Fault diagnosis for distillation process based on CNN–DAE

Abstract 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%.

[1]  Dongil Shin,et al.  Deep neural network and random forest classifier for source tracking of chemical leaks using fence monitoring data , 2018, Journal of Loss Prevention in the Process Industries.

[2]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[3]  Thomas Hofmann,et al.  Greedy Layer-Wise Training of Deep Networks , 2007 .

[4]  Liang Gao,et al.  A new data-driven intelligent fault diagnosis by using convolutional neural network , 2017, 2017 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM).

[5]  Jianjun Hu,et al.  An Ensemble Deep Convolutional Neural Network Model with Improved D-S Evidence Fusion for Bearing Fault Diagnosis , 2017, Sensors.

[6]  Haidong Shao,et al.  A novel deep autoencoder feature learning method for rotating machinery fault diagnosis , 2017 .

[7]  N. Rajeswaran,et al.  Hybrid Artificial Intelligence based Fault Diagnosis of SVPWM Voltage Source Inverters for Induction Motor , 2018 .

[8]  Hao Wu,et al.  Deep convolutional neural network model based chemical process fault diagnosis , 2018, Comput. Chem. Eng..

[9]  Wei Liu,et al.  Face recognition based on convolution neural network , 2017, 2017 36th Chinese Control Conference (CCC).

[10]  Ruqiang Yan,et al.  A sparse auto-encoder-based deep neural network approach for induction motor faults classification , 2016 .

[11]  Venkat Venkatasubramanian,et al.  Challenges in the industrial applications of fault diagnostic systems , 2000 .

[12]  Qunxiong Zhu,et al.  Study and Application of Fault Prediction Methods with Improved Reservoir Neural Networks , 2014 .

[13]  Bart De Ketelaere,et al.  A systematic comparison of PCA-based statistical process monitoring methods for high-dimensional, time-dependent processes , 2016 .

[14]  Yoshua. Bengio,et al.  Learning Deep Architectures for AI , 2007, Found. Trends Mach. Learn..

[15]  Yoshua Bengio,et al.  Extracting and composing robust features with denoising autoencoders , 2008, ICML '08.

[16]  Zhiqiang Ge,et al.  Semi-supervised fault classification based on dynamic Sparse Stacked auto-encoders model , 2017 .

[17]  Xiaogang Wang,et al.  Deeply learned face representations are sparse, selective, and robust , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[18]  Jun Cai,et al.  Multi-fault classification based on support vector machine trained by chaos particle swarm optimization , 2010, Knowl. Based Syst..

[19]  Hongkai Jiang,et al.  An adaptive deep convolutional neural network for rolling bearing fault diagnosis , 2017 .

[20]  Feng Qian,et al.  High dimension feature extraction based visualized SOM fault diagnosis method and its application in p-xylene oxidation process , 2015 .

[21]  Punyaphol Horata,et al.  Handwritten Character Recognition Using Histograms of Oriented Gradient Features in Deep Learning of Artificial Neural Network , 2013, 2013 International Conference on IT Convergence and Security (ICITCS).

[22]  Yingwei Zhang,et al.  Multivariate process monitoring and analysis based on multi-scale KPLS , 2011 .

[23]  Yee Whye Teh,et al.  A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.

[24]  Florian Metze,et al.  Extracting deep bottleneck features using stacked auto-encoders , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[25]  Hongbo Shi,et al.  Hidden Markov Model-Based Fault Detection Approach for a Multimode Process , 2016 .

[26]  Raghunathan Rengaswamy,et al.  A review of process fault detection and diagnosis: Part III: Process history based methods , 2003, Comput. Chem. Eng..

[27]  Xiaogang Wang,et al.  Deep Learning Face Representation by Joint Identification-Verification , 2014, NIPS.

[28]  Zhi-Huan Song,et al.  A novel fault diagnosis system using pattern classification on kernel FDA subspace , 2011, Expert Syst. Appl..

[29]  Ming Zhao,et al.  A convolutional neural network based feature learning and fault diagnosis method for the condition monitoring of gearbox , 2017 .

[30]  Rob Fergus,et al.  Stochastic Pooling for Regularization of Deep Convolutional Neural Networks , 2013, ICLR.

[31]  Geoffrey E. Hinton,et al.  Reducing the Dimensionality of Data with Neural Networks , 2006, Science.

[32]  Lawrence D. Jackel,et al.  Backpropagation Applied to Handwritten Zip Code Recognition , 1989, Neural Computation.

[33]  Maria J. Fuente,et al.  Fault detection based on neural networks and independent component analysis , 2014, Proceedings of the 2014 IEEE Emerging Technology and Factory Automation (ETFA).

[34]  Liu Jun,et al.  A review of object detection based on convolutional neural network , 2017, 2017 36th Chinese Control Conference (CCC).

[35]  Jicong Fan,et al.  Fault detection and diagnosis of non-linear non-Gaussian dynamic processes using kernel dynamic independent component analysis , 2014, Inf. Sci..

[36]  Faisal Khan,et al.  Root Cause Diagnosis of Process Fault Using KPCA and Bayesian Network , 2017 .