[1] 吴春磊. 基于改进深度学习模型的过程故障识别研究[D]. 南昌:华东交通大学, 2019 WU Chunlei. Research on process fault identification based on improved depth learning model[D]. Nanchang:East China Jiaotong University, 2019(in Chinese)
[2] 孙旭, 李晓光, 李嘉锋, 等. 基于深度学习的图像超分辨率复原研究进展[J]. 自动化学报, 2017, 43(5):697-709 SUN Xu, LI Xiaoguang, LI Jiafeng, et al. Review on deep learning based image super-resolution restoration algorithms[J]. Acta Automatica Sinica, 2017, 43(5):697-709(in Chinese)
[3] ZHANG Q, ZHU S. Visual interpretability for deep learning:A survey[J]. Frontiers of Information Technology & Electronic Engineering, 2018, 19(1):27-39
[4] REN X, ZHOU Y, HUANG Z, et al. A novel text structure feature extractor for Chinese scene text detection and recognition[J]. IEEE Access, 2017, 5:3193-3204
[5] 奚雪峰, 周国栋. 面向自然语言处理的深度学习研究[J]. 自动化学报, 2016, 42(10):1445-1465 XI Xuefeng, ZHOU Guodong. A survey on deep learning for natural language processing[J]. Acta Automatica Sinica, 2016, 42(10):1445-1465(in Chinese)
[6] DRUZHKOV P N, KUSTIKOVA V D. A survey of deep learning methods and software tools for image classification and object detection[J]. Pattern Recognition and Image Analysis, 2016, 26(1):9-15
[7] HAN J, ZHANG D, CHENG G, et al. Advanced deep-learning techniques for salient and category-specific object detection:A survey[J]. IEEE Signal Processing Magazine, 2018, 35(1):84-100
[8] JIA F, LEI Y, LU N, et al. Deep normalized convolutional neural network for imbalanced fault classification of machinery and its understanding via visualization[J]. Mechanical Systems and Signal Processing, 2018, 110:349-367
[9] MAO W, FENG W, LIANG X. A novel deep output kernel learning method for bearing fault structural diagnosis[J]. Mechanical Systems and Signal Processing, 2019, 117:293-318
[10] SHAO H, JIANG H, ZHANG H, et al. Rolling bearing fault feature learning using improved convolutional deep belief network with compressed sensing[J]. Mechanical Systems and Signal Processing, 2018, 100:743-765
[11] LI F, PANG X, YANG Z. Motor current signal analysis using deep neural networks for planetary gear fault diagnosis[J]. Measurement, 2019, 145:45-54
[12] QIU G, GU Y, CAI Q. A deep convolutional neural networks model for intelligent fault diagnosis of a gearbox under different operational conditions[J]. Measurement, 2019, 145:94-107
[13] WANG S, XIANG J, ZHONG Y, et al. A data indicator-based deep belief networks to detect multiple faults in axial piston pumps[J]. Mechanical Systems and Signal Processing, 2018, 112:154-170
[14] LI Y, YANG Y, LI G, et al. A fault diagnosis scheme for planetary gearboxes using modified multi-scale symbolic dynamic entropy and mRMR feature selection[J]. Mechanical Systems and Signal Processing, 2017, 91:295-312
[15] GUO Y, TAN Z, CHEN H, et al. Deep learning-based fault diagnosis of variable refrigerant flow air-conditioning system for building energy saving[J]. Applied Energy, 2018, 225:732-745
[16] GE Z, SONG Z, GAO F. Review of recent research on data-based process monitoring[J]. Industrial & Engineering Chemistry Research, 2013, 52(10):3543-3562
[17] YOON S, MACGREGOR J F. Fault diagnosis with multivariate statistical models part I:Using steady state fault signatures[J]. Journal of Process Control, 2001, 11(4):387-400
[18] WANG Y, JIANG Q. Data-driven nonlinear chemical process fault diagnosis based on hierarchical representation learning[J]. The Canadian Journal of Chemical Engineering, 2020, 98(10):2150-2165
[19] GHOLIZADEH M, YAZDIZADEH A, MOHAMMAD-BAGHERPOUR H. Fault detection and identification using combination of EKF and neuro-fuzzy network applied to a chemical process (CSTR)[J]. Pattern Analysis and Applications, 2019, 22(2):359-373
[20] DOWNS J J, VOGEL E F. A plant-wide industrial process control problem[J]. Computers & Chemical Engineering, 1993, 17(3):245-255
[21] BATHELT A, RICKER N L, JELALI M. Revision of the Tennessee Eastman process model[J]. IFAC-PapersOnLine, 2015, 48(8):309-314
[22] MCAVOY T J, YE N. Base control for the Tennessee Eastman problem[J]. Computers & Chemical Engineering, 1994, 18(5):383-413
[23] SHAMS M B, BUDMAN H, DUEVER T. Fault detection using CUSUM based techniques with application to the Tennessee Eastman process[J]. IFAC Proceedings Volumes, 2010, 43(5):109-114
[24] YIN S, DING S X, HAGHANI A, et al. A comparison study of basic data-driven fault diagnosis and process monitoring methods on the benchmark Tennessee Eastman process[J]. Journal of Process Control, 2012, 22(9):1567-1581
[25] GE X, WANG B, YANG X, et al. Fault detection and diagnosis for reactive distillation based on convolutional neural network[J]. Computers & Chemical Engineering, 2021, doi:10.1016/j.compchemeng.2020.107172
[26] LI C, ZHAO D, MU S, et al. Fault diagnosis for distillation process based on CNN-DAE[J]. Chinese Journal of Chemical Engineering, 2019, 27(3):598-604
[27] ZHENG S, ZHAO J. A new unsupervised data mining method based on the stacked autoencoder for chemical process fault diagnosis[J]. Computers & Chemical Engineering, 2020, doi:10.1016/j.compchemeng.2020.106755
[28] PENG J, HU Z, LIU J, et al. Fault diagnosis based on chemical sensor data with an active deep neural network[J]. Sensors, 2016, doi:10.3390/s16101695
[29] LV F, WEN C, BAO Z, et al. Fault diagnosis based on deep learning[C]//2016 American Control Conference (ACC). Boston, MA, USA:IEEE, 2016:6851-6856
[30] LV F, WEN C, LIU M, et al. Weighted time series fault diagnosis based on a stacked sparse autoencoder[J]. Journal of Chemometrics, 2017, doi:10.1002/cem.2912
[31] GUO C, HU W, YANG F, et al. Deep learning technique for process fault detection and diagnosis in the presence of incomplete data[J]. Chinese Journal of Chemical Engineering, 2020, 28(9):2358-2367
[32] YAN W, GUO P, GONG L, et al. Nonlinear and robust statistical process monitoring based on variant autoencoders[J]. Chemometrics and Intelligent Laboratory Systems, 2016, 158:31-40
[33] YIN J, YAN X. Mutual information-dynamic stacked sparse autoencoders for fault detection[J]. Industrial & Engineering Chemistry Research, 2019, 58(47):21614-21624
[34] YIN J, YAN X. Stacked sparse autoencoders monitoring model based on fault-related variable selection[J]. Soft Computing, 2021, 25(5):3531-3543
[35] ZHANG Z, JIANG T, LI S, et al. Automated feature learning for nonlinear process monitoring-An approach using stacked denoising autoencoder and k-nearest neighbor rule[J]. Journal of Process Control, 2018, 64:49-61
[36] 张祥, 崔哲, 董玉玺, 等. 基于VAE-DBN的故障分类方法在化工过程中的应用[J]. 过程工程学报, 2018, 18(3):590-594 ZHANG Xiang, CUI Zhe, DONG Yuxi, et al. Application of fault classification method based on VAE-DBN in chemical process[J]. The Chinese Journal of Process Engineering, 2018, 18(3):590-594(in Chinese)
[37] LEE Y S, CHEN J. Enhancing monitoring performance of data sparse nonlinear processes through information sharing among different grades using Gaussian mixture prior variational autoencoders[J]. Chemometrics and Intelligent Laboratory Systems, 2021, doi:j.chemolab.2020.104219
[38] YANG Z, GE Z. Monitoring and prediction of big process data with deep latent variable models and parallel computing[J]. Journal of Process Control, 2020, 92:19-34
[39] LI J, YAN X. Process monitoring using principal component analysis and stacked autoencoder for linear and nonlinear coexisting industrial processes[J]. Journal of the Taiwan Institute of Chemical Engineers, 2020, 112:322-329
[40] JIANG L, GE Z, SONG Z. Semi-supervised fault classification based on dynamic Sparse Stacked auto-encoders model[J]. Chemometrics and Intelligent Laboratory Systems, 2017, 168:72-83
[41] WANG Y, YANG H, YUAN X, et al. Deep learning for fault-relevant feature extraction and fault classification with stacked supervised auto-encoder[J]. Journal of Process Control, 2020, 92:79-89
[42] YU J, LIU X, YE L. Convolutional long short-term memory autoencoder-based feature learning for fault detection in industrial processes[J]. IEEE Transactions on Instrumentation and Measurement, 2021, 70:1-15
[43] LI Z, TIAN L, JIANG Q, et al. Fault diagnostic method based on deep learning and multimodel feature fusion for complex industrial processes[J]. Industrial & Engineering Chemistry Research, 2020, 59(40):18061-18069
[44] HINTON G E, SALAKHUTDINOV R R. Reducing the dimensionality of data with neural networks[J]. Science, 2006, 313(5786):504-507
[45] TAMILSELVAN P, WANG P F. Failure diagnosis using deep belief learning based health state classification[J]. Reliability Engineering & System Safety, 2013, 115:124-135
[46] TRAN V T, ALTHOBIANI F, 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
[47] ZHANG Z, ZHAO J. A deep belief network based fault diagnosis model for complex chemical processes[J]. Computers & Chemical Engineering, 2017, 107:395-407
[48] WANG Y, ZHANG J, DENG F. A method of fault diagnosis based on DE-DBN[M]//Lecture Notes in Electrical Engineering. Singapore:Springer Singapore, 2017:209-217
[49] WANG Y, PAN Z, YUAN X, et al. A novel deep learning based fault diagnosis approach for chemical process with extended deep belief network[J]. ISA Transactions, 2020, 96:457-467
[50] WEI Y, WENG Z. Research on TE process fault diagnosis method based on DBN and dropout[J]. The Canadian Journal of Chemical Engineering, 2020, 98(6):1293-1306
[51] 程换新, 王建庆. 改进深度置信网络对TE过程故障诊断研究[J]. 电子测量技术, 2019, 42(9):117-120 CHENG Huanxin, WANG Jianqing. Improved DBN for TE process fault diagnosis[J]. Electronic Measurement Technology, 2019, 42(9):117-120(in Chinese)
[52] YU J, YAN X. Whole process monitoring based on unstable neuron output information in hidden layers of deep belief network[J]. IEEE Transactions on Cybernetics, 2020, 50(9):3998-4007
[53] TANG Q, CHAI Y, QU J, et al. Fisher discriminative sparse representation based on DBN for fault diagnosis of complex system[J]. Applied Sciences, 2018, doi:10.3390/app8050795
[54] TANG P, PENG K, ZHANG K, et al. A deep belief network-based fault detection method for nonlinear processes[J]. IFAC-PapersOnLine, 2018, 51(24):9-14
[55] YU J, YAN X. Active features extracted by deep belief network for process monitoring[J]. ISA Transactions, 2019, 84:247-261
[56] JIANG Q, YAN X. Learning deep correlated representations for nonlinear process monitoring[J]. IEEE Transactions on Industrial Informatics, 2019, 15(12):6200-6209
[57] TIAN W, LIU Z, LI L, et al. Identification of abnormal conditions in high-dimensional chemical process based on feature selection and deep learning[J]. Chinese Journal of Chemical Engineering, 2020, 28(7):1875-1883
[58] LECUN Y, JACKEL L D, BOSER B, et al. Handwritten digit recognition:Applications of neural network chips and automatic learning[J]. IEEE Communications Magazine, 1989, 27(11):41-46
[59] KRIZHEVSKY A, SUTSKEVER I, HINTON G E. Imagenet classification with deep convolutional neural networks[J]. Advances in Neural Information Processing Systems, 2012, 25:1097-1105
[60] SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition[EB/OL]. 2014, http://arxiv.org/abs/1409.1556
[61] GAO X, YANG F, FENG E. A process fault diagnosis method using multi-time scale dynamic feature extraction based on convolutional neural network[J]. The Canadian Journal of Chemical Engineering, 2020, 98(6):1280-1292
[62] GUO S, YANG T, GAO W, et al. A novel fault diagnosis method for rotating machinery based on a convolutional neural network[J]. Sensors (Basel, Switzerland), 2018, doi:10.3390/s18051429
[63] GUO S, YANG T, GAO W, et al. An intelligent fault diagnosis method for bearings with variable rotating speed based on Pythagorean spatial pyramid pooling CNN[J]. Sensors (Basel, Switzerland), 2018, doi:10.3390/s18113857
[64] WU H, ZHAO J. Deep convolutional neural network model based chemical process fault diagnosis[J]. Computers & Chemical Engineering, 2018, 115:185-197
[65] EOM Y H, YOO J W, HONG S B, et al. Refrigerant charge fault detection method of air source heat pump system using convolutional neural network for energy saving[J]. Energy, 2019, doi:10.1016/j.energy.2019.115877
[66] LEE K B, CHEON S, KIM C O. A convolutional neural network for fault classification and diagnosis in semiconductor manufacturing processes[J]. IEEE Transactions on Semiconductor Manufacturing, 2017, 30(2):135-142
[67] 李元, 冯成成. 基于一维卷积神经网络深度学习的工业过程故障检测[J]. 测控技术, 2019, 38(9):36-40, 61 LI Yuan, FENG Chengcheng. Fault detection of industrial process based on deep learning of one-dimensional convolution neural network[J]. Measurement & Control Technology, 2019, 38(9):36-40, 61(in Chinese)
[68] SHI S, ZHAO Z. Fluidized bed agglomeration monitoring based on convolutional neural networks[C]//201837th Chinese Control Conference (CCC). Wuhan:IEEE, 2018:5973-5978
[69] HOCHREITER S, SCHMIDHUBER J. Long short-term memory[J]. Neural Computation, 1997, 9(8):1735-1780
[70] CHO K, VAN MERRIENBOER B, GULCEHRE C, et al. Learning phrase representations using RNN encoder-decoder for statistical machine translation[EB/OL]. 2014, https://arxiv.org/abs/1406.1078
[71] KANG J. Visualization analysis for fault diagnosis in chemical processes using recurrent neural networks[J]. Journal of the Taiwan Institute of Chemical Engineers, 2020, 112:137-151
[72] ZHAO H, SUN S, JIN B. Sequential fault diagnosis based on LSTM neural network[J]. IEEE Access, 2018, 6:12929-12939
[73] ZHANG S, BI K, QIU T. Bidirectional recurrent neural network-based chemical process fault diagnosis[J]. Industrial & Engineering Chemistry Research, 2020, 59(2):824-834
[74] SUN W, PAIVA A R C, XU P, et al. Fault detection and identification using Bayesian recurrent neural networks[J]. Computers & Chemical Engineering, 2020, doi:10.1016/j.compchemeng.2020.106991
[75] REN J, NI D. A batch-wise LSTM-encoder decoder network for batch process monitoring[J]. Chemical Engineering Research and Design, 2020, 164:102-112
[76] 张清清. 基于孪生LSTM的TE过程故障诊断方法研究[D]. 成都:电子科技大学, 2020 ZHANG Qingqing. Research on fault diagnosis method of TE process based on siamese LSTM[D]. Chengdu:University of Electronic Science and Technology of China, 2020(in Chinese)
[77] SHAO B, HU X, BIAN G, et al. A multichannel LSTM-CNN method for fault diagnosis of chemical process[J]. Mathematical Problems in Engineering, 2019, 2019:1-14
[78] WANG N, YANG F, ZHANG R, et al. Intelligent fault diagnosis for chemical processes using deep learning multimodel fusion[J]. IEEE Transactions on Cybernetics, 8832, PP(99):1-15
[79] XING L, YAO W, HUANG Y. Fault diagnosis of multi-sensor signal with unknown composite fault based on deep learning[J]. Chongqing Daxue Xuebao/Journal of Chongqing University, 2020, 43(9):93-100
[80] SHAHNAZARI H. Fault diagnosis of nonlinear systems using recurrent neural networks[J]. Chemical Engineering Research and Design, 2020, 153:233-245
[81] ZHANG L, LIN J, LIU B, et al. A review on deep learning applications in prognostics and health management[J]. IEEE Access, 2019, 7:162415-162438
[82] LIU H, ZHOU J, XU Y, et al. Unsupervised fault diagnosis of rolling bearings using a deep neural network based on generative adversarial networks[J]. Neurocomputing, 2018, 315:412-424
[83] LIU J, QU F, HONG X, 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
[84] WU H, ZHAO J. Fault detection and diagnosis based on transfer learning for multimode chemical processes[J]. Computers & Chemical Engineering, 2020, doi:10.1016/j.compchemeng.2020.106731
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2022, Vol. 39 
