Open Set Recognition for Machinery Fault Diagnosis

Mapping Intimacies ◽

10.1109/indin45523.2021.9557572 ◽

2021 ◽

Author(s):

Jiawen Xu ◽

Matthias Kovatsch ◽

Sergio Lucia

Keyword(s):

Fault Diagnosis ◽

Open Set ◽

Open Set Recognition ◽

Machinery Fault Diagnosis

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Cross-domain open set machinery fault diagnosis based on adversarial network with multiple auxiliary classifiers

IEEE Transactions on Industrial Informatics ◽

10.1109/tii.2021.3138558 ◽

2021 ◽

pp. 1-1

Author(s):

Jun Zhu ◽

Chenggeng Huang ◽

Changqing Shen ◽

Yongjun Shen

Keyword(s):

Fault Diagnosis ◽

Adversarial Network ◽

Cross Domain ◽

Open Set ◽

Machinery Fault Diagnosis

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Deep transfer learning with limited data for machinery fault diagnosis

Applied Soft Computing ◽

10.1016/j.asoc.2021.107150 ◽

2021 ◽

Vol 103 ◽

pp. 107150

Author(s):

Te Han ◽

Chao Liu ◽

Rui Wu ◽

Dongxiang Jiang

Keyword(s):

Fault Diagnosis ◽

Transfer Learning ◽

Limited Data ◽

Machinery Fault Diagnosis

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Deep Feature-aligned Convolutional Neural Network for Machinery Fault Diagnosis

2020 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD) ◽

10.1109/icsmd50554.2020.9261675 ◽

2020 ◽

Author(s):

Junbin Chen ◽

Longcan Liu ◽

Ruyi Huang ◽

Weihua Li

Keyword(s):

Neural Network ◽

Fault Diagnosis ◽

Convolutional Neural Network ◽

Deep Feature ◽

Machinery Fault Diagnosis

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Geometric Transformation-Based Network Ensemble for Open-Set Recognition

2021 IEEE International Conference on Multimedia and Expo (ICME) ◽

10.1109/icme51207.2021.9428392 ◽

2021 ◽

Author(s):

Pramuditha Perera ◽

Vishal M. Patel

Keyword(s):

Geometric Transformation ◽

Open Set ◽

Open Set Recognition

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A Robust Deep Learning Network for Low-speed Machinery Fault Diagnosis based on Multi-kernel and RPCA

IEEE/ASME Transactions on Mechatronics ◽

10.1109/tmech.2021.3084956 ◽

2021 ◽

pp. 1-1

Author(s):

Haihong Tang ◽

Zhiqiang Liao ◽

Peng Chen ◽

Dunwen Zuo ◽

Sheng Yi

Keyword(s):

Deep Learning ◽

Fault Diagnosis ◽

Low Speed ◽

Learning Network ◽

Deep Learning Network ◽

Machinery Fault Diagnosis

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THE APPLICATION OF SOME NON-LINEAR METHODS IN ROTATING MACHINERY FAULT DIAGNOSIS

Mechanical Systems and Signal Processing ◽

10.1006/mssp.2000.1316 ◽

2001 ◽

Vol 15 (4) ◽

pp. 697-705 ◽

Author(s):

W.J. WANG ◽

J. CHEN ◽

X.K. WU ◽

Z.T. WU

Keyword(s):

Fault Diagnosis ◽

Rotating Machinery ◽

Machinery Fault Diagnosis

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TQWT-based multi-scale dictionary learning for rotating machinery fault diagnosis

2017 13th IEEE Conference on Automation Science and Engineering (CASE) ◽

10.1109/coase.2017.8256162 ◽

2017 ◽

Author(s):

Zhibin Zhao ◽

Xuefeng Chen ◽

Baoqing Ding ◽

Shuming Wu

Keyword(s):

Fault Diagnosis ◽

Dictionary Learning ◽

Rotating Machinery ◽

Multi Scale ◽

Machinery Fault Diagnosis

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The method of machinery fault diagnosis for circuit breaker based on bispectrum and Hilbert-Huang transform

2012 International Conference on Systems and Informatics (ICSAI2012) ◽

10.1109/icsai.2012.6223064 ◽

2012 ◽

Author(s):

Shutao Zhao ◽

Jianpeng Li ◽

Bin Hu

Keyword(s):

Fault Diagnosis ◽

Circuit Breaker ◽

Hilbert Huang Transform ◽

Machinery Fault Diagnosis

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Digital Twin for rotating machinery fault diagnosis in smart manufacturing

International Journal of Production Research ◽

10.1080/00207543.2018.1552032 ◽

2018 ◽

Vol 57 (12) ◽

pp. 3920-3934 ◽

Author(s):

Jinjiang Wang ◽

Lunkuan Ye ◽

Robert X. Gao ◽

Chen Li ◽

Laibin Zhang

Keyword(s):

Fault Diagnosis ◽

Rotating Machinery ◽

Smart Manufacturing ◽

Digital Twin ◽

Machinery Fault Diagnosis

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Feature models and condition visualization for rotating machinery fault diagnosis

2013 IEEE 20th International Conference on Electronics, Circuits, and Systems (ICECS) ◽

10.1109/icecs.2013.6815405 ◽

2013 ◽

Author(s):

Thomas W. Rauber ◽

Francisco de Assis Boldt ◽

Flavio M. Varejao ◽

Marcos Pellegrini Ribeiro

Keyword(s):

Fault Diagnosis ◽

Rotating Machinery ◽

Feature Models ◽

Machinery Fault Diagnosis

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