标题/作者	年份
Deep learning and its applications to machine health monitoring R Zhao, R Yan, Z Chen, K Mao, P Wang, RX Gao Mechanical Systems and Signal Processing 115, 213-237	2019
Applications of machine learning to machine fault diagnosis: A review and roadmap Y Lei, B Yang, X Jiang, F Jia, N Li, AK Nandi Mechanical Systems and Signal Processing 138, 106587	2020
Deep residual shrinkage networks for fault diagnosis M Zhao, S Zhong, X Fu, B Tang, M Pecht IEEE Transactions on Industrial Informatics 16 (7), 4681-4690	2019
Deep learning algorithms for bearing fault diagnostics—A comprehensive review S Zhang, S Zhang, B Wang, TG Habetler IEEE Access 8, 29857-29881	2020
Deep transfer network with joint distribution adaptation: A new intelligent fault diagnosis framework for industry application T Han, C Liu, W Yang, D Jiang ISA transactions 97, 269-281	2020
Vibration based condition monitoring and fault diagnosis of wind turbine planetary gearbox: A review T Wang, Q Han, F Chu, Z Feng Mechanical Systems and Signal Processing 126, 662-685	2019
Deep transfer learning based on sparse autoencoder for remaining useful life prediction of tool in manufacturing C Sun, M Ma, Z Zhao, S Tian, R Yan, X Chen IEEE transactions on industrial informatics 15 (4), 2416-2425	2018
A deep learning method for bearing fault diagnosis based on cyclic spectral coherence and convolutional neural networks Z Chen, A Mauricio, W Li, K Gryllias Mechanical Systems and Signal Processing 140, 106683	2020
A comprehensive review on convolutional neural network in machine fault diagnosis J Jiao, M Zhao, J Lin, K Liang Neurocomputing 417, 36-63	2020
Mechanical fault diagnosis using convolutional neural networks and extreme learning machine Z Chen, K Gryllias, W Li Mechanical systems and signal processing 133, 106272	2019
Bearing fault detection and diagnosis using case western reserve university dataset with deep learning approaches: A review D Neupane, J Seok IEEE Access 8, 93155-93178	2020
Intelligent fault diagnosis for rotary machinery using transferable convolutional neural network Z Chen, K Gryllias, W Li IEEE Transactions on Industrial Informatics 16 (1), 339-349	2019
Deep residual network based fault detection and diagnosis of photovoltaic arrays using current-voltage curves and ambient conditions Z Chen, Y Chen, L Wu, S Cheng, P Lin Energy Conversion and Management 198, 111793	2019
Multiscale kernel based residual convolutional neural network for motor fault diagnosis under nonstationary conditions R Liu, F Wang, B Yang, SJ Qin IEEE Transactions on Industrial Informatics 16 (6), 3797-3806	2019
Intelligent fault diagnosis method of planetary gearboxes based on convolution neural network and discrete wavelet transform R Chen, X Huang, L Yang, X Xu, X Zhang, Y Zhang Computers in industry 106, 48-59	2019
Challenges and opportunities of deep learning models for machinery fault detection and diagnosis: A review SR Saufi, ZAB Ahmad, MS Leong, MH Lim Ieee Access 7, 122644-122662	2019
Big data analytics for intelligent manufacturing systems: A review J Wang, C Xu, J Zhang, R Zhong Journal of Manufacturing Systems 62, 738-752	2022
A deep learning method for bearing fault diagnosis based on time-frequency image J Wang, Z Mo, H Zhang, Q Miao IEEE Access 7, 42373-42383	2019
Multiple wavelet coefficients fusion in deep residual networks for fault diagnosis M Zhao, M Kang, B Tang, M Pecht IEEE Transactions on Industrial Electronics 66 (6), 4696-4706	2018
Information fusion and semi-supervised deep learning scheme for diagnosing gear faults in induction machine systems R Razavi-Far, E Hallaji, M Farajzadeh-Zanjani, M Saif, SH Kia, H Henao, ... IEEE Transactions on Industrial Electronics 66 (8), 6331-6342	2018

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