertise and human labor. However, the other disadvantages of these methods are…”
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A New Adaptive Intelligent DC motors Fault Diagnosis System Using R-WDCNN Classifier Autor Xie, Yonghui, Qin, Huabin, Yin, Haihong, Hou, Xujie

“…). Utilizing residual learning techniques, this method adeptly identifies and classifies four distinct types of vibrational signals and is fine-tuned through an adaptive batch normalization algorithm (AdaBN…”
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A Migration Learning Method Based on Adaptive Batch Normalization Improved Rotating Machinery Fault Diagnosis Autor Li, Xueyi, Yu, Tianyu, Li, Daiyou, Wang, Xiangkai, Shi, Cheng, Xie, Zhijie, Kong, Xiangwei

“… To overcome this issue, this paper proposes a bearing fault diagnosis method based on the adaptive batch normalization algorithm, which aims to enhance the generalization ability of the model…”
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Fault Diagnosis of Rotating Machinery Based on One-Dimensional Deep Residual Shrinkage Network with a Wide Convolution Layer Autor Li, Shijie, Jiang, Shouda, Gao, Tianyu, Yang, Jingli, Tang, Qing

“… Moreover, an adaptive batch normalization algorithm (AdaBN) is introduced into the diagnosis model to enhance the adaptability to noise…”
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