Search Results - "adaptive batch normalization algorithm"

Development of Deep Convolutional Neural Network with Adaptive Batch Normalization Algorithm for Bearing Fault Diagnosis

Authors: Chao Fu Qing Lv Hsiung-Cheng Lin

Source: Shock and Vibration, Vol 2020 (2020)

Subject Terms: 2. Zero hunger, Physics, QC1-999, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

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Access URL: https://doaj.org/article/92b2d4129eda47d6972e3b662dc978f6
https://downloads.hindawi.com/journals/sv/2020/8837958.pdf
http://downloads.hindawi.com/journals/sv/2020/8837958.pdf
https://www.hindawi.com/journals/sv/2020/8837958/

Fault diagnosis method of multi-rotor UAV based on one-dimensional convolutional neural network with adaptive batch normalization algorithm

Authors: Pu Yang Wanting Li Chenwan Wen et al.

Source: Measurement Science and Technology. 35:025102

Subject Terms: 0209 industrial biotechnology, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Fault diagnosis method of multi-rotor UAV based on one-dimensional convolutional neural network with adaptive batch normalization algorithm.

Authors: Yang, Pu Li, Wanting Wen, Chenwan et al.

Source: Measurement Science & Technology; Feb2024, Vol. 35 Issue 2, p1-13, 13p

Subject Terms: CONVOLUTIONAL neural networks, FAULT diagnosis, DRONE aircraft, DIAGNOSIS, ALGORITHMS, FEATURE extraction

Ball Screw Fault Diagnosis Based on Wavelet Convolution Transfer Learning

Authors: Yifan Xie Chang Liu Liji Huang et al.

Source: Sensors, Vol 22, Iss 6270, p 6270 (2022)

Subject Terms: transfer learning, convolutional neural network, adaptive batch normalization algorithm, fault diagnosis, ball screw, Chemical technology, TP1-1185

Relation: https://www.mdpi.com/1424-8220/22/16/6270; https://doaj.org/toc/1424-8220; https://doaj.org/article/de0fffe63ce34c10b9549ead9cbc80b5

Availability: https://doi.org/10.3390/s22166270
https://doaj.org/article/de0fffe63ce34c10b9549ead9cbc80b5

Development of Deep Convolutional Neural Network with Adaptive Batch Normalization Algorithm for Bearing Fault Diagnosis.

Authors: Fu, Chao Lv, Qing Lin, Hsiung-Cheng

Source: Shock & Vibration; 9/18/2020, p1-10, 10p

Subject Terms: CONVOLUTIONAL neural networks, FAULT diagnosis, ALGORITHMS, ARTIFICIAL neural networks, SUPPORT vector machines, HILBERT-Huang transform, FAST Fourier transforms

A Migration Learning Method Based on Adaptive Batch Normalization Improved Rotating Machinery Fault Diagnosis.

Authors: Li, Xueyi Yu, Tianyu Li, Daiyou et al.

Source: Sustainability (2071-1050); May2023, Vol. 15 Issue 10, p8034, 15p

Ball Screw Fault Diagnosis Based on Wavelet Convolution Transfer Learning.

Authors: Xie, Yifan Liu, Chang Huang, Liji et al.

Source: Sensors (14248220); Aug2022, Vol. 22 Issue 16, p6270-6270, 16p

Subject Terms: FAULT diagnosis, NUMERICAL control of machine tools, NUTS, SCREWS, SENSOR placement

Adaptive diagnosis of DC motors using R-WDCNN classifiers based on VMD-SVD.

Authors: Qin, Huabin Liu, Mingliang Wang, Jian et al.

Source: Applied Intelligence; Jul2021, Vol. 51 Issue 7, p4888-4907, 20p

Subject Terms: SINGULAR value decomposition, CONVOLUTIONAL neural networks, DIAGNOSIS, ALGORITHMS, FAULT diagnosis

Fault Diagnosis of Rotating Machinery Based on One-Dimensional Deep Residual Shrinkage Network with a Wide Convolution Layer.

Authors: Yang, Jingli Gao, Tianyu Jiang, Shouda et al.

Source: Shock & Vibration; 12/16/2020, p1-12, 12p

Subject Terms: ROTATING machinery, FAULT diagnosis, MECHANICAL models, DIAGNOSIS, ALGORITHMS

Optimized GRU with Self-Attention for Bearing Fault Diagnosis Using Bayesian Hyperparameter Tuning.

Authors: Liu, Zongchao Teng, Shuai Wang, Shaodi

Source: Algorithms; Sep2025, Vol. 18 Issue 9, p576, 18p

Subject Terms: RECURRENT neural networks, FAULT diagnosis, MATHEMATICAL optimization, DIAGNOSIS, MACHINE learning, VIBRATION measurements

Experimental motor fault detection and identification of a quadrotor UAV using a hybrid deep learning approach.

Authors: Khaneghaei, Mohammad Asadi, Davood Mowla, Md. Najmul et al.

Source: International Journal of Dynamics & Control; Aug2025, Vol. 13 Issue 8, p1-23, 23p

A Migration Learning Method Based on Adaptive Batch Normalization Improved Rotating Machinery Fault Diagnosis

Authors: Xueyi Li Tianyu Yu Daiyou Li et al.

Source: Sustainability, Vol 15, Iss 10, p 8034 (2023)

Subject Terms: fault diagnosis, AdaBN, transfer learning, rotating machinery, Environmental effects of industries and plants, TD194-195, Renewable energy sources, TJ807-830, Environmental sciences, GE1-350

Relation: https://www.mdpi.com/2071-1050/15/10/8034; https://doaj.org/toc/2071-1050; https://doaj.org/article/a741e80953544c3e98b42ca47424181b

Availability: https://doi.org/10.3390/su15108034
https://doaj.org/article/a741e80953544c3e98b42ca47424181b

Adaptability Study of an Unmanned Aerial Vehicle Actuator Fault Detection Model for Different Task Scenarios.

Authors: Wang, Lulu Cheng, Yuehua Jiang, Bin et al.

Source: Drones (2504-446X); May2025, Vol. 9 Issue 5, p360, 22p

Convolutional Neural Network-Based Risk Assessment of Regional Susceptibility to Road Collapse Disasters: A Case Study in Guangxi.

Authors: Li, Cheng Lu, Zhixiang Hu, Yulong et al.

Source: Applied Sciences (2076-3417); Mar2025, Vol. 15 Issue 6, p3108, 22p

Subject Terms: CONVOLUTIONAL neural networks, RECEIVER operating characteristic curves, YIELD curve (Finance), SOCIAL development, RISK assessment

Geographic Terms: GUANGXI Zhuangzu Zizhiqu (China)

Intelligent Diagnosis of Bearing Failures Based on Recurrence Quantification and Energy Difference.

Authors: Wang, Mukai Wang, Tianfeng Lu, Duhui et al.

Source: Applied Sciences (2076-3417); Nov2024, Vol. 14 Issue 21, p9643, 19p

Subject Terms: CONVOLUTIONAL neural networks, FAULT diagnosis, ROLLER bearings, ROTATING machinery, MACHINE learning

A hybrid deep learning model for fault diagnosis of rolling bearings using raw vibration signals

Authors: Jiang, Liang Tang, Jiahui Sun, Ning et al.

Contributors: Jiang, Liang Tang, Jiahui Sun, Ning et al.

Source: Measurement Science and Technology ; volume 35, issue 9, page 096201 ; ISSN 0957-0233 1361-6501

Availability: https://doi.org/10.1088/1361-6501/ad3fd2
https://iopscience.iop.org/article/10.1088/1361-6501/ad3fd2
https://iopscience.iop.org/article/10.1088/1361-6501/ad3fd2/pdf

Bearing Fault Diagnosis Using a Vector-Based Convolutional Fuzzy Neural Network.

Authors: Lin, Cheng-Jian Lin, Chun-Hui Lin, Frank

Source: Applied Sciences (2076-3417); Mar2023, Vol. 13 Issue 5, p3337, 11p

Subject Terms: CONVOLUTIONAL neural networks, FAULT diagnosis, FUZZY neural networks, SPINDLES (Machine tools), FEATURE extraction, MACHINE tools

Health Indicator Construction Method of Bearings Based on Wasserstein Dual-Domain Adversarial Networks Under Normal Data Only.

Authors: Li, Jie Zi, Yanyang Wang, Yu et al.

Source: IEEE Transactions on Industrial Electronics; Oct2022, Vol. 69 Issue 10, p10615-10624, 10p

Subject Terms: GENERATIVE adversarial networks, SYSTEM failures, ROLLER bearings, RELIABILITY in engineering

Adaptive diagnosis of DC motors using R-WDCNN classifiers based on VMD-SVD

Authors: Zijian Guo Huabin Qin Junbo Liu et al.

Source: Applied Intelligence. 51:4888-4907

Subject Terms: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, 0201 civil engineering

Access URL: https://link.springer.com/article/10.1007/s10489-020-02087-3
https://dblp.uni-trier.de/db/journals/apin/apin51.html#QinLWGL21

A novel convolutional neural network with interference suppression for the fault diagnosis of mechanical rotating components.

Authors: Yang, Jingli Yin, Shuangyan Sun, Chao et al.

Source: Neural Computing & Applications; Jul2022, Vol. 34 Issue 13, p10971-10987, 17p

Subject Terms: CONVOLUTIONAL neural networks, FAULT diagnosis, INTERFERENCE suppression, VIBRATION (Mechanics), SIGNAL sampling