基于深度学习的故障诊断方法综述

文成林; 吕菲亚

doi:10.11999/JEIT190715

基于深度学习的故障诊断方法综述

doi: 10.11999/JEIT190715

文成林^1,,
吕菲亚^2, ,

1.
杭州电子科技大学自动化学院杭州 310018
2.
安阳师范学院软件学院安阳 455000

基金项目: 国家自然科学基金(U1509203, 61751304, 61573137, 61673160)，浙江省重点项目(LZ16F030002)

详细信息

作者简介:
文成林：男，1963年生，教授，主要研究方向为故障诊断，多目标跟踪，信息融合等

吕菲亚：女，1991年生，博士，讲师，主要研究方向为故障诊断，机器学习，信息融合等

通讯作者:
吕菲亚　lvfeiya0215@126.com

¹⁾本文所讨论的故障实时诊断与预测技术均假定故障可被感知并能被分离，可被感知是指故障在一定程度上影响系统的状态和输出，能被分离是指依据现有信息可以指示故障发生部位和发生机理。²⁾机理分析方法指是通过对系统内部原因/机理的分析研究，从而找出其发展变化规律的一种科学研究方法，依赖于因果关系的提取与表征，适用于输入、输出及状态变量较少的系统^[6]，包括分析方法和统计方法。³⁾特征工程指的是把原始数据转变为模型的训练数据的过程，目的是获取更好的训练数据特征，包括特征构建、特征提取、特征选择3个部分。⁴⁾虽然神经网络可以以任意精度逼近非线性函数^[18]，但是面对复杂工业过程的高维、非高斯分布、非线性、时变、多模态等特性，传统的神经网络方法多是从逼近论的角度拟合监测数据并进行特征提取，受限于网络结构训练算法和计算复杂度的影响，通常只是设置2到3个隐层，降低了逼近的精度。
⁵⁾假设特征图长宽相同.
中图分类号: TP274
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- 被引次数: 0
出版历程
- 收稿日期: 2019-09-17
- 修回日期: 2019-12-02
- 网络出版日期: 2019-12-10
- 刊出日期: 2020-01-21

Review on Deep Learning Based Fault Diagnosis

Chenglin WEN^1
,,
Feiya LÜ^{2
, ,}

1.
Institute of Automation, School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China
2.
School of Software Engineering, Anyang Normal University, Anyang 455000, China

Funds: The National Natural Science Foundation of China (U1509203, 61751304, 61573137, 61673160), Zhejiang Provincial Foundation (LZ16F030002)

摘要

摘要:
海量高维度的过程测量信息给传统的故障诊断算法带来极大的计算复杂度和建模复杂度，且传统诊断算法存在难以利用高阶量进行在线估计的不足。鉴于深度学习技术强大的数据表示学习和分析能力，基于深度学习的故障诊断引起了工业界和学术界的广泛关注，并促使智能过程控制更加自动化和有效。该文从方法上将基于深度学习的故障诊断技术分为：基于栈式自编码的故障诊断方法、基于深度置信网络的故障诊断方法、基于卷积神经网络的故障诊断方法及基于循环神经网络的故障诊断方法4类，分别进行了回顾和总结，最后从数据预处理、深度网络设计和决策3个层面对这一领域进行展望，提出了“集成创新”、“数据+知识”和“多技术融合”等故障诊断思想，阐明基于深度学习技术进行复杂系统的故障诊断仍具有巨大潜力。
- 故障诊断 /
- 数据驱动 /
- 深度学习 /
- 高阶相关性
Abstract:
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.
- Fault diagnosis /
- Data driven /
- Deep learning /
- Higher-order correlation
¹⁾本文所讨论的故障实时诊断与预测技术均假定故障可被感知并能被分离，可被感知是指故障在一定程度上影响系统的状态和输出，能被分离是指依据现有信息可以指示故障发生部位和发生机理。²⁾机理分析方法指是通过对系统内部原因/机理的分析研究，从而找出其发展变化规律的一种科学研究方法，依赖于因果关系的提取与表征，适用于输入、输出及状态变量较少的系统^[6]，包括分析方法和统计方法。³⁾特征工程指的是把原始数据转变为模型的训练数据的过程，目的是获取更好的训练数据特征，包括特征构建、特征提取、特征选择3个部分。⁴⁾虽然神经网络可以以任意精度逼近非线性函数^[18]，但是面对复杂工业过程的高维、非高斯分布、非线性、时变、多模态等特性，传统的神经网络方法多是从逼近论的角度拟合监测数据并进行特征提取，受限于网络结构训练算法和计算复杂度的影响，通常只是设置2到3个隐层，降低了逼近的精度。

⁵⁾假设特征图长宽相同.

HTML全文

图 1 数据驱动的故障诊断框架

下载: 全尺寸图片幻灯片

图 2 基于深度学习的故障诊断研究思路汇总

下载: 全尺寸图片幻灯片

图 3 基于深度学习的故障诊断方法分类

下载: 全尺寸图片幻灯片

图 4 栈式自编码网络的结构

下载: 全尺寸图片幻灯片

图 5 基于受限玻尔兹曼机的深度网络结构

下载: 全尺寸图片幻灯片

图 6 卷积神经网络的结构

下载: 全尺寸图片幻灯片

图 7 循环神经网络的结构

下载: 全尺寸图片幻灯片

参考文献(104)

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

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