A survey on Deep Learning based bearing fault diagnosis

Deng, 2014, A tutorial survey of architectures, algorithms, and applications for deep learning, APSIPA Trans. Sig. Inf. Process., 3, e2

Bengio, 2007, Greedy layer-wise training of deep networks, 153

Hinton, 2006, A fast learning algorithm for deep belief nets, Neural Comput., 18, 1527, 10.1162/neco.2006.18.7.1527

Krizhevsky, 2012, ImageNet classification with deep convolutional neural networks, 1097

Szegedy, 2017, Inception-v4, inception-resnet and the impact of residual connections on learning, 4278

Mikolov, 2010, Recurrent neural network based language model, 2, 3

Collobert, 2008, A unified architecture for natural language processing: deep neural networks with multitask learning, 160

Greenspan, 2016, Guest editorial deep learning in medical imaging: overview and future promise of an exciting new technique, IEEE Trans. Med. Imag., 35, 1153, 10.1109/TMI.2016.2553401

Xu, 2014, Deep learning of feature representation with multiple instance learning for medical image analysis, 1626

Chen, 2015, Deepdriving: learning affordance for direct perception in autonomous driving, 2722

Rao, 2018, Deep learning for self-driving cars: chances and challenges, 35

Zhao, 2019, Deep learning and its applications to machine health monitoring, Mech. Syst. Sig. Process., 115, 213, 10.1016/j.ymssp.2018.05.050

Tamilselvan, 2013, Failure diagnosis using deep belief learning based health state classification, Reliab. Eng. Syst. Saf., 115, 124, 10.1016/j.ress.2013.02.022

Lanham, 2002

Nandi, 2005, Condition monitoring and fault diagnosis of electrical motors-a review, IEEE Trans. Energy Convers., 20, 719, 10.1109/TEC.2005.847955

Huang, 1996, 201

Zarei, 2014, Vibration analysis for bearing fault detection and classification using an intelligent filter, Mechatronics, 24, 151, 10.1016/j.mechatronics.2014.01.003

Chacon, 2015, A novel approach for incipient defect detection in rolling bearings using acoustic emission technique, Appl. Acoust., 89, 88, 10.1016/j.apacoust.2014.09.002

Singh, 2014, Motor current signature analysis for bearing fault detection in mechanical systems, Proced. Mater. Sci., 6, 171, 10.1016/j.mspro.2014.07.021

Zhao, 2004, Human face recognition based on multi-features using neural networks committee, Pattern Recognit. Lett., 25, 1351, 10.1016/j.patrec.2004.05.008

Samanta, 2003, Artificial neural network based fault diagnostics of rolling element bearings using time-domain features, Mech. Syst. Sig. Process., 17, 317, 10.1006/mssp.2001.1462

Malhi, 2004, PCA-based feature selection scheme for machine defect classification, IEEE Trans. Instrum. Meas., 53, 1517, 10.1109/TIM.2004.834070

Yen, 2000, Wavelet packet feature extraction for vibration monitoring, IEEE Trans. Ind. Electron., 47, 650, 10.1109/41.847906

Lou, 2004, Bearing fault diagnosis based on wavelet transform and fuzzy inference, Mech. Syst. Sig. Process., 18, 1077, 10.1016/S0888-3270(03)00077-3

Hemmati, 2016, Roller bearing acoustic signature extraction by wavelet packet transform, applications in fault detection and size estimation, Appl. Acoust., 104, 101, 10.1016/j.apacoust.2015.11.003

Van, 2016, Two-stage feature selection for bearing fault diagnosis based on dual-tree complex wavelet transform and empirical mode decomposition, Proc. Inst. Mech. Eng. Part C J. Mech. Eng. Sci., 230, 291, 10.1177/0954406215573976

Li, 2016, Rolling element bearing defect detection using the generalized synchrosqueezing transform guided by time–frequency ridge enhancement, ISA Trans., 60, 274, 10.1016/j.isatra.2015.10.014

Hyvärinen, 2000, Independent component analysis: algorithms and applications, Neural Netw., 13, 411, 10.1016/S0893-6080(00)00026-5

Guyon, 2003, An introduction to variable and feature selection, J. Mach. Learn. Res., 3, 1157

Baraldi, 2016, Hierarchical k-nearest neighbours classification and binary differential evolution for fault diagnostics of automotive bearings operating under variable conditions, Eng. Appl. Artif. Intell., 56, 1, 10.1016/j.engappai.2016.08.011

Dhamande, 2016, Bearing fault diagnosis based on statistical feature extraction in time and frequency domain and neural network, Int. J. Vehicle Struct. Syst., 8, 229

Huang, 2004, A constructive approach for finding arbitrary roots of polynomials by neural networks, IEEE Trans. Neural Netw., 15, 477, 10.1109/TNN.2004.824424

Li, 2016, A new rolling bearing fault diagnosis method based on multiscale permutation entropy and improved support vector machine based binary tree, Measurement, 77, 80, 10.1016/j.measurement.2015.08.034

Huang, 2008, A constructive hybrid structure optimization methodology for radial basis probabilistic neural networks, IEEE Trans. Neural Netw., 19, 2099, 10.1109/TNN.2008.2004370

Li, 2015, Multimodal deep support vector classification with homologous features and its application to gearbox fault diagnosis, Neurocomputing, 168, 119, 10.1016/j.neucom.2015.06.008

Jia, 2016, Deep neural networks: a promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data, Mech. Syst. Signal Process., 72, 303, 10.1016/j.ymssp.2015.10.025

Deng, 2014, Deep learning: methods and applications, Found. Trends Sig. Process., 7, 197, 10.1561/2000000039

LeCun, 2015, Deep learning, Nature, 521, 436, 10.1038/nature14539

Ngiam, 2011, Multimodal deep learning, 689

Rumelhart, 1986, Learning representations by back-propagating errors, Cognit. Model., 323, 533

Vincent, 2008, Extracting and composing robust features with denoising autoencoders, 1096

Smolensky, 1986

Hinton, 2010, A practical guide to training restricted Boltzmann machines, Momentum, 9, 926

Hinton, 2009, Deep belief networks, Scholarpedia, 4, 5947, 10.4249/scholarpedia.5947

Salakhutdinov, 2009, Deep Boltzmann machines, 448

Salakhutdinov, 2010, Efficient learning of deep Boltzmann machines, 693

Amaral, 2013, Using different cost functions to train stacked auto-encoders, 114

Shao, 2017, A novel deep autoencoder feature learning method for rotating machinery fault diagnosis, Mech. Syst. Sig. Process., 95, 187, 10.1016/j.ymssp.2017.03.034

Ma, 2015, Maximum correntropy criterion based sparse adaptive filtering algorithms for robust channel estimation under non-Gaussian environments, J. Frankl. Inst., 352, 2708, 10.1016/j.jfranklin.2015.03.039

Vincent, 2010, Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion, J. Mach. Learn. Res., 11, 3371

Xia, 2017, Intelligent fault diagnosis approach with unsupervised feature learning by stacked denoising autoencoder, IET Sci. Meas. Technol., 11, 687, 10.1049/iet-smt.2016.0423

Lu, 2017, Fault diagnosis of rotary machinery components using a stacked denoising autoencoder-based health state identification, Sig. Process., 130, 377, 10.1016/j.sigpro.2016.07.028

Shao, 2017, An enhancement deep feature fusion method for rotating machinery fault diagnosis, Knowl. Based Syst., 119, 200, 10.1016/j.knosys.2016.12.012

Rifai, 2011, Contractive auto-encoders: explicit invariance during feature extraction, 833

Liu, 2016, Rolling bearing fault diagnosis based on STFT-deep learning and sound signals, Shock Vib., 2016, 1, 10.1155/2016/7974090

Junbo, 2015, Fault diagnosis method study in roller bearing based on wavelet transform and stacked auto-encoder, 4608

Thirukovalluru, 2016, Generating feature sets for fault diagnosis using denoising stacked auto-encoder, 1

Guo, 2016, Multifeatures fusion and nonlinear dimension reduction for intelligent bearing condition monitoring, Shock Vib., 2016, 1

Mao, 2017, Bearing fault diagnosis with auto-encoder extreme learning machine: a comparative study, Proc. Inst. Mech. Eng. Part C J. Mech. Eng. Sci., 231, 1560, 10.1177/0954406216675896

Huang, 2006, Extreme learning machine: theory and applications, Neurocomputing, 70, 489, 10.1016/j.neucom.2005.12.126

Tang, 2016, Extreme learning machine for multilayer perceptron, IEEE Trans. Neural Netw. Learn. Syst., 27, 809, 10.1109/TNNLS.2015.2424995

Kasun, 2013, Representational learning with extreme learning machine for big data, IEEE Intell. Syst., 28, 31

Makhzani, 2015, Winner-take-all autoencoders, 2791

Li, 2018, Bearing fault diagnosis using fully-connected winner-take-all autoencoder, IEEE Access, 6, 6013

Jia, 2018, A neural network constructed by deep learning technique and its application to intelligent fault diagnosis of machines, Neurocomputing, 272, 619, 10.1016/j.neucom.2017.07.032

Nair, 2010, Rectified linear units improve restricted boltzmann machines, 807

Konda, 2014, Zero-bias autoencoders and the benefits of co-adapting features, Stat, 1050

Zhou, 2017, A novel multimode fault classification method based on deep learning, J. Control Sci. Eng., 2017, 1, 10.1155/2017/9517385

Chen, 2017, Deep neural networks-based rolling bearing fault diagnosis, Microelectron. Reliab., 75, 327, 10.1016/j.microrel.2017.03.006

Deng, 2016, Rolling bearing fault diagnosis based on deep boltzmann machines, 1

Li, 2016, Fault diagnosis for rotating machinery using vibration measurement deep statistical feature learning, Sensors, 16, 895, 10.3390/s16060895

Gan, 2016, Construction of hierarchical diagnosis network based on deep learning and its application in the fault pattern recognition of rolling element bearings, Mech. Syst. Signal Process., 72, 92, 10.1016/j.ymssp.2015.11.014

Shao, 2015, Rolling bearing fault diagnosis using an optimization deep belief network, Meas. Sci. Technol., 26, 115002, 10.1088/0957-0233/26/11/115002

Shao, 2017, Rolling bearing fault diagnosis using adaptive deep belief network with dual-tree complex wavelet packet, ISA Trans., 69, 187, 10.1016/j.isatra.2017.03.017

Tao, 2016, Bearing fault diagnosis based on deep belief network and multisensor information fusion, Shock Vib., 2016, 1

Dong, 2007, Hidden semi-markov model-based methodology for multi-sensor equipment health diagnosis and prognosis, Eur. J. Operat. Res., 178, 858, 10.1016/j.ejor.2006.01.041

Janssens, 2016, Convolutional neural network based fault detection for rotating machinery, J. Sound Vib., 377, 331, 10.1016/j.jsv.2016.05.027

Guo, 2016, Hierarchical adaptive deep convolution neural network and its application to bearing fault diagnosis, Measurement, 93, 490, 10.1016/j.measurement.2016.07.054

Ding, 2017, Energy-fluctuated multiscale feature learning with deep convnet for intelligent spindle bearing fault diagnosis, IEEE Trans. Instrum. Meas., 66, 1926, 10.1109/TIM.2017.2674738

Sermanet, 2011, Traffic sign recognition with multi-scale convolutional networks, 2809

Eren, 2017, Bearing fault detection by one-dimensional convolutional neural networks, Math. Probl. Eng., 2017, 1, 10.1155/2017/8617315

Ince, 2016, Real-time motor fault detection by 1-d convolutional neural networks, IEEE Trans. Ind. Electron., 63, 7067, 10.1109/TIE.2016.2582729

Farabet, 2009, Cnp: an fpga-based processor for convolutional networks, 32

Wang, 2017, An adaptive deep convolutional neural network for rolling bearing fault diagnosis, Meas. Sci. Technol., 28, 095005, 10.1088/1361-6501/aa6e22

Zhang, 2017, A new deep learning model for fault diagnosis with good anti-noise and domain adaptation ability on raw vibration signals, Sensors, 17, 425, 10.3390/s17020425

Zhang, 2018, A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment and different working load, Mech. Syst. Signal Process., 100, 439, 10.1016/j.ymssp.2017.06.022

Srivastava, 2014, Dropout: a simple way to prevent neural networks from overfitting., J. Mach. Learn. Res., 15, 1929

Brown, 2011, Ensemble learning, 312

Chen, 2017, Multisensor feature fusion for bearing fault diagnosis using sparse autoencoder and deep belief network, IEEE Trans. Instrum. Meas., 66, 1693, 10.1109/TIM.2017.2669947

Zhang, 2017, Fault diagnosis from raw sensor data using deep neural networks considering temporal coherence, Sensors, 17, 549, 10.3390/s17030549

Lee, 2009, Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations, 609

Norouzi, 2009, Stacks of convolutional restricted Boltzmann machines for shift-invariant feature learning, 2735

Shao, 2018, Rolling bearing fault feature learning using improved convolutional deep belief network with compressed sensing, Mech. Syst. Signal Process., 100, 743, 10.1016/j.ymssp.2017.08.002

Schmidhuber, 2015, Deep learning in neural networks: an overview, Neural Netw., 61, 85, 10.1016/j.neunet.2014.09.003

Loparo, 2013

J. Lee, H. Qiu, G. Yu, J. Lin, et al., Rexnord Technical Services, ‘Bearing Data Set’, IMS, University of Cincinnati, NASA Ames Prognostics Data Repository, 2007.