Deep learning and its applications to machine health monitoring
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Yin, 2015, Data-based techniques focused on modern industry: an overview, IEEE Trans. Industr. Electron., 62, 657, 10.1109/TIE.2014.2308133
Jeschke, 2017
D. Lund, C. MacGillivray, V. Turner, M. Morales, Worldwide and regional internet of things (iot) 2014–2020 forecast: A virtuous circle of proven value and demand, International Data Corporation (IDC), Tech. Rep.
Li, 2000, Stochastic prognostics for rolling element bearings, Mech. Syst. Signal Process., 14, 747, 10.1006/mssp.2000.1301
Oppenheimer, 2002, Physically based diagnosis and prognosis of cracked rotor shafts, 122
Yu, 2014, Model-based prognosis for hybrid systems with mode-dependent degradation behaviors, IEEE Trans. Industr. Electron., 61, 546, 10.1109/TIE.2013.2244538
Jardine, 2006, A review on machinery diagnostics and prognostics implementing condition-based maintenance, Mech. Syst. Signal Processing, 20, 1483, 10.1016/j.ymssp.2005.09.012
Stojanovic, 2016, Application of cuckoo search algorithm to constrained control problem of a parallel robot platform, Int. J. Adv. Manuf. Technol., 87, 2497, 10.1007/s00170-016-8627-z
Pršić, 2017, A nature inspired optimal control of pneumatic-driven parallel robot platform, Proc. Inst. Mech. Eng., Part C: J. Mech. Eng. Sci., 231, 59, 10.1177/0954406216662367
S. Ren, K. He, R. Girshick, J. Sun, Faster R-CNN: towards real-time object detection with region proposal networks, in: Advances in Neural Information Processing Systems, 91–99, 2015.
R. Collobert, J. Weston, A unified architecture for natural language processing: Deep neural networks with multitask learning, in: Proceedings of the 25th International Conference on Machine learning, ACM, 160–167, 2008.
Hinton, 2012, Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups, IEEE Signal Process. Mag., 29, 82, 10.1109/MSP.2012.2205597
Leung, 2014, Deep learning of the tissue-regulated splicing code, Bioinformatics, 30, i121, 10.1093/bioinformatics/btu277
J. Schmidhuber, Deep Learning in Neural Networks: An Overview, Neural Networks 61 (2015) 85–117, doi: 10.1016/j.neunet.2014.09.003, published online 2014; based on TR arXiv:1404.7828 [cs.NE].
R. Raina, A. Madhavan, A.Y. Ng, Large-scale deep unsupervised learning using graphics processors, in: Proceedings of the 26th Annual International Conference on Machine Learning, ACM, 873–880, 2009.
Hinton, 2007, Learning multiple layers of representation, Trends Cognitive Sci., 11, 428, 10.1016/j.tics.2007.09.004
Widodo, 2007, Support vector machine in machine condition monitoring and fault diagnosis, Mech. Syst. Signal Processing, 21, 2560, 10.1016/j.ymssp.2006.12.007
Yan, 2005, Degradation assessment and fault modes classification using logistic regression, J. Manuf. Sci. Eng., 127, 912, 10.1115/1.1962019
Muralidharan, 2012, A comparative study of Naïve Bayes classifier and Bayes net classifier for fault diagnosis of monoblock centrifugal pump using wavelet analysis, Appl. Soft Comput., 12, 2023, 10.1016/j.asoc.2012.03.021
Bengio, 2013, Representation learning: Aa review and new perspectives, IEEE Trans. Pattern Anal. Mach. Intell., 35, 1798, 10.1109/TPAMI.2013.50
Malhi, 2004, PCA-based feature selection scheme for machine defect classification, IEEE Trans. Instrum. Meas., 53, 1517, 10.1109/TIM.2004.834070
Wang, 2017, Multisensory fusion based virtual tool wear sensing for ubiquitous manufacturing, Robotics Computer-Integrated Manuf., 45, 47, 10.1016/j.rcim.2016.05.010
Wang, 2016, A new probabilistic kernel factor analysis for multisensory data fusion: application to tool condition monitoring, IEEE Trans. Instrum. Meas., 65, 2527, 10.1109/TIM.2016.2584238
Pan, 2010, A survey on transfer learning, IEEE Trans. Knowl. Data Eng., 22, 1345, 10.1109/TKDE.2009.191
P. Vincent, H. Larochelle, Y. Bengio, P.-A. Manzagol, Extracting and composing robust features with denoising autoencoders, in: Proceedings of the 25th International Conference on Machine Learning, ACM, 1096–1103, 2008.
Hinton, 2006, A fast learning algorithm for deep belief nets, Neural Comput., 18, 1527, 10.1162/neco.2006.18.7.1527
R. Salakhutdinov, G.E. Hinton, Deep boltzmann machines., in: AISTATS, vol. 1, 3, 2009.
P. Sermanet, S. Chintala, Y. LeCun, Convolutional neural networks applied to house numbers digit classification, in: Pattern Recognition (ICPR), 2012 21st International Conference on, IEEE, 3288–3291, 2012.
Funahashi, 1993, Approximation of dynamical systems by continuous time recurrent neural networks, Neural Networks, 6, 801, 10.1016/S0893-6080(05)80125-X
Deng, 2014, Deep learning: methods and applications, Found. Trends Signal Process., 7, 197, 10.1561/2000000039
Goodfellow, 2016
Ng, 2011, Sparse autoencoder, CS294A Lecture Notes, 72, 1
Hinton, 2010, A practical guide to training restricted Boltzmann machines, Momentum, 9, 926
Mohamed, 2012, Acoustic modeling using deep belief networks, IEEE Trans. Audio, Speech, Language Processing, 20, 14, 10.1109/TASL.2011.2109382
B.B. Le Cun, J.S. Denker, D. Henderson, R.E. Howard, W. Hubbard, L.D. Jackel, Handwritten digit recognition with a back-propagation network, in: Advances in Neural Information Processing Systems, Citeseer, 1990.
K. Jarrett, K. Kavukcuoglu, Y. Lecun, et al., What is the best multi-stage architecture for object recognition?, in: 2009 IEEE 12th International Conference on Computer Vision, IEEE, 2146–2153, 2009.
A. Krizhevsky, I. Sutskever, G.E. Hinton, Imagenet classification with deep convolutional neural networks, in: Advances in Neural Information Processing Systems, 1097–1105, 2012.
O. Abdel-Hamid, A.-R. Mohamed, H. Jiang, G. Penn, Applying convolutional neural networks concepts to hybrid NN-HMM model for speech recognition, in: 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, 4277–4280, 2012.
Y. Kim, Convolutional neural networks for sentence classification, arXiv preprint arXiv:1408.5882.
H. Jaeger, Tutorial on training recurrent neural networks, covering BPPT, RTRL, EKF and the “echo state network” approach, GMD-Forschungszentrum Informationstechnik, 2002.
Giles, 1992, Learning and extracting finite state automata with second-order recurrent neural networks, Neural Comput., 4, 393, 10.1162/neco.1992.4.3.393
Gers, 2000, Learning to forget: continual prediction with LSTM, Neural Comput., 12, 2451, 10.1162/089976600300015015
Gers, 2002, Learning precise timing with LSTM recurrent networks, J. Mach. Learn. Res., 3, 115
K. Cho, B. Van Merriënboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, Y. Bengio, Learning phrase representations using RNN encoder-decoder for statistical machine translation, arXiv preprint arXiv:1406.1078.
J. Chung, C. Gulcehre, K. Cho, Y. Bengio, Empirical evaluation of gated recurrent neural networks on sequence modeling, arXiv preprint arXiv:1412.3555.
S. Ruder, An overview of gradient descent optimization algorithms, arXiv preprint arXiv:1609.04747.
X. Glorot, Y. Bengio, Understanding the difficulty of training deep feedforward neural networks, in: Proceedings of the thirteenth international conference on artificial intelligence and statistics, 249–256, 2010.
Su, 2007, Induction machine condition monitoring using neural network modeling, IEEE Trans. Ind. Electron., 54, 241, 10.1109/TIE.2006.888786
Li, 2000, Neural-network-based motor rolling bearing fault diagnosis, IEEE Trans. Industr. Electron., 47, 1060, 10.1109/41.873214
Samanta, 2003, Artificial neural network based fault diagnostics of rolling element bearings using time-domain features, Mech. Syst. Signal Processing, 17, 317, 10.1006/mssp.2001.1462
Aminian, 2000, Neural-network based analog-circuit fault diagnosis using wavelet transform as preprocessor, IEEE Trans. Circuits Syst. II: Analog Digital Signal Processing, 47, 151, 10.1109/82.823545
Sun, 2016, A sparse auto-encoder-based deep neural network approach for induction motor faults classification, Measurement, 89, 171, 10.1016/j.measurement.2016.04.007
Lu, 2017, Fault diagnosis of rotary machinery components using a stacked denoising autoencoder-based health state identification, Signal Processing, 130, 377, 10.1016/j.sigpro.2016.07.028
S. Tao, T. Zhang, J. Yang, X. Wang, W. Lu, Bearing fault diagnosis method based on stacked autoencoder and softmax regression, in: Control Conference (CCC), 2015 34th Chinese, IEEE, 6331–6335, 2015.
Jia, 2016, Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data, Mech. Syst. Signal Processing, 72, 303, 10.1016/j.ymssp.2015.10.025
Sun, 2018, Intelligent bearing fault diagnosis method combining compressed data acquisition and deep learning, IEEE Trans. Instrum. Meas., 67, 185, 10.1109/TIM.2017.2759418
Zhou, 2017, A novel multimode fault classification method based on deep learning, J. Control Sci. Eng., 14
T. Junbo, L. Weining, A. Juneng, W. Xueqian, Fault diagnosis method study in roller bearing based on wavelet transform and stacked auto-encoder, in: The 27th Chinese Control and Decision Conference (2015 CCDC), IEEE, 4608–4613, 2015.
Z. Huijie, R. Ting, W. Xinqing, Z. You, F. Husheng, Fault diagnosis of hydraulic pump based on stacked autoencoders, in: 2015 12th IEEE International Conference on Electronic Measurement Instruments (ICEMI), vol. 1, 58–62, 2015.
H. Liu, L. Li, J. Ma, Rolling Bearing Fault Diagnosis Based on STFT-Deep Learning and Sound Signals (2016), Shock and Vibration ArticleID 6127479, 12 pages.
G.S. Galloway, V.M. Catterson, T. Fay, A. Robb, C. Love, Diagnosis of tidal turbine vibration data through deep neural networks, in: Proceedings of the 3rd European Conference of the Prognostic and Health Management Society, PHM Society, 172–180, 2016.
K. Li, Q. Wang, Study on signal recognition and diagnosis for spacecraft based on deep learning method, in: Prognostics and System Health Management Conference (PHM), IEEE, 1–5, 2015.
L. Guo, H. Gao, H. Huang, X. He, S. Li, Multifeatures Fusion and Nonlinear Dimension Reduction for Intelligent Bearing Condition Monitoring (2016), Shock and Vibration ArticleID 4632562, 10pages.
N.K. Verma, V.K. Gupta, M. Sharma, R.K. Sevakula, Intelligent condition based monitoring of rotating machines using sparse auto-encoders, in: 2013 IEEE Conference on Prognostics and Health Management (PHM), 1–7, 2013.
Sohaib, 2017, A hybrid feature model and deep-learning-based bearing fault diagnosis, Sensors, 17, 2876, 10.3390/s17122876
Cheng, 2017, Rotor current-based fault diagnosis for DFIG wind turbine drivetrain gearboxes using frequency analysis and a deep classifier, IEEE Trans. Ind. Appl., 10.1109/TIA.2016.2628362
Chen, 2017, Rolling bearing fault severity identification using deep sparse auto-encoder network with noise added sample expansion, Proc. Inst. Mech. Eng., Part O: J. Risk Reliab., 231, 666
V.V. Kishore K. Reddy, Soumalya Sarkar, M. Giering, Anomaly Detection and Fault Disambiguation in Large Flight Data: A Multi-modal Deep Auto-encoder Approach, in: Annual Conference of the Prognostics and Health Management Society, Denver, Colorado, 1–8, 2016.
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
R. Thirukovalluru, S. Dixit, R.K. Sevakula, N.K. Verma, A. Salour, Generating feature sets for fault diagnosis using denoising stacked auto-encoder, in: IEEE International Conference on Prognostics and Health Management (ICPHM), 1–7, 2016.
Wang, 2016, Transformer fault diagnosis using continuous sparse autoencoder, SpringerPlus, 5, 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
Cambria, 2013, Extreme learning machines [Trends Controversies], IEEE Intell. Syst., 28, 30, 10.1109/MIS.2013.140
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
Shao, 2017, A novel deep autoencoder feature learning method for rotating machinery fault diagnosis, Mech. Syst. Signal Processing, 95, 187, 10.1016/j.ymssp.2017.03.034
Shao, 2018, A novel method for intelligent fault diagnosis of rolling bearings using ensemble deep auto-encoders, Mech. Syst. Signal Processing, 102, 278, 10.1016/j.ymssp.2017.09.026
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
Li, 2017, Bearing fault diagnosis using fully-connected winner-take-all autoencoder, IEEE Access
W. Lu, X. Wang, C. Yang, T. Zhang, A novel feature extraction method using deep neural network for rolling bearing fault diagnosis, in: The 27th Chinese Control and Decision Conference (2015 CCDC), 2427–2431, 2015.
J. Deutsch, D. He, Using deep learning based approaches for bearing remaining useful life prediction, in: Annual Conference of the Prognostics and Health Management Society, PHM Society, 1–7, 2016.
Deutsch, 2018, Using deep learning-based approach to predict remaining useful life of rotating components, IEEE Trans. Syst., Man, Cybern.: Syst., 48, 11, 10.1109/TSMC.2017.2697842
Liao, 2016, Enhanced restricted boltzmann machine with prognosability regularization for prognostics and health assessment, IEEE Trans. Industr. Electron., 63, 7076, 10.1109/TIE.2016.2586442
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
Li, 2016, Fault diagnosis for rotating machinery using vibration measurement deep statistical feature learning, Sensors, 16, 895, 10.3390/s16060895
Li, 2016, Gearbox fault diagnosis based on deep random forest fusion of acoustic and vibratory signals, Mech. Syst. Signal Processing, 76, 283, 10.1016/j.ymssp.2016.02.007
Shao, 2017, A deep learning approach for fault diagnosis of induction motors in manufacturing, Chinese J. Mech. Eng., 30, 1347, 10.1007/s10033-017-0189-y
Zhang, 2017, A deep learning-based recognition method for degradation monitoring of ball screw with multi-sensor data fusion, Microelectron. Reliab., 75, 215, 10.1016/j.microrel.2017.03.038
Wang, 2017, A hydraulic fault diagnosis method based on sliding-window spectrum feature and deep belief network, J. Vibroengineering, 19, 4272, 10.21595/jve.2017.18549
Han, 2017, A new fault diagnosis method based on deep belief network and support vector machine with Teager-Kaiser energy operator for bearings, Adv. Mech. Eng., 9, 10.1177/1687814017743113
Wang, 2017, A deep learning-based approach to material removal rate prediction in polishing, CIRP Ann.-Manuf. Technol., 66, 429, 10.1016/j.cirp.2017.04.013
Chen, 2017, Deep neural networks-based rolling bearing fault diagnosis, Microelectron. Reliab., 75, 327, 10.1016/j.microrel.2017.03.006
Gao, 2017, Deep quantum inspired neural network with application to aircraft fuel system fault diagnosis, Neurocomputing, 238, 13, 10.1016/j.neucom.2017.01.032
Oh, 2018, Scalable and unsupervised feature engineering using vibration-imaging and deep learning for rotor system diagnosis, IEEE Trans. Industr. Electron., 65, 3539, 10.1109/TIE.2017.2752151
M. Ma, X. Chen, S. Wang, Y. Liu, W. Li, Bearing degradation assessment based on weibull distribution and deep belief network, in: Proceedings of 2016 International Symposium of Flexible Automation (ISFA), 1–4, 2016.
S. Shao, W. Sun, P. Wang, R.X. Gao, R. Yan, Learning features from vibration signals for induction motor fault diagnosis, in: Proceedings of 2016 International Symposium of Flexible Automation (ISFA), 1–6, 2016.
Fu, 2015, Analysis of feature extracting ability for cutting state monitoring using deep belief networks, Procedia CIRP, 31, 29, 10.1016/j.procir.2015.03.016
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
P. Tamilselvan, Y. Wang, P. Wang, Deep belief network based state classification for structural health diagnosis, in: 2012 IEEE Aerospace Conference, 1–11, 2012.
Tao, 2016, Bearing fault diagnosis based on deep belief network and multisensor information fusion, Shock Vib., 9
Chen, 2015, Multi-layer neural network with deep belief network for gearbox fault diagnosis, J. Vibroeng., 17, 2379
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 Processing, 72, 92, 10.1016/j.ymssp.2015.11.014
H. Oh, B.C. Jeon, J.H. Jung, B.D. Youn, Unsupervised feature extraction scheme by deep learning, in: Annual Conference of the Prognostic and Health Management Society, PHM Society, 1–8, 2016.
C. Zhang, J.H. Sun, K.C. Tan, Deep belief networks ensemble with multi-objective optimization for failure diagnosis, in: Systems, Man, and Cybernetics (SMC), 2015 IEEE International Conference on, IEEE, 32–37, 2015.
Zhang, 2017, Multiobjective deep belief networks ensemble for remaining useful life estimation in prognostics, IEEE Trans. Neural Networks Learn. Syst., 28, 2306, 10.1109/TNNLS.2016.2582798
Liu, 2017, Dislocated time series convolutional neural architecture: an intelligent fault diagnosis approach for electric machine, IEEE Trans. Industr. Inf., 13, 1310, 10.1109/TII.2016.2645238
Wang, 2017, Virtualization and deep recognition for System Fault Classification, J. Manuf. Syst., 44, 310, 10.1016/j.jmsy.2017.04.012
Janssens, 2016, Convolutional neural network based fault detection for rotating machinery, J. Sound Vib., 377, 331, 10.1016/j.jsv.2016.05.027
Lu, 2017, Intelligent fault diagnosis of rolling bearing using hierarchical convolutional network based health state classification, Adv. Eng. Inform., 32, 139, 10.1016/j.aei.2017.02.005
G.S. Babu, P. Zhao, X.-L. Li, Deep convolutional neural network based regression approach for estimation of remaining useful life, in: International Conference on Database Systems for Advanced Applications, Springer, 214–228, 2016.
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
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
LeCun, 1998, Gradient-based learning applied to document recognition, Proc. IEEE, 86, 2278, 10.1109/5.726791
Sun, 2017, An intelligent gear fault diagnosis methodology using a complex wavelet enhanced convolutional neural network, Materials, 10, 790, 10.3390/ma10070790
J. Wang, j. Zhuang, L. Duan, W. Cheng, A multi-scale convolution neural network for featureless fault diagnosis, in: Proceedings of 2016 International Symposium of Flexible Automation (ISFA), 1–6, 2016c.
Chen, 2015, Gearbox fault identification and classification with convolutional neural networks, Shock Vib, 10
D. Weimer, B. Scholz-Reiter, M. Shpitalni, Design of deep convolutional neural network architectures for automated feature extraction in industrial inspection, CIRP Annals-Manufacturing Technology.
Dong, 2016, Small fault diagnosis of front-end speed controlled wind generator based on deep learning, WSEAS Trans. Circuits Syst., 15, 64
You, 2017, A hybrid technique based on convolutional neural network and support vector regression for intelligent diagnosis of rotating machinery, Adv. Mech. Eng., 9, 168, 10.1177/1687814017704146
Lee, 2017, A convolutional neural network for fault classification and diagnosis in semiconductor manufacturingprocesses, IEEE Trans. Semicond. Manuf., 30, 135, 10.1109/TSM.2017.2676245
L. Wen, X. Li, L. Gao, Y. Zhang, A new convolutional neural network based data-driven fault diagnosis method, IEEE Transactions on Industrial Electronics doi10.1109/TIE.2017.2774777.
Li, 2017, An ensemble deep convolutional neural network model with improved DS evidence fusion for bearing fault diagnosis, Sensors, 17, 1729, 10.3390/s17081729
Singh, 2017, Compound fault prediction of rolling bearing using multimedia data, Multimedia Tools Appl., 1
Verstraete, 2017, Deep learning enabled fault diagnosis using time-frequency image analysis of rolling element bearings, Shock and Vibration, 17
Ince, 2016, Real-time motor fault detection by 1-D convolutional neural networks, IEEE Trans. Industr. Electron., 63, 7067, 10.1109/TIE.2016.2582729
Abdeljaber, 2017, Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks, J. Sound Vib., 388, 154, 10.1016/j.jsv.2016.10.043
Jing, 2017, A convolutional neural network based feature learning and fault diagnosis method for the condition monitoring of gearbox, Measurement, 111, 1, 10.1016/j.measurement.2017.07.017
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 Processing, 100, 439, 10.1016/j.ymssp.2017.06.022
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
Y. Li, N. Wang, J. Shi, J. Liu, X. Hou, Revisiting batch normalization for practical domain adaptation, arXiv preprint arXiv:1603.04779.
Sun, 2017, Convolutional discriminative feature learning for induction motor fault diagnosis, IEEE Trans. Industr. Inf., 13, 1350, 10.1109/TII.2017.2672988
Cabrera, 2017, Automatic feature extraction of time-series applied to fault severity assessment of helical gearbox in stationary and non-stationary speed operation, Appl. Soft Comput., 58, 53, 10.1016/j.asoc.2017.04.016
Shao, 2018, Electric locomotive bearing fault diagnosis using novel convolutional deep belief network, IEEE Trans. Industr. Electron., 65, 4290
Zhao, 2017, Deep residual networks with dynamically weighted wavelet coefficients for fault diagnosis of planetary gearboxes, IEEE Trans. Ind. Electron.
Pan, 2017, LiftingNet: a novel deep learning network with layerwise feature learning from noisy mechanical data for fault classification, IEEE Trans. Ind. Electron.
M. Yuan, Y. Wu, L. Lin, Fault diagnosis and remaining useful life estimation of aero engine using LSTM neural network, in: 2016 IEEE International Conference on Aircraft Utility Systems (AUS), 135–140, 2016.
R. Zhao, J. Wang, R. Yan, K. Mao, Machine health monitoring with LSTM networks, in: IEEE International Conference on Sensing Technology, 1–6, 2016.
Zhao, 2017, Learning to monitor machine health with convolutional bi-directional lstm networks, Sensors, 17, 273, 10.3390/s17020273
Zhao, 2017, Machine health monitoring using local feature-based gated recurrent unit networks, IEEE Trans. Industr. Electron., 65, 1539, 10.1109/TIE.2017.2733438
P. Malhotra, A. Ramakrishnan, G. Anand, L. Vig, P. Agarwal, G. Shroff, Multi-sensor prognostics using an unsupervised health index based on LSTM Encoder-Decoder, arXiv preprint arXiv:1608.06154.
Stojanovic, 2016, Robust identification of OE model with constrained output using optimal input design, J. Franklin Inst., 353, 576, 10.1016/j.jfranklin.2015.12.007
Stojanovic, 2014, Adaptive input design for identification of output error model with constrained output, Circuits, Systems, Signal Processing, 33, 97, 10.1007/s00034-013-9633-0
Filipovic, 2011, Robust identification of pneumatic servo actuators in the real situationsRobuste Identifikation von pneumatischen Servo-Aktuatoren in der realen Situationen, Forsch. Ingenieurwes., 75, 183, 10.1007/s10010-011-0144-5
phm society, 2010 PHM Data Challenge, URLhttps://www.phmsociety.org/competition/phm/10, 2010
K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, arXiv preprint arXiv:1512.03385.
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, L.F.-F., ImageNet: A Large-Scale Hierarchical Image Database, in: 2009 IEEE Conference on Computer Vision and Pattern Recognition, 248–255, 2009.
Maaten, 2008, Visualizing data using t-SNE, J. Mach. Learn. Res., 9, 2579
J. Yosinski, J. Clune, A. Nguyen, T. Fuchs, H. Lipson, Understanding neural networks through deep visualization, arXiv preprint arXiv:1506.06579.
F. Shen, C. Chen, R. Yan, R.X. Gao, Bearing fault diagnosis based on SVD feature extraction and transfer learning classification, in: 2015 IEEE Prognostics and System Health Management Conference (PHM), 1–6, 2015.
J. Xie, L. Zhang, L. Duan, J. Wang, On cross-domain feature fusion in gearbox fault diagnosis under various operating conditions based on Transfer Component Analysis, in: IEEE International Conference on Prognostics and Health Management (ICPHM), 1–6, 2016.
Lu, 2017, Deep model based domain adaptation for fault diagnosis, IEEE Trans. Industr. Electron., 64, 2296, 10.1109/TIE.2016.2627020
Mao, 2017, Online sequential prediction of bearings imbalanced fault diagnosis by extreme learning machine, Mech. Syst. Signal Processing, 83, 450, 10.1016/j.ymssp.2016.06.024
Duan, 2016, A new support vector data description method for machinery fault diagnosis with unbalanced datasets, Expert Syst. Appl., 64, 239, 10.1016/j.eswa.2016.07.039
C. Huang, Y. Li, C. Change Loy, X. Tang, Learning deep representation for imbalanced classification, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 5375–5384, 2016.