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The increasing complexity of Industry 4.0 systems brings new challenges regarding predictive maintenance tasks such as fault detection and diagnosis.
“Intelligent predictive decision support system for condition-based maintenance,”
R. Yam et al., · 2001
Earlier work this paper cites.
“A review of process fault detection and diagnosis. part i: Quantitative model-based methods 27(3), 293–311. part ii: Qualitative models and search strategies 27(3), 313–32. part iii: Process history based methods 27(3), 327–346,”
Venkat Venkatasubramanian et al., · 2003
Earlier work this paper cites.
Computational Intelligence in Fault Diagnosis
Vasile Palade et al., · 2006
Earlier work this paper cites.
“Random forests classifier for machine fault diagnosis,”
Bo-Suk Yang et al., · 2008
Earlier work this paper cites.
“Speaker identification on the scotus corpus,”
Jiahong Yuan and Mark Y. Liberman, · 2008
Earlier work this paper cites.
“Bearing fault detection of induction motor using wavelet and neural networks.,”
Pratyay Konar et al., · 2009
Earlier work this paper cites.
“Application to induction motor faults diagnosis of the amplitude recovery method combined with fft,”
Yukun Liu et al., · 2010
Earlier work this paper cites.
“Application of artificial neural networks (ann) to model the failure of urban water mains,”
Raed Jafar et al., · 2010
Earlier work this paper cites.
“Current status of machine prognostics in condition-based maintenance: A review,”
Ying Peng et al., · 2010
Earlier work this paper cites.
“The computer expression recognition toolbox (cert),”
Gwen Littlewort, Jacob Whitehill, Tingfan Wu, Ian Fasel, Mark Frank, Javier Movellan, and Marian Bartlett, · 2011
Earlier work this paper cites.
“Fault diagnosis and prognosis using wavelet packet decomposition, fourier transform and artificial neural network,”
Zhenyou Zhang et al., · 2013
Earlier work this paper cites.
“Representation learning: A review and new perspectives,”
Y. Bengio et al., · 2013
Earlier work this paper cites.
“Fault location and diagnosis in a medium voltage epr power cable,”
Alistair Reid et al., · 2013
Earlier work this paper cites.
“Vibration analysis for bearing fault detection and classification using an intelligent filter,”
Jafar Zarei et al., · 2014
Earlier work this paper cites.
“Log-based predictive maintenance,”
Ruben Sipos et al., · 2014
Earlier work this paper cites.
“Covarep—a collaborative voice analysis repository for speech technologies,”
Gilles Degottex, John Kane, Thomas Drugman, Tuomo Raitio, and Stefan Scherer, · 2014
Earlier work this paper cites.
“GloVe: Global vectors for word representation,”
Jeffrey Pennington, Richard Socher, and Christopher Manning, · 2014
Earlier work this paper cites.
“Deep learning,”
Yann LeCun et al., · 2015
Earlier work this paper cites.
“A robust bearing fault detection and diagnosis technique for brushless dc motors under non-stationary operating conditions,”
Wathiq Abed, · 2015
Earlier work this paper cites.
“Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data,”
Feng Jia et al., · 2015
Earlier work this paper cites.
“Thermal image based fault diagnosis for rotating machinery,”
Olivier Janssens et al., · 2015
Earlier work this paper cites.
“An intelligent approach for cooling radiator fault diagnosis based on infrared thermal image processing technique,”
Amin Taheri-Garavand et al., · 2015
Earlier work this paper cites.
“A comparative evaluation of unsupervised anomaly detection algorithms for multivariate data,”
Markus Goldstein and Seiichi Uchida, · 2016
Earlier work this paper cites.
“Bilevel feature extraction-based text mining for fault diagnosis of railway systems,”
Feng Wang et al., · 2016
Earlier work this paper cites.
“Multimodal machine learning: A survey and taxonomy,”
Tadas Baltrusaitis, Chaitanya Ahuja, and Louis-Philippe Morency, · 2017
Earlier work this paper cites.
“Attention is all you need,”
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, L ukasz Kaiser, and Illia Polosukhin, · 2017
Earlier work this paper cites.
“Industrial process monitoring in the big data/industry 4.0 era: from detection, to diagnosis, to prognosis,”
Marco S. Reis and Geert Gins, · 2017
Earlier work this paper cites.
“Reliability modelling and analysis of a single machine subsystem of a cable plant,”
Gulshan Taneja et al., · 2017
Cited alongside, same era.
“Machine learning-based cps for clustering high throughput machining cycle conditions,”
Javier Diaz Rozo et al., · 2017
Cited alongside, same era.
“Fault diagnosis for rotating machinery using multiple sensors and convolutional neural networks,”
Min Xia et al., · 2017
Cited alongside, same era.
“A new convolutional neural network based data-driven fault diagnosis method,”
Long Wen et al., · 2017
Cited alongside, same era.
“Liftingnet: A novel deep learning network with layerwise feature learning from noisy mechanical data for fault classification,”
Jun Pan et al., · 2017
Cited alongside, same era.
“A recurrent neural network based health indicator for remaining useful life prediction of bearings,”
“A new snapshot ensemble convolutional neural network for fault diagnosis,”
Long Wen et al., · 2019
Later among the works it cites.
“A bearing fault and severity diagnostic technique using adaptive deep belief networks and dempster–shafer theory,”
Kun Yu et al., · 2019
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“Machine vision intelligence for product defect inspection based on deep learning and hough transform,”
Jinjiang Wang et al., · 2019
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“Deep multimodal representation learning: A survey,”
Wenzhong Guo, Jianwen Wang, and Shiping Wang, · 2019
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“Conformer: Convolution-augmented transformer for speech recognition,”
Anmol Gulati, James Qin, Chung-Cheng Chiu, Niki Parmar, Yu Zhang, Jiahui Yu, Wei Han, Shibo Wang, Zhengdong Zhang, Yonghui Wu, and Ruoming Pang, · 2020
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Liang Guo et al., · 2017
Cited alongside, same era.
“Intelligent bearing fault diagnosis method combining compressed data acquisition and deep learning,”
Jiedi Sun et al., · 2017
Cited alongside, same era.
“Speech-transformer: A no-recurrence sequence-to-sequence model for speech recognition,”
Linhao Dong, Shuang Xu, and Bo Xu, · 2018
Cited alongside, same era.
“Deep learning driven approaches for predictive maintenance: A framework of intelligent fault diagnosis and prognosis in the industry 4.0 era,”
Zhe Li, · 2018
Cited alongside, same era.
“Bearing fault diagnosis based on improved ensemble learning and deep belief network,”
Tianchen Liang et al., · 2018
Cited alongside, same era.
“A novel method for intelligent fault diagnosis of rolling bearings using ensemble deep auto-encoders,”
Haidong Shao et al., · 2018
Cited alongside, same era.
“Imbalanced learning for fault diagnosis problem of rotating machinery based on generative adversarial networks,”
Yuan Xie and Tao Zhang, · 2018
Cited alongside, same era.
Yao Hung Hubert Tsai, Shaojie Bai, Paul Pu Liang, J. Zico Kolter, Louis Philippe Morency, and Ruslan Salakhutdinov, · 2020
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“Transformers are rnns: Fast autoregressive transformers with linear attention,”
Angelos Katharopoulos, Apoorv Vyas, Nikolaos Pappas, and François Fleuret, · 2020
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“Big bird: Transformers for longer sequences,”
Manzil Zaheer, Guru Guruganesh, Kumar Avinava Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontañón, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, and Amr Ahmed, · 2020
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“Longformer: The long-document transformer,”
Iz Beltagy, Matthew E. Peters, and Arman Cohan, · 2020
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“Compressive transformers for long-range sequence modelling,”
Jack W. Rae, Anna Potapenko, Siddhant M. Jayakumar, Chloe Hillier, and Timothy P. Lillicrap, · 2020
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“Streaming Transformer-Based Acoustic Models Using Self-Attention with Augmented Memory,”
Chunyang Wu, Yongqiang Wang, Yangyang Shi, Ching-Feng Yeh, and Frank Zhang, · 2020
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“Synchronous transformers for end-to-end speech recognition,”
Zhengkun Tian, Jiangyan Yi, Ye Bai, Jianhua Tao, Shuai Zhang, and Zhengqi Wen, · 2020
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“Deep learning algorithms for bearing fault diagnostics—a comprehensive review,”
Shen Zhang et al., · 2020
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“Interpretable, multidimensional, multimodal anomaly detection with negative sampling for detection of device failure,”
John Sipple, · 2020
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Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer, · 2020
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“An image is worth 16x16 words: Transformers for image recognition at scale,”
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby, · 2021
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“Rethinking attention with performers,”
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“Efficient content-based sparse attention with routing transformers,”
Aurko Roy, Mohammad Saffar, Ashish Vaswani, and David Grangier, · 2021
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“Emformer: Efficient memory transformer based acoustic model for low latency streaming speech recognition,”
Yangyang Shi, Yongqiang Wang, Chunyang Wu, Ching-Feng Yeh, Julian Chan, Frank Zhang, Duc Le, and Mike Seltzer, · 2021
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“Simultaneous-fault diagnosis considering time series with a deep learning transformer architecture for air handling units,”
Bingjie Wu et al., · 2021
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“Streaming simultaneous speech translation with augmented memory transformer,”
Xutai Ma, Yongqiang Wang, Mohammad Javad Dousti, Philipp Koehn, and Juan Pino, · 2021
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“Learning modality-specific representations with self-supervised multi-task learning for multimodal sentiment analysis,”
Wenmeng Yu, Hua Xu, Ziqi Yuan, and Jiele Wu, · 2021
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“Improving multimodal fusion with hierarchical mutual information maximization for multimodal sentiment analysis,”
Wei Han, Hui Chen, and Soujanya Poria, · 2021
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“Multimodal learning with transformers: A survey,” 2022
Peng Xu, Xiatian Zhu, and David A. Clifton, · 2022
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“Efficient transformers: A survey,”
Yi Tay, Mostafa Dehghani, Dara Bahri, and Donald Metzler, · 2022
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“ ∞ \infty -former: Infinite memory transformer,”
Pedro Henrique Martins, Zita Marinho, and Andre Martins, · 2022
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