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Research Article

One-dimensional residual convolutional auto-encoder for fault detection in complex industrial processes

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Pages 5655-5674 | Received 08 May 2020, Accepted 29 Jul 2021, Published online: 09 Sep 2021

References

  • Bengio, Y., A. Courville, and P. Vincent. 2013. “Representation Learning: A Review and New Perspectives.” IEEE Transactions on Pattern Analysis and Machine Intelligence 35 (8): 1798–1828.
  • Butte, V. K., and L. C. Tang. 2010. “Multivariate Charting Techniques: A Review and a Line-column Approach.” Quality and Reliability Engineering International 26: 443–451.
  • Chen, J., and C. M. Liao. 2002. “Dynamic Process Fault Monitoring Based on Neural Network and PCA.” Journal of Process Control 12 (2): 277–289.
  • Chen, Q., R. J. Wynne, P. Goulding, and D. Sandoz. 2000. “The Application of Principal Component Analysis and Kernel Density Estimation to Enhance Process Monitoring.” Control Engineering Practice 8 (5): 531–543.
  • Chen, S., J. Yu, and S. Wang. 2020. “One-dimensional Convolutional Auto-Encoder-based Feature Learning for Fault Diagnosis of Multivariate Processes.” Journal of Process Control 87: 54–67.
  • Ciresan, D. C., U. Meier, J. Masci, L. M. Gambardella, and J. Schmidhuber. 2011. “Flexible, High Performance Convolutional Neural Networks for Image Classification.” Twenty-Second International Joint Conference on Artificial Intelligence, Barcelona, July, 1237–1242.
  • Du, B., S. Member, W. Xiong, S. Member, and J. Wu. 2017. “Stacked Convolutional Denoising Auto-encoders for Feature Representation.” IEEE Transactions on Cybernetics 47 (4): 1017–1027.
  • Garcia-Bracamonte, J.E., J.M. Ramirez-Cortes, J. de Jesus Rangel-Magdaleno, P. Gomez-Gil, H. Peregrina-Barreto and V. Alarcon-Aquino 2019. “An Approach on MCSA-based Fault Detection Using Independent Component Analysis and Neural Networks.” IEEE Transactions on Instrumentation and Measurement 68 (5): 1353–1361.
  • Ge, Z., Z. Song, and F. Gao. 2013. “Review of Recent Research on Data-based Process Monitoring.” Industrial & Engineering Chemistry Research 52 (10): 3543–3562.
  • Gülnur, B., Cenk ündey, and A. Inar. 2002. “A Modular Simulation Package for Fed-batch Fermentation: Penicillin Production.” Computers and Chemical Engineering 26 (11): 1553–1565.
  • He, K., X. Zhang, S. Ren, and J. Sun. 2015. “Deep Residual Learning for Image Recognition.” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 770–778.
  • Hershey, S., S. Chaudhuri, D. P. W. Ellis, J. F. Gemmeke, A. Jansen, R. C. Moore, M. Plakal, et al. 2017. “CNN Architectures for Large-Scale Audio Classification.” 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), New Orleans, USA, 131–135.
  • Hou, T. H., W. L. Liu, and L. Lin. 2003. “Intelligent Remote Monitoring and Diagnosis of Manufacturing Processes Using an Integrated Approach of Neural Networks and Rough Sets.” Journal of Intelligent Manufacturing 14 (2): 239–253.
  • Hu, G., H. Li, Y. Xia, and L. Luo. 2018. “A Deep Boltzmann Machine and Multi-grained Scanning Forest Ensemble Collaborative Method and Its Application to Industrial Fault Diagnosis.” Computers in Industry 100: 287–296.
  • Jiang, Q., X. Yan, and Z. Lv. 2014. “Independent Component Analysis-based Non-Gaussian Process Monitoring with Preselecting Optimal Components and Support Vector Data Description.” International Journal of Production Research 52 (11-12): 3273–3286.
  • Jing, L., T. Wang, M. Zhao, and P. Wang. 2017. “An Adaptive Multi-sensor Data Fusion Method Based on Deep Convolutional Neural Networks for Fault Diagnosis of Planetary Gearbox.” Sensors 17 (2): 414.
  • Kontar, R., S. Zhou, and J. Horst. 2017. “Estimation and Monitoring of Key Performance Indicators of Manufacturing Systems Using the Multi-output Gaussian Process.” International Journal of Production Research 55 (7-8): 1–16.
  • LeCun, Y., and Y. Bengio. 1995. “Convolutional Networks for Images, Speech, and Time Series.” The Handbook of Brain Theory and Neural Networks 3361 (10).
  • Lee, J. M., C. K. Yoo, S. W. Choi, Peter A. Vanrolleghem, In-Beum Lee,  2004. “Nonlinear Process Monitoring Using Kernel Principal Component Analysis.” Chemical Engineering Science 59 (1): 223–234.
  • Li, X., X. Li, and H. Ma. 2020. “Deep Representation Clustering-based Fault Diagnosis Method with Unsupervised Data Applied to Rotating Machinery.” Mechanical Systems and Signal Processing 143: 106825.
  • Li, W., W. Sun, and Y. Zhao. 2020. “Deep Image Compression with Residual Learning.” Applied Sciences 10 (11): 4023.
  • Li, N., W. Yan, and Y. Yang. 2015. “Spatial-statistical Local Approach for Improved Manifold-based Process Monitoring.” Industrial & Engineering Chemistry Research 54 (34): 8509–8519.
  • Liu, R., F. Wang, and B. Yang. 2020. “Multiscale Kernel Based Residual Convolutional Neural Network for Motor Fault Diagnosis Under Nonstationary Conditions.” IEEE Transactions on Industrial Informatics 16 (6): 3797–3806.
  • Lv, Fy., C. L. Wen, M. Q. Liu, and Z. J. Bao. 2017. “Weighted Time Series Fault Diagnosis Based on a Stacked Sparse Autoencoder.” Journal of Chemometrics 31: 2912–2927.
  • Ma, S., F. Chu, and Q. Han. 2019. “Deep Residual Learning with Demodulated Time-frequency Features for Fault Diagnosis of Planetary Gearbox Under Nonstationary Running Conditions.” Mechanical Systems and Signal Processing 127: 190–201.
  • Mao, X., C. Shen, and Y. Yang. 2016. “Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections.” arXiv preprint arXiv:1606.08921.
  • Masci, J., U. Meier, D. Cireşan, and Jürgen Schmidhuber. 2011. “Stacked Convolutional Auto-encoders for Hierarchical Feature extraction.” International Conference on Artificial Neural Networks. Springer, Berlin, Heidelberg, 52–59.
  • Mcavoy, T. J., and N. Ye. 1994. “Base Control for the Tennessee Eastman Problem.” Computers and Chemical Engineering 18 (5): 383–413.
  • Peng, X., Y. Tang, W. Du, and F. Qian. 2017. “Multimode Process Monitoring and Fault Detection: A Sparse Modeling and Dictionary Learning Method.” IEEE Transactions on Industrial Electronics 64 (6): 4866–4875.
  • Peng, J., H. Zhixin, and L. Jun. 2016. “Fault Diagnosis Based on Chemical Sensor Data with an Active Deep Neural Network.” Sensors 16 (10): 1695.
  • Plakias, S., and Y. S. Boutalis. 2019. “Exploiting the Generative Adversarial Framework for One-class Multi-dimensional Fault Detection.” Neurocomputing 332 (7): 396–405.
  • Shen, X., and Agrawal, S.. 2006. “Kernel Density Estimation for an Anomaly Based Intrusion Detection System.” International Conference on Machine Learning Models. DBLP.
  • Skordilis, E., and R. Moghaddass. 2017. “A Condition Monitoring Approach for Real-time Monitoring of Degrading Systems Using Kalman Filter and Logistic Regression.” International Journal of Production Research 55 (19-20): 5579–5596.
  • Tang, P., K. X. Peng, K. Zhang, Z. W. Chen, X. Yang, and L. L. Li. 2018. “A Deep Belief Network-based Fault Detection Method for Nonlinear Process.” IFAC Papers OnLine 51 (24): 9–14.
  • Tong, C., H. Nael, El-Farra, and A. Palazoglu. 2014. “Fault Detection and Isolation in Hybrid Process Systems Using a Combined Data-driven and Observer-design Methodology.” AIChE Journal 60 (8): 2805–2814.
  • Wang, Z., Q. Liu, and H. Chen. 2020. “A Deformable CNN-DLSTM Based Transfer Learning Method for Fault Diagnosis of Rolling Bearing Under Multiple Working Conditions.” International Journal of Production Research 59 (16): 4811–4825.
  • Wang, J., Y. Ma, L. Zhang, R. X. Gao, and D. Wu. 2018. “Deep Learning for Smart Manufacturing: Methods and Applications.” Journal of Manufacturing Systems 48: 144–156.
  • Wang, Y., Z. Pan, X. Yuan, C. Yang, and W. Gui, 2019. “A Novel Deep Learning Based Fault Diagnosis Approach for Chemical Process with Extended Deep Belief Network.” ISA Transactions. doi:10.1016/j.isatra.2019.07.001.
  • Wang, J., L. Ye, and R. X. Gao. 2019. “Digital Twin for Rotating Machinery Fault Diagnosis in Smart Manufacturing.” International Journal of Production Research 57 (12): 3920–3934.
  • Wise, B. M., and N. B. Gallagher. 1996. “The Process Chemometrics Approach to Process Monitoring and Fault Detection.” Journal of Process Control 6 (6): 329–348.
  • Wu, H., and J. Zhao. 2018. “Deep Convolutional Neural Network Model Based Chemical Process Fault Diagnosis.” Computers & Chemical Engineering 115: 185–197.
  • Yan, S., and X. Yan. 2019. “Design Teacher and Supervised Dual Stacked Autoencoders for Quality-relevant Fault Detection in Industrial Process.” Applied Soft Computing 81: 105526.
  • Yang, W. A. 2015. “Monitoring and Diagnosing of Mean Shifts in Multivariate Manufacturing Processes Using Two-level Selective Ensemble of Learning Vector Quantization Neural Networks.” Journal of Intelligent Manufacturing 26 (4): 769–783.
  • Yang, C., and J. Hou. 2019. “Fed-batch Fermentation Penicillin Process Fault Diagnosis and Detection Based on Support Vector Machine.” Neurocomputing 190 (19): 117–123.
  • Yin, Z., and J. Hou. 2016. “Recent Advances on SVM Based Fault Diagnosis and Process Monitoring in Complicated Industrial Processes.” Neurocomputing 174: 643–650.
  • Yu, J. 2010. “Hidden Markov Models Combining Local and Global Information for Nonlinear and Multimodal Process Monitoring.” Journal of Process Control 20 (3): 344–359.
  • Yu, J. 2012. “Local and Global Principal Component Analysis for Process Monitoring.” Journal of Process Control 22 (7): 1358–1373.
  • Yu, J. 2013. “A Support Vector Clustering-based Probabilistic Method for Unsupervised Fault Detection and Classification of Complex Chemical Processes Using Unlabeled Data.” AIChE Journal 59: 407–419.
  • Yu, J., X. Liu, and L. Ye. 2020. “Convolutional Long Short-term Memory Autoencoder-based Feature Learning for Fault Detection in Industrial Processes.” IEEE Transactions on Instrumentation and Measurement 70: 1–15.
  • Yu, J., and S. J. Qin. 2009. “Multiway Gaussian Mixture Model Based Multiphase Batch Process Monitoring.” Industrial & Engineering Chemistry Research 48 (18): 8585–8594.
  • Yu, J. B., and X. Yan. 2018. “Layer-by-layer Enhancement Strategy of Favorable Features of the Deep Belief Network for Industrial Process Monitoring.” Industrial & Engineering Chemistry Research 57 (45): 15479–15490.
  • Yu, J., and X. Zhou. 2020. “One-Dimensional Residual Convolutional Autoencoder Based Feature Learning for Gearbox Fault Diagnosis.” IEEE Transactions on Industrial Informatics 16 (10): 6347–6358.
  • Zhang, Z., T. Jiang, S. Li, and Y. Yang. 2018. “Automated Feature Learning for Nonlinear Process Monitoring – An Approach Using Stacked Denoising Autoencoder and k-nearest Neighbor Rule.” Journal of Process Control 64: 49–61.
  • Zhang, W., X. Li, and Q. Ding. 2019. “Deep Residual Learning-based Fault Diagnosis Method for Rotating Machinery.” ISA Transactions 95: 295–305.
  • Zhang, C., J. Yu, and S. Wang. 2020. “Fault Detection and Recognition of Multivariate Process Based on Feature Learning of One-Dimensional Convolutional Neural Network and Stacked Denoised Autoencoder.” International Journal of Production Research 59 (8): 2426–2449.
  • Zhao, M., M. Kang, and B. Tang B. 2018. “Deep Residual Networks with Dynamically Weighted Wavelet Coefficients for Fault Diagnosis of Planetary Gearboxes.” IEEE Transactions on Industrial Electronics 65 (5): 4290–4300.
  • Zhong, S., Q. Wen, and Z. Ge. 2014. “Semi-supervised Fisher Discriminant Analysis Model for Fault Classification in Industrial Processes.” Chemometrics and Intelligent Laboratory Systems 138: 203–211.

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