Deep convolutional neural network model based chemical process fault diagnosis

Ayubi Rad, 2015, Designing supervised local neural network classifiers based on EM clustering for fault diagnosis of Tennessee Eastman process, Chemom. Intell. Lab. Syst., 146, 149, 10.1016/j.chemolab.2015.05.013

Bathelt, 2015, Revision of the Tennessee eastman process model, IFAC PapersOnLine, 309, 10.1016/j.ifacol.2015.08.199

Bouvrie, 2006, Notes on convolutional neural networks, In Pract., 47

Chiang, 2000, Fault diagnosis in chemical processes using Fisher discriminant analysis, discriminant partial least squares, and principal component analysis, Chemom. Intell. Lab. Syst., 50, 243, 10.1016/S0169-7439(99)00061-1

Chiang, 2004, Fault diagnosis based on Fisher discriminant analysis and support vector machines, Comput. Chem. Eng., 28, 1389, 10.1016/j.compchemeng.2003.10.002

Cho, 2005, Fault identification for process monitoring using kernel principal component analysis, Chem. Eng. Sci., 60, 279, 10.1016/j.ces.2004.08.007

Choi, 2004, Process monitoring using a Gaussian mixture model via principal component analysis and discriminant analysis, Comput. Chem. Eng., 28, 1377, 10.1016/j.compchemeng.2003.09.031

Dai, 2011, Fault diagnosis of batch chemical processes using a dynamic time warping (DTW)-based artificial immune system, Ind. Eng. Chem. Res., 50, 4534, 10.1021/ie101465b

Downs, 1993, A plant-wide industrial process control problem, Comput. Chem. Eng., 17, 245, 10.1016/0098-1354(93)80018-I

Eslamloueyan, 2003, Multiple simultaneous fault diagnosis via hierarchical and single artificial neural networks, Sci. Iran, 10

Eslamloueyan, 2011, Designing a hierarchical neural network based on fuzzy clustering for fault diagnosis of the Tennessee–Eastman process, Appl. Soft Comput., 11, 1407, 10.1016/j.asoc.2010.04.012

Fan, 1993, An approach to fault diagnosis of chemical processes via neural networks, AIChE J, 39, 82, 10.1002/aic.690390109

Fan, 2014, Fault detection and diagnosis of non-linear non-Gaussian dynamic processes using kernel dynamic independent component analysis, Inf. Sci., 259, 369, 10.1016/j.ins.2013.06.021

Ge, 2012, Local ICA for multivariate statistical fault diagnosis in systems with unknown signal and error distributions, AIChE J, 58, 2357, 10.1002/aic.12760

Ghosh, 2011, Immune-system-inspired approach to process monitoring and fault diagnosis. ind. eng. chem, Res, 50, 1637

He, 2016, Deep residual learning for image recognition, 770

He, 2007, Fault detection using the k-nearest neighbor rule for semiconductor manufacturing processes, IEEE Trans. Semicond. Manuf., 20, 345, 10.1109/TSM.2007.907607

Hinton, 2002, Stochastic neighbor embedding, Adv. Neural Inf. Process. Syst., 833

Hinton, 2006, Reducing the dimensionality of data using neural networks, Science, 313, 504, 10.1126/science.1127647

Hinton, 2006, A fast learning algorithm for deep belief nets, Neural Comput, 18, 1527, 10.1162/neco.2006.18.7.1527

Hinton, G.E., Srivastava, N., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.R., 2012. Improving neural networks by preventing co-adaptation of feature detectors. arXiv Prepr. arXiv:1207.0580.

Hsu, 2010, A novel process monitoring approach with dynamic independent component analysis, Control Eng. Pract., 18, 242, 10.1016/j.conengprac.2009.11.002

Kano, 2003, Monitoring independent components for fault detection, AIChE J., 49, 969, 10.1002/aic.690490414

Krizhevsky, 2012, ImageNet classification with deep convolutional neural networks, Adv. Neural Inf. Process. Syst., 1

Kulkarni, 2005, Knowledge incorporated support vector machines to detect faults in Tennessee Eastman process, Comput. Chem. Eng., 29, 2128, 10.1016/j.compchemeng.2005.06.006

LeCun, 1989, Handwritten digit recognition: applications of neural network chips and automatic learning, IEEE Commun. Mag., 27, 41, 10.1109/35.41400

LeCun, 2015, Deep learning, Nature, 521, 436, 10.1038/nature14539

Lee, 2007, Fault detection of non-linear processes using kernel independent component analysis, Can. J. Chem. Eng., 85, 526, 10.1002/cjce.5450850414

Lin, M., Chen, Q., Yan, S., 2013. Network in network. arXiv Prepr. 10. doi:10.1109/ASRU.2015.7404828.

Lv, 2016, Fault diagnosis based on deep learning, 6851

Maaten, 2008, Visualizing data using t-SNE, J. Mach. Learn. Res., 1, 267

Maurya, 2005, Fault diagnosis by qualitative trend analysis of the principal components, Chem. Eng. Res. Des., 83, 1122, 10.1205/cherd.04280

Maurya, 2007, A signed directed graph and qualitative trend analysis-based framework for incipient fault diagnosis, Chem. Eng. Res. Des., 85, 1407, 10.1016/S0263-8762(07)73181-7

Maurya, 2010, A framework for on-line trend extraction and fault diagnosis, Eng. Appl. Artif. Intell., 23, 950, 10.1016/j.engappai.2010.01.027

MacGregor, 1994, Process monitoring and diagnosis by multiblock PLS methods, AIChE J, 40, 826, 10.1002/aic.690400509

Mahadevan, 2009, Fault detection and diagnosis in process data using one-class support vector machines, J. Process Control, 19, 1627, 10.1016/j.jprocont.2009.07.011

Plovoso, 1994, Applications of multivariate statistical methods to process monitoring and controller design, Int. J. Control, 59, 743, 10.1080/00207179408923103

Rato, 2016, A systematic comparison of PCA-based statistical process monitoring methods for high-dimensional, time-dependent processes, AIChE J., 62, 1478, 10.1002/aic.15062

Rato, 2017, Markovian and non-Markovian sensitivity enhancing transformations for process monitoring, Chem. Eng. Sci., 163, 223, 10.1016/j.ces.2017.01.047

Russell, 2000, Fault detection in industrial processes using canonical variate analysis and dynamic principal component analysis, Chemom. Intell. Lab. Syst., 51, 81, 10.1016/S0169-7439(00)00058-7

Shu, 2016, Fault diagnosis of chemical processes using artificial immune system with vaccine transplant, Ind. Eng. Chem. Res., 55, 3360, 10.1021/acs.iecr.5b02646

Simonyan, 2014, Very deep convolutional networks for large-scale image recognition, CoRR, 1

Srivastava, 2014, Dropout: a simple way to prevent neural networks from overfitting, J. Mach. Learn. Res., 15, 1929

Szegedy, C., Liu, W., Jia, Y., Sermanet, P., 2014. Going deeper with convolutions. arXiv Prepr. arXiv:1409.4842 1–9. doi:10.1109/CVPR.2015.7298594.

Venkatasubramanian, 1989, A neural network methodology for process fault diagnosis, AIChE J., 35, 1993, 10.1002/aic.690351210

Venkatasubramanian, 2003, A review of process fault detection and diagnosis part II : qualitative models and search strategies, Comput. Chem. Eng., 27, 313, 10.1016/S0098-1354(02)00161-8

Venkatasubramanian, 2003, A review of process fault detection and diagnosis part III: process history based methods, Comput. Chem. Eng., 10.1016/S0098-1354(02)00161-8

Venkatasubramanian, 2003, A review of process fault detection and diagnosis part I: quantitative model based methods, Comput. Chem. Eng., 27, 293, 10.1016/S0098-1354(02)00160-6

Watanabe, 1989, Incipient fault diagnosis of chemical processes via artificial neural networks, AIChE J., 35, 1803, 10.1002/aic.690351106

Watanabe, 1994, Diagnosis of multiple simultaneous fault via hierarchical artificial neural networks, AIChE J., 40, 839, 10.1002/aic.690400510

Wise, 1990, Theoretical basis for the use of principal component models for monitoring multivariate processes, Process Control Qual., 1, 41

Xie, 2015, A hierarchical deep neural network for fault diagnosis on Tennessee–Eastman process

Yélamos, 2009, Performance assessment of a novel fault diagnosis system based on support vector machines, Comput. Chem. Eng., 33, 244, 10.1016/j.compchemeng.2008.08.008

Yin, 2012, A comparison study of basic data-driven fault diagnosis and process monitoring methods on the benchmark Tennessee Eastman process, J. Process Control, 22, 1567, 10.1016/j.jprocont.2012.06.009

Yu, 2008, Multimode process monitoring with bayesian inference-based finite Gaussian mixture models, AIChE J., 54, 1811, 10.1002/aic.11515

Zhang, 2008, Fault detection and diagnosis of nonlinear processes using improved kernel independent component analysis (KICA) and support vector machine (SVM), Ind. Eng. Chem. Res., 47, 6961, 10.1021/ie071496x

Zhang, 2011, Multivariate process monitoring and analysis based on multi-scale KPLS, Chem. Eng. Res. Des., 89, 2667, 10.1016/j.cherd.2011.05.005

Zhang, 2017, A deep belief network based fault diagnosis model for complex chemical processes, Comput. Chem. Eng., 10.1016/j.compchemeng.2017.02.041

Zhu, 2011, A novel fault diagnosis system using pattern classification on kernel FDA subspace, Expert Syst. Appl., 38, 6895, 10.1016/j.eswa.2010.12.034