| Citation: | Chenglin WEN, Feiya LÜ. Review on Deep Learning Based Fault Diagnosis[J]. Journal of Electronics & Information Technology, 2020, 42(1): 234-248. doi: 10.11999/JEIT190715
|
The massive high-dimensional measurements accumulated by distributed control systems bring great computational and modeling complexity to the traditional fault diagnosis algorithms, which fail to take advantage of the higher-order information for online estimation. In view of its powerful ability of representation learning, deep learning based fault diagnosis is extensively studied, both in academia and in industry, making intelligent process control more automated and effective. In this paper, deep learning based fault diagnosis is reviewed and summarized as four parts, i.e., stacked auto-encoder based fault diagnosis, deep belief network based fault diagnosis, convolutional neural network based fault diagnosis, and recurrent neural network based fault diagnosis. Furthermore, some necessity and potential trends, "integrated innovation", "data + knowledge" and "information fusion", are discussed from the view of data preprocessing, network design and decision.
|
国务院. 国家中长期科学和技术发展规划纲要(2006–2020年)[R]. 北京: 国务院, 2006.
China, Outline of the national medium and long term science and technology development program (2006–2020) [R], 2006.
|
|
张颖伟, QIN S J. 复杂工业过程的故障诊断[M]. 沈阳: 东北大学出版社, 2007, 10–20.
ZHANG Yingwei and QIN S J. Fault Diagnosis of Complex Industrial Processes [M]. Shenyang: Northeastern University Press, 2006, 10–20.
|
|
周东华, 胡艳艳. 动态系统的故障诊断技术[J]. 自动化学报, 2009, 35(6): 748–758.
ZHOU Donghua and Hu Yanyan. Fault diagnosis techniques for dynamic systems[J]. Acta Automatica Sinica, 2009, 35(6): 748–758.
|
|
吴斌, 于春梅, 李强. 过程工业故障诊断[M]. 北京: 科学出版社, 2012.
WU Bin, YU Chunmei, and LI Qiang. Fault Diagnosis for Process Industry[M]. Beijing: Science Press, 2012.
|
|
工业和信息化部. 高端装备制造业“十二五”发展规划[R]. 北京: 工业和信息化部, 2012.
Ministry of Industry and Information Technology. Twelfth Five-year Development Plan of High-end Equipment Manufacturing Industry[R]. 2012.
|
|
文成林, 吕菲亚, 包哲静, 等. 基于数据驱动的微小故障诊断方法综述[J]. 自动化学报, 2016, 42(9): 1285–1299. doi: 10.16383/j.aas.2016.c160105
WEN Chenlin, LÜ Feiya, BAO Zhejing, et al. A review of data driven-based incipient fault diagnosis[J]. Acta Automatica Sinica, 2016, 42(9): 1285–1299. doi: 10.16383/j.aas.2016.c160105
|
|
HIMMELBLAU D M. Fault Detection and Diagnosis in Chemical and Petrochemical Processes[M]. New York: Elsevier Science Ltd, 1978: 45–70.
|
|
GERTLER J. Fault Detection and Diagnosis[M]. London, England: Springer, 2015: 5–10.
|
|
张杰, 阳宪惠. 多变量统计过程控制[M]. 北京: 化学工业出版社, 2000: 1–23.
ZHANG Jie and YANG Xianhui. Multivariate Statistical Process Control[M]. Beijing: Chemical Industry Press, 2000: 1–23.
|
|
周东华, 刘洋, 何潇. 闭环系统故障诊断技术综述[J]. 自动化学报, 2013, 39(11): 1933–1943. doi: 10.3724/SP.J.1004.2013.01933
ZHOU Donghua, LIU Yang, and HE Xiao. Review on fault diagnosis techniques for closed-loop systems[J]. Acta Automatica Sinica, 2013, 39(11): 1933–1943. doi: 10.3724/SP.J.1004.2013.01933
|
|
LEI Yaguo, JIA Feng, LIN Jing, et al. An intelligent fault diagnosis method using unsupervised feature learning towards mechanical big data[J]. IEEE Transactions on Industrial Electronics, 2016, 63(5): 3137–3147. doi: 10.1109/TIE.2016.2519325
|
|
周东华, 李钢, 李元. 数据驱动的工业过程故障诊断技术——基于主元分析与偏最小二乘的方法[M]. 北京: 科学出版社, 2011: 1–15.
ZHOU Donghua, LI Gang, and LI Yuan. Data-driven Industrial Process Fault Diagnosis Technology [M]. Beijing: Science Press, 2011: 1–15.
|
|
王福利, 常玉清, 王姝, 等. 多模态复杂工业过程监测及故障诊断[M]. 北京: 科学出版社, 2016: 1–5.
WANG Fuli, CHANG Yuqing, WANG Yi, et al. Monitoring and Fault Diagnosis of Multi-mode Complex Industrial Processes[M]. Beijing: Science Press, 2016: 1–5.
|
|
YIN Shen, DING S X, XIE Xiaochen, et al. A review on basic data-driven approaches for industrial process monitoring[J]. IEEE Transactions on Industrial Electronics, 2014, 61(11): 6418–6428. doi: 10.1109/TIE.2014.2301773
|
|
YIN Shen, Li Xianwei, Gao Huijun, et al. Data-based techniques focused on modern industry: An overview[J]. IEEE Transactions on Industrial Electronics, 2015, 62(1): 657–667. doi: 10.1109/TIE.2014.2308133
|
|
LIU Ruonan, YANG Boyuan, ZIO E, et al. Artificial intelligence for fault diagnosis of rotating machinery: A review[J]. Mechanical Systems and Signal Processing, 2018, 108: 33–47. doi: 10.1016/j.ymssp.2018.02.016
|
|
ZHAO Rui, YAN Ruqiang, CHEN Zhenghua, et al. Deep learning and its applications to machine health monitoring[J]. Mechanical Systems and Signal Processing, 2019, 115: 213–237. doi: 10.1016/j.ymssp.2018.05.050
|
|
HORNIK K, STINCHCOMBE M, and WHITE H. Multilayer feedforward networks are universal approximators[J]. Neural Networks, 1989, 2(5): 359–366. doi: 10.1016/0893-6080(89)90020-8
|
|
CHANG C H. Deep and shallow architecture of multilayer neural networks[J]. IEEE Transactions on Neural Networks and Learning Systems, 2015, 26(10): 2477–2486. doi: 10.1109/TNNLS.2014.2387439
|
|
HINTON G E and SALAKHUTDINOV R R. Reducing the dimensionality of data with neural networks[J]. Science, 2006, 313(5786): 504–507. doi: 10.1126/science.1127647
|
|
LECUN Y, BENGIO Y, and HINTON G. Deep learning[J]. Nature, 2015, 521(7553): 436–444. doi: 10.1038/nature14539
|
|
GOODFELLOW I, BENGIO Y, and COURVILLE A. Deep Learning[M]. Cambridge, Massachusetts: MIT Press, 2016: 1–50.
|
|
SCHMIDHUBER J. Deep learning in neural networks: An overview[J]. Neural Networks, 2015, 61: 85–117. doi: 10.1016/j.neunet.2014.09.003
|
|
LEVINE S, PASTOR P, KRIZHEVSKY A, et al. Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection[J]. The International Journal of Robotics Research, 2018, 37(4/5): 421–436. doi: 10.1177/0278364917710318
|
|
KERMANY D S, GOLDBAUM M, CAI Wenjia, et al. Identifying medical diagnoses and treatable diseases by image-based deep learning[J]. Cell, 2018, 172(5): 1122–1131. doi: 10.1016/j.cell.2018.02.010
|
|
YOUNG T, HAZARIKA D, PORIA S, et al. Recent trends in deep learning based natural language processing[J]. IEEE Computational Intelligence Magazine, 2018, 13(3): 55–75. doi: 10.1109/MCI.2018.2840738
|
|
DENG Li and LIU Yang. Deep Learning in Natural Language Processing[M]. Singapore: Springer, 2018: 1–120.
|
|
BENGIO Y, COURVILLE A, and VINCENT P. Representation learning: A review and new perspectives[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(8): 1798–1828. doi: 10.1109/TPAMI.2013.50
|
|
JIA Feng, LEI Yaguo, LIN Jing, et al. Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data[J]. Mechanical Systems and Signal Processing, 2016, 72/73: 303–315. doi: 10.1016/j.ymssp.2015.10.025
|
|
CHEN Zhiqiang, CHEN Xudong, LI Chuan, et al. Vibration-based gearbox fault diagnosis using deep neural networks[J]. Journal of Vibroengineering, 2017, 19(4): 2475–2496. doi: 10.21595/jve.2016.17267
|
|
WANG J, MA Y, ZHANG L, et al. Deep learning for smart manufacturing: Methods and applications[J]. Journal of Manufacturing Systems, 2018, 48: 144–156. doi: 10.1016/j.jmsy.2018.01.003
|
|
GAN Meng, WANG Cong, and ZHU Chang’an. Construction of hierarchical diagnosis network based on deep learning and its application in the fault pattern recognition of rolling element bearings[J]. Mechanical Systems and Signal Processing, 2016, 72/73: 92–104. doi: 10.1016/j.ymssp.2015.11.014
|
|
SOHAIB M, KIM C H, and KIM J M. A hybrid feature model and deep-learning-based bearing fault diagnosis[J]. Sensors, 2017, 17(12): 2876. doi: 10.3390/s17122876
|
|
MCCULLOCH W S and PITTS W. A logical calculus of the ideas immanent in nervous activity[J]. The Bulletin of Mathematical Biophysics, 1943, 5(4): 115–133. doi: 10.1007/BF02478259
|
|
WATANABE K, MATSUURA I, ABE M, et al. Incipient fault diagnosis of chemical processes via artificial neural networks[J]. AICHE Journal, 1989, 35(11): 1803–1812. doi: 10.1002/aic.690351106
|
|
CHOW M Y, MANGUM P, and THOMAS R J. Incipient fault detection in DC machines using a neural network[C]. The 22nd Asilomar Conference on Signals, Systems and Computers, Pacific Grove, USA, 1988: 706–709. doi: 10.1109/ACSSC.1988.754641.
|
|
QI Yumei, SHEN Changqing, WANG Dong, et al. Stacked sparse autoencoder-based deep network for fault diagnosis of rotating machinery[J]. IEEE Access, 2017, 5: 15066–15079. doi: 10.1109/ACCESS.2017.2728010
|
|
LÜ Feiya, WEN Chenglin, LIU Meiqin, et al. Higher-order correlation-based multivariate statistical process monitoring[J]. Journal of Chemometrics, 2018, 32(8): e3033. doi: 10.1002/cem.3033
|
|
LÜ Feiya, WEN Chenglin, and LIU Meiqin. Representation learning based adaptive multimode process monitoring[J]. Chemometrics and Intelligent Laboratory Systems, 2018, 181: 95–104. doi: 10.1016/j.chemolab.2018.07.011
|
|
LÜ Feiya, WEN Chenglin, BAO Zejing, et al. Fault diagnosis based on deep learning[C]. 2016 American Control Conference, Boston, USA 2016: 6851–6856. doi: 10.1109/ACC.2016.7526751.
|
|
LIAO Linxia, JIN Wenjing, and PAVEL R. Enhanced restricted Boltzmann machine with prognosability regularization for prognostics and health assessment[J]. IEEE Transactions on Industrial Electronics, 2016, 63(11): 7076–7083. doi: 10.1109/TIE.2016.2586442
|
|
SHAO Haidong, JIANG Hongkai, ZHANG Haizhou, et al. Electric locomotive bearing fault diagnosis using a novel convolutional deep belief network[J]. IEEE Transactions on Industrial Electronics, 2018, 65(3): 2727–2736. doi: 10.1109/TIE.2017.2745473
|
|
CHEN Zhuyun and LI Weihua. Multisensor feature fusion for bearing fault diagnosis using sparse autoencoder and deep belief network[J]. IEEE Transactions on Instrumentation and Measurement, 2017, 66(7): 1693–1702. doi: 10.1109/TIM.2017.2669947
|
|
RAINA R, BATTLE A, LEE H, et al. Self-taught learning: Transfer learning from unlabeled data[C]. The 24th International Conference on Machine Learning, Corvalis, USA, 2007: 759–766. doi: 10.1145/1273496.1273592.
|
|
VINCENT P, LAROCHELLE H, LAJOIE I, et al. Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion[J]. The Journal of Machine Learning Research, 2010, 11: 3371–3408.
|
|
THIRUKOVALLURU R, DIXIT S, SEVAKULA R K, et al. Generating feature sets for fault diagnosis using denoising stacked auto-encoder[C]. 2016 IEEE International Conference on Prognostics and Health Management, Ottawa, Canada, 2016: 1–7. doi: 10.1109/ICPHM.2016.7542865.
|
|
LU Chen, WANG Zhenya, QIN Weili, et al. Fault diagnosis of rotary machinery components using a stacked denoising autoencoder-based health state identification[J]. Signal Processing, 2017, 130: 377–388. doi: 10.1016/j.sigpro.2016.07.028
|
|
JIA Feng, LEI Yaguo, GUO Liang, et al. A neural network constructed by deep learning technique and its application to intelligent fault diagnosis of machines[J]. Neurocomputing, 2018, 272: 619–628. doi: 10.1016/j.neucom.2017.07.032
|
|
MENG Zong, ZHAN Xuyang, LI Jing, et al. An enhancement denoising autoencoder for rolling bearing fault diagnosis[J]. Measurement, 2018, 130: 448–454. doi: 10.1016/j.measurement.2018.08.010
|
|
SUN Wenjun, SHAO Siyu, ZHAO Rui, et al. A sparse auto-encoder-based deep neural network approach for induction motor faults classification[J]. Measurement, 2016, 89: 171–178. doi: 10.1016/j.measurement.2016.04.007
|
|
LIU Han, ZHOU Jianzhong, XU Yanhe, et al. Unsupervised fault diagnosis of rolling bearings using a deep neural network based on generative adversarial networks[J]. Neurocomputing, 2018, 315: 412–424. doi: 10.1016/j.neucom.2018.07.034
|
|
MA Meng, SUN Chuang, and CHEN Xuefeng. Deep coupling autoencoder for fault diagnosis with multimodal sensory data[J]. IEEE Transactions on Industrial Informatics, 2018, 14(3): 1137–1145. doi: 10.1109/TII.2018.2793246
|
|
WEN Long, GAO Liang, and LI Xinyu. A new deep transfer learning based on sparse auto-encoder for fault diagnosis[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2019, 49(1): 136–144. doi: 10.1109/TSMC.2017.2754287
|
|
LÜ Feiya, WEN Chenglin, LIU Meiqin, et al. Weighted time series fault diagnosis based on a stacked sparse autoencoder[J]. Journal of Chemometrics, 2017, 31(9): e2912. doi: 10.1002/cem.2912
|
|
LÜ F, WEN C, and LIU M. Dynamic reconstruction based representation Learning for multivariable process monitoring[J]. Journal of Process Control, 2019, 81: 112–115. doi: 10.1016/j.chemolab.2018.07.011
|
|
HINTON G E and SEJNOWSKI T J. Learning and relearning in Boltzmann machines[J]. Parallel Distributed Processing: Explorations in the Microstructure of Cognition, 1986, 1: 282–317.
|
|
NAIR V and HINTON G E. Rectified linear units improve restricted Boltzmann machines[C]. The 27th International Conference on Machine Learning, Haifa, Israel, 2010: 807–814.
|
|
HINTON G. A Practical Guide to Training Restricted Boltzmann machines[M]. MONTAVON G, ORR G B, and MÜLLER K R. Neural Networks: Tricks of the Trade. 2nd ed. Berlin, Heidelberg: Springer, 2012: 599–619. doi: 10.1007/978-3-642-35289-8_32.
|
|
MOHAMED A R, DAHL G E, and HINTON G. Acoustic modeling using deep belief networks[J]. IEEE Transactions on Audio, Speech, and Language Processing, 2012, 20(1): 14–22. doi: 10.1109/tasl.2011.2109382
|
|
HUANG Wenhao, SONG Guojie, Hong Haikun, et al. Deep architecture for traffic flow prediction: Deep belief networks with multitask learning[J]. IEEE Transactions on Intelligent Transportation Systems, 2014, 15(5): 2191–2201. doi: 10.1109/TITS.2014.2311123
|
|
SALAKHUTDINOV R and HINTON G. Deep Boltzmann machines[C]. The 12th International Conference on Artificial Intelligence and Statistics, Florida, USA, 2009: 448–455.
|
|
ZHANG Chunyang, CHEN C L P, GAN Min, et al. Predictive deep Boltzmann machine for multiperiod wind speed forecasting[J]. IEEE Transactions on Sustainable Energy, 2015, 6(4): 1416–1425. doi: 10.1109/TSTE.2015.2434387
|
|
LE ROUX N and BENGIO Y. Representational power of restricted Boltzmann machines and deep belief networks[J]. Neural Computation, 2008, 20(6): 1631–1649. doi: 10.1162/neco.2008.04-07-510
|
|
SHAO Haidong, JIANG Hongkai, ZHANG Xun, et al. Rolling bearing fault diagnosis using an optimization deep belief network[J]. Measurement Science and Technology, 2015, 26(11): 115002. doi: 10.1088/0957-0233/26/11/115002
|
|
LI Chuan, SÁNCHEZ R V, ZURITA G, et al. Multimodal deep support vector classification with homologous features and its application to gearbox fault diagnosis[J]. Neurocomputing, 2015, 168: 119–127. doi: 10.1016/j.neucom.2015.06.008
|
|
LI Chuan, SÁNCHEZ R V, ZURITA G, et al. Gearbox fault diagnosis based on deep random forest fusion of acoustic and vibratory signals[J]. Mechanical Systems and Signal Processing, 2016, 76–77: 283–293. doi: 10.1016/j.ymssp.2016.02.007
|
|
TAO Jie, LIU Yilun, and YANG Dalian. Bearing fault diagnosis based on deep belief network and multisensor information fusion[J]. Shock and Vibration, 2016, 201: 9306205. doi: 10.1155/2016/9306205
|
|
WANG Lukun, ZHAO Xiaoying, PEI Jiangnan, et al. Transformer fault diagnosis using continuous sparse autoencoder[J]. SpringerPlus, 2016, 5: 448. doi: 10.1186/s40064-016-2107-7
|
|
SHAO Haidong, JIANG Hongkai, WANG Fuan, et al. Rolling bearing fault diagnosis using adaptive deep belief network with dual-tree complex wavelet packet[J]. ISA Transactions, 2017, 69: 187–201. doi: 10.1016/j.isatra.2017.03.017
|
|
TRAN V T, ALTHOBIANI F, and BALL A. An approach to fault diagnosis of reciprocating compressor valves using Teager-Kaiser energy operator and deep belief networks[J]. Expert Systems with Applications, 2014, 41(9): 4113–4122. doi: 10.1016/j.eswa.2013.12.026
|
|
ZHANG Zhanpeng and ZHAO Jinsong. A deep belief network based fault diagnosis model for complex chemical processes[J]. Computers & Chemical Engineering, 2017, 107: 395–407. doi: 10.1016/j.compchemeng.2017.02.041
|
|
ZHANG Chong, LIM P, QIN A K, et al. Multiobjective deep belief networks ensemble for remaining useful life estimation in prognostics[J]. IEEE Transactions on Neural Networks and Learning Systems, 2017, 28(10): 2306–2318. doi: 10.1109/TNNLS.2016.2582798
|
|
ZHAO Guangquan, ZHANG Guohui, LIU Yuefeng, et al. Lithium-ion battery remaining useful life prediction with deep belief network and relevance vector machine[C]. 2017 IEEE International Conference on Prognostics and Health Management, Dallas, USA, 2017: 7–13. doi: 10.1109/ICPHM.2017.7998298.
|
|
RUSSAKOVSKY O, DENG Jia, SU Hao, et al. ImageNet large scale visual recognition challenge[J]. International Journal of Computer Vision, 2015, 115(3): 211–252. doi: 10.1007/s11263-015-0816-y
|
|
GU Jiuxiang, WANG Zhenhua, KUEN J, et al. Recent advances in convolutional neural networks[J]. Pattern Recognition, 2018, 77: 354–377. doi: 10.1016/j.patcog.2017.10.013
|
|
CHEN Zhiqiang, Li Chuan, and SÁNCHEZ R V. Gearbox fault identification and classification with convolutional neural networks[J]. Shock and Vibration, 2015, 2015: 390134. doi: 10.1155/2015/390134
|
|
JANSSENS O, SLAVKOVIKJ V, VERVISCH B, et al. Convolutional neural network based fault detection for rotating machinery[J]. Journal of Sound and Vibration, 2016, 377: 331–345. doi: 10.1016/j.jsv.2016.05.027
|
|
DING Xiaoxi and HE Qingbo. Energy-fluctuated multiscale feature learning with deep ConvNet for intelligent spindle bearing fault diagnosis[J]. IEEE Transactions on Instrumentation and Measurement, 2017, 66(8): 1926–1935. doi: 10.1109/TIM.2017.2674738
|
|
WANG Jinjiang, ZHUANG Junfei, DUAN Lixiang, et al. A multi-scale convolution neural network for featureless fault diagnosis[C]. 2016 International Symposium on Flexible Automation, Cleveland, USA, 2016: 65–70. doi: 10.1109/ISFA.2016.7790137.
|
|
GUO Xiaojie, CHEN Liang, and SHEN Changqing. Hierarchical adaptive deep convolution neural network and its application to bearing fault diagnosis[J]. Measurement, 2016, 93: 490–502. doi: 10.1016/j.measurement.2016.07.054
|
|
BABU G S, ZHAO Peilin, and LI Xiaoli. Deep convolutional neural network based regression approach for estimation of remaining useful life[C]. The 21st International Conference on Database Systems for Advanced Applications, Dallas, USA, 2016: 214–228. doi: 10.1007/978-3-319-32025-0_14.
|
|
WEIMER D, SCHOLZ-REITER B, and SHPITALNI M. Design of deep convolutional neural network architectures for automated feature extraction in industrial inspection[J]. CIRP Annals, 2016, 65(1): 417–420. doi: 10.1016/j.cirp.2016.04.072
|
|
KIRANYAZ S, INCE T, and GABBOUJ M. Real-time patient-specific ECG classification by 1-D convolutional neural networks[J]. IEEE Transactions on Biomedical Engineering, 2016, 63(3): 664–675. doi: 10.1109/TBME.2015.2468589
|
|
ABDELJABER O, AVCI O, KIRANYAZ S, et al. Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks[J]. Journal of Sound and Vibration, 2017, 388: 154–170. doi: 10.1016/j.jsv.2016.10.043
|
|
INCE T, KIRANYAZ S, EREN L, et al. Real-time motor fault detection by 1-D convolutional neural networks[J]. IEEE Transactions on Industrial Electronics, 2016, 63(11): 7067–7075. doi: 10.1109/TIE.2016.2582729
|
|
JING Luyang, ZHAO Ming, LI Pin, et al. A convolutional neural network based feature learning and fault diagnosis method for the condition monitoring of gearbox[J]. Measurement, 2017, 111: 1–10. doi: 10.1016/j.measurement.2017.07.017
|
|
LI Dan, CHEN Dacheng, SHI Lei, et al. MAD-GAN: Multivariate anomaly detection for time series data with generative adversarial networks[J]. arXiv preprint arXiv:1901.04997, 2019.
|
|
LIU Jinhai, QU Fuming, HONG Xiaowei, et al. A small-sample wind Turbine fault detection method with synthetic fault data using generative adversarial nets[J]. IEEE Transactions on Industrial Informatics, 2019, 15(7): 3877–3888. doi: 10.1109/TII.2018.2885365
|
|
WANG Zirui, WANG Jun, and WANG Youren. An intelligent diagnosis scheme based on generative adversarial learning deep neural networks and its application to planetary gearbox fault pattern recognition[J]. Neurocomputing, 2018, 310: 213–222. doi: 10.1016/j.neucom.2018.05.024
|
|
LI Xiang, ZHANG Wei, and DING Qian. Cross-domain fault diagnosis of rolling element bearings using deep generative neural networks[J]. IEEE Transactions on Industrial Electronics, 2019, 66(7): 5525–5534. doi: 10.1109/TIE.2018.2868023
|
|
LEE Y O, JO J, and HWANG J. Application of deep neural network and generative adversarial network to industrial maintenance: A case study of induction motor fault detection[C]. 2017 IEEE International Conference on Big Data, Boston, USA, 2017: 3248–3253. doi: 10.1109/BigData.2017.8258307.
|
|
LU Ping, MORRIS M, BRAZELL S, et al. Using generative adversarial networks to improve deep-learning fault interpretation networks[J]. The Leading Edge, 2018, 37(8): 578–583. doi: 10.1190/tle37080578.1
|
|
GILES C L, MILLER C B, CHEN D, et al. Learning and extracting finite state automata with second-order recurrent neural Networks[J]. Neural Computation, 1992, 4(3): 393–405. doi: 10.1162/neco.1992.4.3.393
|
|
FUNAHASHI K I and NAKAMURA Y. Approximation of dynamical systems by continuous time recurrent neural networks[J]. Neural Networks, 1993, 6(6): 801–806. doi: 10.1016/S0893-6080(05)80125-X
|
|
SAK H, SENIOR A, and BEAUFAYS F. Long short-term memory recurrent neural network architectures for large scale acoustic modeling[C]. The 15th Annual Conference of the International Speech Communication Association, Singapore, 2014: 338–342.
|
|
CHUNG J, GULCEHRE C, CHO K, et al. Empirical evaluation of gated recurrent neural networks on sequence modeling[J]. arXiv preprint arXiv:1412.3555, 2014.
|
|
GUO Liang, LI Naipeng, JIA Feng, et al. A recurrent neural network based health indicator for remaining useful life prediction of bearings[J]. Neurocomputing, 2017, 240: 98–109. doi: 10.1016/j.neucom.2017.02.045
|
|
MALHOTRA P, TV V, RAMAKRISHNAN A, et al. Multi-sensor prognostics using an unsupervised health index based on LSTM encoder-decoder[C]. The 1st ACM SIGKDD Workshop on Machine Learning for Prognostics and Health Management, San Francisco, USA, 2016.
|
|
YUAN Mei, WU Yuting, and LIN Li. Fault diagnosis and remaining useful life estimation of aero engine using LSTM neural network[C]. 2016 IEEE International Conference on Aircraft Utility Systems, Beijing, China, 2016: 135–140. doi: 10.1109/AUS.2016.7748035.
|
|
LIU Han, ZHOU Jianzhong, ZHENG Yang, et al. Fault diagnosis of rolling bearings with recurrent neural network-based autoencoders[J]. ISA Transactions, 2018, 77: 167–178. doi: 10.1016/j.isatra.2018.04.005
|
|
LI Xiaochuan, DUAN Fang, LOUKOPOULOS P, et al. Canonical variable analysis and long short-term memory for fault diagnosis and performance estimation of a centrifugal compressor[J]. Control Engineering Practice, 2018, 72: 177–191. doi: 10.1016/j.conengprac.2017.12.006
|
|
JIANG Hongkai, LI Xingqiu, SHAO Haidong, et al. Intelligent fault diagnosis of rolling bearings using an improved deep recurrent neural network[J]. Measurement Science and Technology, 2018, 29(6): 065107. doi: 10.1088/1361-6501/aab945
|
|
YANG Rui, HUANG Mengjie, LU Qidong, et al. Rotating machinery fault diagnosis using long-short-term memory recurrent neural network[J]. IFAC-PapersOnLine, 2018, 51(24): 228–232. doi: 10.1016/j.ifacol.2018.09.582
|
|
ZHAO Rui, YAN Ruqiang, WANG Jinjiang, et al. Learning to monitor machine health with convolutional bi-directional LSTM networks[J]. Sensors, 2017, 17(2): 273. doi: 10.3390/s17020273
|
Figures(7)
DownLoad:
