Fault diagnosis for distillation process based on CNN–DAE
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摘要
Distillation is the most widely used operation for liquid mixture separation in the chemical industry. It is of great importance to detect and diagnose faults in distillation process. Due to the strong feedback and coupling of processes in a distillation column, it is difficult to use deep auto-encoders(DAEs) alone to achieve good results in detecting and diagnosing faults, in terms of accuracy and efficiency. This paper proposes a hybrid fault-diagnosis model based on convolutional neural networks(CNNs) and DAEs, by integrating the powerful capability of CNN in feature extraction and of DAE in classification. A case study was carried out with the distillation process of depropanization. It is shown that the proposed hybrid model is of good performance compared to other models, in terms of the accuracy of fault detection in such a process. Also, with the increase of structural layers of the CNN–DAE model, the diagnostic accuracy will be improved, with an optimal accuracy of 92.2%.
Distillation is the most widely used operation for liquid mixture separation in the chemical industry. It is of great importance to detect and diagnose faults in distillation process. Due to the strong feedback and coupling of processes in a distillation column, it is difficult to use deep auto-encoders(DAEs) alone to achieve good results in detecting and diagnosing faults, in terms of accuracy and efficiency. This paper proposes a hybrid fault-diagnosis model based on convolutional neural networks(CNNs) and DAEs, by integrating the powerful capability of CNN in feature extraction and of DAE in classification. A case study was carried out with the distillation process of depropanization. It is shown that the proposed hybrid model is of good performance compared to other models, in terms of the accuracy of fault detection in such a process. Also, with the increase of structural layers of the CNN–DAE model, the diagnostic accuracy will be improved, with an optimal accuracy of 92.2%.
Distillation is the most widely used operation for liquid mixture separation in the chemical industry. It is of great importance to detect and diagnose faults in distillation process. Due to the strong feedback and coupling of processes in a distillation column, it is difficult to use deep auto-encoders(DAEs) alone to achieve good results in detecting and diagnosing faults, in terms of accuracy and efficiency. This paper proposes a hybrid fault-diagnosis model based on convolutional neural networks(CNNs) and DAEs, by integrating the powerful capability of CNN in feature extraction and of DAE in classification. A case study was carried out with the distillation process of depropanization. It is shown that the proposed hybrid model is of good performance compared to other models, in terms of the accuracy of fault detection in such a process. Also, with the increase of structural layers of the CNN–DAE model, the diagnostic accuracy will be improved, with an optimal accuracy of 92.2%.
引文
[1] Jiang Yi, Optimum Design of Distillation Column With Mixed Integer Spade Particle Swarm Optimization Algorithm, Ph D Thesis, Qingdao University of Science&Technology, China, 2015(in Chinese).
[2] S. Dash, V. Venkatasubramanian, Challenges in the industrial applications of fault diagnostic systems, Comput. Chem. Eng. 24(2–7)(2000)785–791.
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[7] Z.B. Zhu, Z.H. Song, A novel fault diagnosis system using pattern classi?cation on kernel FDA subspace, Expert Syst. Appl. 38(6)(2011)6895–6905.
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[9] Y. Tian, W. Du, F. Qian, High dimension feature extraction based visualized SOM fault diagnosis method and its application in p-xylene oxidation process, Chin. J.Chem. Eng. 23(9)(2015)1509–1517.
[10] Q. Zhu, Y. Jia, D. Peng, et al., Study and application of fault prediction methods with improved reservoir neural networks, Chin. J. Chem. Eng. 22(7)(2014)812–819.
[11] X. Tang, L. Zhuang, J. Cai, et al., Multi-fault classi?cation based on support vector machine trained by chaos particle swarm optimization, Knowl.-Based Syst. 23(5)(2010)486–490.
[12] M.J. Fuente, G.I. Sainz-Palmero, Fault detection based on neural networks and independent component analysis, Emerging Technology and Factory Automation(ETFA), IEEE 2014, pp. 1–6.
[13] H. Gharahbagheri, S.A. Imtiaz, F. Khan, Root cause diagnosis of process fault using KPCA and Bayesian network, Ind. Eng. Chem. Res. 56(8)(2017)2054–2070.
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[17] G.E. Hinton, S. Osindero, Y.W. Teh, A fast learning algorithm for deep belief nets,Neural Comput. 18(7)(2006)1527–1554.
[18] Y. Bengio, Learning deep architectures for AI, Found. Trends Mach. Learn. 2(1)(2009)1–127.
[19] P. Vincent, H. Larochelle, Y. Bengio, et al., Extracting and composing robust features with denoising autoencoders, International Conference on Machine Learning, ACM2008, pp. 1096–1103.
[20] B. Sch?lkopf, J. Platt, T. Hofmann, Greedy layer-wise training of deep networks, International Conference on Neural Information Processing Systems, MIT Press 2006,pp. 153–160.
[21] N. Zhang, X.M. Tian, L.F. Cai, Nonlinear dynamic fault diagnosis method based on DAutoencoder, Fifth International Conference on Measuring Technology and Mechatronics Automation, IEEE 2013, pp. 729–732.
[22] W. Sun, S. Shao, R. Zhao, et al., A sparse auto-encoder-based deep neural network approach for induction motor faults classi?cation, Measurement 89(ISFA)(2016)171–178.
[23] H. Shao, H. Jiang, H. Zhao, et al., A novel deep autoencoder feature learning method for rotating machinery fault diagnosis, Mech. Syst. Signal Process. 95(2017)187–204.
[24] L. Jiang, Z. Ge, Z. Song, Semi-supervised fault classi?cation based on dynamic Sparse Stacked auto-encoders model, Chemom. Intell. Lab. Syst. 168(2017).
[25] Y. LeCun, B. Boser, J.S. Denker, et al., Backpropagation applied to handwritten zip code recognition, Neural Comput. 1(4)(1989)541–551.
[26] A. Krizhevsky, I. Sutskever, G.E. Hinton, ImageNet classi?cation with deep convolutional neural networks, Commun. ACM 60(2)(2012)2012.
[27] Y. Sun, X. Wang, X. Tang, Deep learning face representation by joint identi?cation–veri?cation, Adv. Neural Inf. Proces. Syst. 27(2014)1988–1996.
[28] Y. Sun, X. Wang, X. Tang, Deeply learned face representations are sparse, selective,and robust, Computer Vision and Pattern Recognition, IEEE 2015, pp. 2892–2900.
[29] L. Jing, M. Zhao, P. Li, et al., A convolutional neural network based feature learning and fault diagnosis method for the condition monitoring of gearbox, Meas. J. Int.Meas. Confed. 111(2017)1–10.
[30] S. Li, G. Liu, X. Tang, et al., An ensemble deep convolutional neural network model with improved D-S evidence fusion for bearing fault diagnosis, Sensors 17(8)(2017)1729.
[31] W. Fuan, J. Hongkai, S. Haidong, et al., An adaptive deep convolutional neural network for rolling bearing fault diagnosis, Meas. Sci. Technol. 28(9)(2017).
[32] L. Wen, L. Gao, X. Li, et al., A new data-driven intelligent fault diagnosis by using convolutional neural network, IEEE International Conference on Industrial Engineering&Engineering Management, 2018.
[33] H. Wu, J. Zhao, Deep convolutional neural network model based chemical process fault diagnosis, Comput. Chem. Eng. 115(2018)185–197.
[34] K. Yan, S. Huang, Y. Song, et al., Face recognition based on convolution neural network, Control Conference(CCC), 36th Chinese, IEEE 2017, pp. 4077–4081.
[35] Z. Wang, J. Liu, A review of object detection based on convolutional neural network,Control Conference(CCC), 36th Chinese, IEEE 2017, pp. 11104–11109.
[36] M.D. Zeiler, R. Fergus, Stochastic Pooling for Regularization of Deep Convolutional Neural Networks, arXiv preprint arXiv:1301.3557 2013.
[37] J. Gehring, Y. Miao, F. Metze, et al., Extracting deep bottleneck features using stacked auto-encoders, IEEE International Conference on Acoustics, Speech and Signal Processing, IEEE 2013, pp. 3377–3381.
[38] S. Iamsa-at, P. Horata, Handwritten character recognition using histograms of oriented gradient features in deep learning of arti?cial neural network, IT Convergence and Security(ICITCS), 2013 International Conference on, IEEE 2013, pp. 1–5.
[2] S. Dash, V. Venkatasubramanian, Challenges in the industrial applications of fault diagnostic systems, Comput. Chem. Eng. 24(2–7)(2000)785–791.
[3] V. Venkatasubramanian, R. Rengaswamy, S. Kavuri, K. Yin, A review of process fault detection and diagnosis(part III):Process history based methods, Comput. Chem.Eng. 27(3)(2003)327–346.
[4] T. Rato, M. Reis, E. Schmitt, et al., A systematic comparison of PCA-based Statistical Process Monitoring methods for high-dimensional, time-dependent processes,AIChE J. 62(5)(2016)1478–1493.
[5] Y. Zhang, Z. Hu, Multivariate process monitoring and analysis based on multi-scale KPLS, Chem. Eng. Res. Des. 89(12)(2011)2667–2678.
[6] J. Fan, Y. Wang, Fault detection and diagnosis of non-linear non-Gaussian dynamic processes using kernel dynamic independent component analysis, Inf. Sci. 259(2014)369–379.
[7] Z.B. Zhu, Z.H. Song, A novel fault diagnosis system using pattern classi?cation on kernel FDA subspace, Expert Syst. Appl. 38(6)(2011)6895–6905.
[8] F. Wang, S. Tan, Y. Yang, et al., Hidden Markov model-based fault detection approach for a multimode process, Ind. Eng. Chem. Res. 55(16)(2016)4613–4621.
[9] Y. Tian, W. Du, F. Qian, High dimension feature extraction based visualized SOM fault diagnosis method and its application in p-xylene oxidation process, Chin. J.Chem. Eng. 23(9)(2015)1509–1517.
[10] Q. Zhu, Y. Jia, D. Peng, et al., Study and application of fault prediction methods with improved reservoir neural networks, Chin. J. Chem. Eng. 22(7)(2014)812–819.
[11] X. Tang, L. Zhuang, J. Cai, et al., Multi-fault classi?cation based on support vector machine trained by chaos particle swarm optimization, Knowl.-Based Syst. 23(5)(2010)486–490.
[12] M.J. Fuente, G.I. Sainz-Palmero, Fault detection based on neural networks and independent component analysis, Emerging Technology and Factory Automation(ETFA), IEEE 2014, pp. 1–6.
[13] H. Gharahbagheri, S.A. Imtiaz, F. Khan, Root cause diagnosis of process fault using KPCA and Bayesian network, Ind. Eng. Chem. Res. 56(8)(2017)2054–2070.
[14] J. Cho, H. Kim, A.L. Gebreselassie, et al., Deep neural network and random forest classi?er for source tracking of chemical leaks using fence monitoring data, J. Loss Prev.Process Ind. 56(2018)548–558.
[15] N. Rajeswaran, M. Lakshmi Swarupa, T. Sanjeeva Rao, et al., Hybrid arti?cial intelligence based fault diagnosis of SVPWM voltage source inverters for induction motor, Mater. Today Proc. 5(1)(2018)565–571.
[16] G.E. Hinton, R.R. Salakhutdinov, Reducing the dimensionality of data with neural networks, Science 313(5786)(2006)504–507.
[17] G.E. Hinton, S. Osindero, Y.W. Teh, A fast learning algorithm for deep belief nets,Neural Comput. 18(7)(2006)1527–1554.
[18] Y. Bengio, Learning deep architectures for AI, Found. Trends Mach. Learn. 2(1)(2009)1–127.
[19] P. Vincent, H. Larochelle, Y. Bengio, et al., Extracting and composing robust features with denoising autoencoders, International Conference on Machine Learning, ACM2008, pp. 1096–1103.
[20] B. Sch?lkopf, J. Platt, T. Hofmann, Greedy layer-wise training of deep networks, International Conference on Neural Information Processing Systems, MIT Press 2006,pp. 153–160.
[21] N. Zhang, X.M. Tian, L.F. Cai, Nonlinear dynamic fault diagnosis method based on DAutoencoder, Fifth International Conference on Measuring Technology and Mechatronics Automation, IEEE 2013, pp. 729–732.
[22] W. Sun, S. Shao, R. Zhao, et al., A sparse auto-encoder-based deep neural network approach for induction motor faults classi?cation, Measurement 89(ISFA)(2016)171–178.
[23] H. Shao, H. Jiang, H. Zhao, et al., A novel deep autoencoder feature learning method for rotating machinery fault diagnosis, Mech. Syst. Signal Process. 95(2017)187–204.
[24] L. Jiang, Z. Ge, Z. Song, Semi-supervised fault classi?cation based on dynamic Sparse Stacked auto-encoders model, Chemom. Intell. Lab. Syst. 168(2017).
[25] Y. LeCun, B. Boser, J.S. Denker, et al., Backpropagation applied to handwritten zip code recognition, Neural Comput. 1(4)(1989)541–551.
[26] A. Krizhevsky, I. Sutskever, G.E. Hinton, ImageNet classi?cation with deep convolutional neural networks, Commun. ACM 60(2)(2012)2012.
[27] Y. Sun, X. Wang, X. Tang, Deep learning face representation by joint identi?cation–veri?cation, Adv. Neural Inf. Proces. Syst. 27(2014)1988–1996.
[28] Y. Sun, X. Wang, X. Tang, Deeply learned face representations are sparse, selective,and robust, Computer Vision and Pattern Recognition, IEEE 2015, pp. 2892–2900.
[29] L. Jing, M. Zhao, P. Li, et al., A convolutional neural network based feature learning and fault diagnosis method for the condition monitoring of gearbox, Meas. J. Int.Meas. Confed. 111(2017)1–10.
[30] S. Li, G. Liu, X. Tang, et al., An ensemble deep convolutional neural network model with improved D-S evidence fusion for bearing fault diagnosis, Sensors 17(8)(2017)1729.
[31] W. Fuan, J. Hongkai, S. Haidong, et al., An adaptive deep convolutional neural network for rolling bearing fault diagnosis, Meas. Sci. Technol. 28(9)(2017).
[32] L. Wen, L. Gao, X. Li, et al., A new data-driven intelligent fault diagnosis by using convolutional neural network, IEEE International Conference on Industrial Engineering&Engineering Management, 2018.
[33] H. Wu, J. Zhao, Deep convolutional neural network model based chemical process fault diagnosis, Comput. Chem. Eng. 115(2018)185–197.
[34] K. Yan, S. Huang, Y. Song, et al., Face recognition based on convolution neural network, Control Conference(CCC), 36th Chinese, IEEE 2017, pp. 4077–4081.
[35] Z. Wang, J. Liu, A review of object detection based on convolutional neural network,Control Conference(CCC), 36th Chinese, IEEE 2017, pp. 11104–11109.
[36] M.D. Zeiler, R. Fergus, Stochastic Pooling for Regularization of Deep Convolutional Neural Networks, arXiv preprint arXiv:1301.3557 2013.
[37] J. Gehring, Y. Miao, F. Metze, et al., Extracting deep bottleneck features using stacked auto-encoders, IEEE International Conference on Acoustics, Speech and Signal Processing, IEEE 2013, pp. 3377–3381.
[38] S. Iamsa-at, P. Horata, Handwritten character recognition using histograms of oriented gradient features in deep learning of arti?cial neural network, IT Convergence and Security(ICITCS), 2013 International Conference on, IEEE 2013, pp. 1–5.