机械工程、能源工程 |
|
|
|
|
注意力卷积GRU自编码器及其在工业过程监控的应用 |
刘兴( ),余建波*( ) |
同济大学 机械与能源工程学院,上海 201804 |
|
Attention convolutional GRU-based autoencoder and its application in industrial process monitoring |
Xing LIU( ),Jian-bo YU*( ) |
School of Mechanical Engineering, Tongji University, Shanghai 201804, China |
1 |
YIN S, YANG X, KARIMI H R Data-driven adaptive observer for fault diagnosis[J]. Mathematical Problems in Engineering, 2012, 2012: 832836
|
2 |
XIE X, SUN W, CHEUNG K An advanced PLS approach for key performance indicator-related prediction and diagnosis in case of outliers[J]. IEEE Transactions on Industrial Electronics, 2016, 63 (4): 2587- 2594
|
3 |
YANG Q, GE S S, SUN Y Adaptive actuator fault tolerant control for uncertain nonlinear systems with multiple actuators[J]. Automatica, 2015, 60: 92- 99
doi: 10.1016/j.automatica.2015.07.006
|
4 |
WAN Z, LI J, GAO Y Monitoring and diagnosis process of abnormal consumption on smart power grid[J]. Neural Computing and Applications, 2018, 30 (1): 21- 28
doi: 10.1007/s00521-016-2719-4
|
5 |
GE Z Q Review on data-driven modeling and monitoring for plant wide industrial processes[J]. Chemometrics and Intelligent Laboratory Systems, 2017, 171: 16- 25
doi: 10.1016/j.chemolab.2017.09.021
|
6 |
NOR N M, HASSAN C R C, HUSSAIN M A A review of data-driven fault detection and diagnosis methods: applications in chemical process systems[J]. Reviews in Chemical Engineering, 2019, 36 (4): 513- 553
|
7 |
JIANG Q, YAN X, HUANG B Performance-driven distributed PCA process monitoring based on fault-relevant variable selection and bayesian inference[J]. IEEE Transactions on Industrial Electronics, 2015, 63 (1): 377- 386
|
8 |
DENG X, TIAN X, CHEN S, et al Nonlinear process fault diagnosis based on serial principal component analysis[J]. IEEE Transactions on Neural Networks and Learning Systems, 2018, 29 (3): 560- 572
doi: 10.1109/TNNLS.2016.2635111
|
9 |
ZHONG B, WANG J, ZHOU J, et al Quality-related statistical process monitoring method based on global and local partial least-squares projection[J]. Industrial and Engineering Chemistry Research, 2016, 55 (6): 1609- 1622
doi: 10.1021/acs.iecr.5b02559
|
10 |
YU G Fault feature extraction using independent component analysis with reference and its application on fault diagnosis of rotating machinery[J]. Neural Computing and Applications, 2015, 26 (1): 187- 198
doi: 10.1007/s00521-014-1726-6
|
11 |
ZHONG S, WEN Q, GE Z Semi-supervised Fisher discriminant analysis model for fault classification in industrial processes[J]. Chemometrics and Intelligent Laboratory Systems, 2014, 138: 203- 211
doi: 10.1016/j.chemolab.2014.08.008
|
12 |
LEE J M, YOO C K, CHOI S W, et al Nonlinear process monitoring using kernel principal component analysis[J]. Chemical Engineering Science, 2004, 59 (1): 223- 234
doi: 10.1016/j.ces.2003.09.012
|
13 |
JIANG Q, YAN X Parallel PCA–KPCA for nonlinear process monitoring[J]. Control Engineering Practice, 2018, 80 (9): 17- 25
|
14 |
DEWI Y N, RIANA D, MANTORO T. Improving nave bayes performance in single image pap smear using weighted principal component analysis (WPCA) [C]// 2017 International Conference on Computing, Engineering, and Design (ICCED). Jakarta: IEEE, 2018: 1-5
|
15 |
WANG K, JUNG H C, ZHI S Performance analysis of dynamic PCA for closed-loop process monitoring and its improvement by output oversampling scheme[J]. IEEE Transactions on Control Systems and Technology, 2019, 27 (1): 378- 85
doi: 10.1109/TCST.2017.2765621
|
16 |
HEO S, LEE J H Fault detection and classification using artificial neural networks[J]. IFAC-PapersOnLine, 2018, 51 (18): 470- 475
doi: 10.1016/j.ifacol.2018.09.380
|
17 |
YANG C, HOU J Fed-batch fermentation penicillin process fault diagnosis and detection based on support vector machine[J]. Neurocomputing, 2016, 190 (19): 117- 123
|
18 |
LI Y, LIU Y, ZHANG C Discriminant diffusion maps based K-nearest-neighbour for batch process fault detection [J]. Canadian Journal of Chemical Engineering, 2018, 96 (2): 484- 496
doi: 10.1002/cjce.23003
|
19 |
KIM Y, KIM S B Optimal false alarm-controlled support vector data description for multivariate process monitoring[J]. Journal of Process Control, 2017, 65: 1- 14
|
20 |
黄健, 杨旭 基于在线加权慢特征分析的故障检测算法[J]. 上海交通大学学报, 2020, 54 (11): 1142- 1150 HUANG Jian, YANG Xu Online Weighted Based Slow Feature Analysis Fault Detection Algorithm[J]. Journal of Shanghai Jiao Tong University, 2020, 54 (11): 1142- 1150
|
21 |
HINTON, G Reducing the dimensionality of data with neural networks[J]. Science, 2006, 313 (5786): 504- 507
doi: 10.1126/science.1127647
|
22 |
LECUN Y, BENGIO Y, HINTON G Deep learning[J]. Nature, 2015, 521 (7553): 436- 444
doi: 10.1038/nature14539
|
23 |
LIU Y, FAN Y, CHEN J Flame images for oxygen content prediction of combustion systems using DBN[J]. Energy Fuel, 2017, 31 (8): 8776- 8783
doi: 10.1021/acs.energyfuels.7b00576
|
24 |
XUAN Q, FANG BW, LIU Y, et al Automatic pearl classification machine based on a multistream convolutional neural network[J]. IEEE Transactions on Industrial Electronics, 2018, 65 (8): 6538- 6547
doi: 10.1109/TIE.2017.2784394
|
25 |
KRIZHEVSKY A, SUTSKEVER I, HINTON G ImageNet classification with deep convolutional neural networks[J]. Communications of the ACM, 2017, 60 (6): 84- 90
doi: 10.1145/3065386
|
26 |
ZHANG X Y, YIN F, ZHANG Y M, et al Drawing and recognizing chinese characters with recurrent neural network[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, 40 (4): 849- 862
doi: 10.1109/TPAMI.2017.2695539
|
27 |
LAULY S, LAROCHELLE H, KHAPRA M, et al An autoencoder approach to learning bilingual word representations[J]. Advances in Neural Information Processing Systems, 2014, 3: 1853- 1861
|
28 |
ZHANG Z P, ZHAO J S A deep belief network based fault diagnosis model for complex chemical processes[J]. Computers and Chemical Engineering, 2017, 107: 395- 407
doi: 10.1016/j.compchemeng.2017.02.041
|
29 |
TANG P, PENG K, ZHANG K A deep belief network-based fault detection method for nonlinear processes[J]. IFAC-PapersOnLine, 2018, 51 (24): 9- 14
doi: 10.1016/j.ifacol.2018.09.522
|
30 |
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 LI Yuan, FENG Cheng-cheng Fault detection of industrial process based on deep learning of one-dimensional convolution neural network[J]. Measurement and Control Technology, 2019, 38 (9): 36- 40
|
31 |
WU H, ZHAO J S Deep convolutional neural network model based chemical process fault diagnosis[J]. Computers and Chemical Engineering, 2018, 115: 185- 197
doi: 10.1016/j.compchemeng.2018.04.009
|
32 |
ZHANG Z H, TENG J, LI S H, 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
doi: 10.1016/j.jprocont.2018.02.004
|
33 |
CHO K, MERRIENBOER B V, GULCEHRE C, et al. Learning phrase representations using RNN encoder-decoder for statistical machine translation [C]// Proceeding of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). Doha: [s.n.], 2014: 1724-1734.
|
34 |
HOCHREITER S, SCHMIDHUBER J Long short-term memory[J]. Neural Computation, 1997, 9 (8): 1735- 1780
doi: 10.1162/neco.1997.9.8.1735
|
35 |
SHI X, GAO Z, LAUSEN L. Deep learning for precipitation nowcasting: a benchmark and a new model [C]// Proceedings of the 31st International Conference on Neural Information Processing Systems. Long Beach: [s.n.], 2017: 5617-5627.
|
36 |
MNIH V, HEESS N, GRAVES A. Recurrent models of visual attention [EB/OL]. [2020-07-24]. http://arxiv. org/pdf/1406.6247.pdf.
|
37 |
CHEN Q, WYNNE R J, GOULDING P, et al The application of principal component analysis and kernel density estimation to enhance process monitoring[J]. Control Engineering Practice, 2000, 8 (5): 531- 543
doi: 10.1016/S0967-0661(99)00191-4
|
38 |
LAURENS V D M, HINTON G Visualizing data using t-SNE[J]. Journal of Machine Learning Research, 2008, 9 (86): 2579- 2605
|
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
|
Shared |
|
|
|
|
|
Discussed |
|
|
|
|