Abstract
In multivariate process control (MPC), the conventional multivariate quality control charts (e.g., T2) have been shown to be efficient for out-of-control signals detection based upon an overall statistic, whereas do not relieve the need for multivariate process pattern recognition (MPPR). MPPR is very beneficial to locate the assignable causes that lead to the out-of-control situation in multivariate manufacturing process. Both Support Vector Machines (SVM) and Convolutional Neural Networks (CNN) techniques have been widely applied and obtained many successes in image and visual analysis, but both methods have some weakness. Therefore, we propose a hybrid system that composes the mentioned techniques. Firstly, two different structure of CNNs were pre-trained as feature extractor due to the capability of unsupervised feature learning. The feature extracted by two CNNs were combined to train a SVM classifier. Experimental analysis show that the proposed hybrid system presents better performance than the isolated stand-alone systems.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
H. Hotelling, Multivariate quality control—illustrated by the air testing of sample bombsights, in Techniques of Statistical Analysis, ed. by C. Eisenhart, M.W. Hastay, W.A. Wallis (McGraw-Hill, NY, 1947), pp. 11–184
R.B. Crosier, Multivariate generalizations of cumulative sum quality control schemes. Technometrics 30(3), 291–303 (1988)
C.A. Lowry, W.H. Woodall, C.W. Champ, S.E. Rigdon, A multivariate exponentially weighted moving average control chart. Technometrics 34, 46–53 (1992)
R.S. Guh, Y.R. Shiue, An effective application of decision tree learning for on-line detection of mean shifts in multivariate control charts. Comput. Ind. Eng. 55(2), 475–493 (2008)
R.S. Guh, On-line identification and quantification of mean shifts in bivariate processes using a neural network-based approach. Qual. Reliab. Eng. Int. 23, 367–385 (2007)
S.T.A. Niaki, B. Abbasi, Fault diagnosis in multivariate control charts using artificial neural networks. Int. Qual. Reliab. Eng. 21, 825–840 (2005)
C.S. Cheng, H.P. Cheng, Identifying the source of variance shifts in the multivariate process using neural networks and support vector machines. Expert Syst. Appl. 35, 198–206 (2008)
Y. LeCun, Y. Bengio, G. Hinton, Deep learning. Nat. 521(7553), 436–444 (2015)
A. Krizhevsky, I. Sutskever, G.E. Hinton, ImageNet classification with deep convolutional neural networks, in Advances in Neural Information Processing Systems (2012)
X. Han, K. Rasul, R. Vollgraf, Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms, arXiv:1708.07747 (2017)
A.F. Agarap, A neural network architecture combining gated recurrent unit (GRU) and support vector machine (SVM) for intrusion detection in network traffic data, arXiv:1709.03082 (2017)
M. Abadi et al., Tensorflow: large-scale machine learning on heterogeneous distributed systems, arXiv:1603.04467 (2016)
F. Pedregosa et al., Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
L. Maaten, G. Hinton, Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008)
J.J. Down, E.F. Vogel, A plant-wide industrial process control problem. Comput. Chem. Eng. 17(3), 245–255 (1993)
L.H. Chiang, E.L. Russell, R.D. Braatz, Fault Detection and Diagnosis in Industrial Systems, Advanced Textbooks in Control and Signal Processing (Springer, London, Great Britain, 2001)
Acknowledgements
This research was supported by the National Natural Science Foundation of China (No. 51375290, 71777173).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Zheng, X., Yu, J. (2019). Multivariate Process Monitoring and Fault Identification Using Convolutional Neural Networks. In: Huang, G., Chien, CF., Dou, R. (eds) Proceeding of the 24th International Conference on Industrial Engineering and Engineering Management 2018. Springer, Singapore. https://doi.org/10.1007/978-981-13-3402-3_22
Download citation
DOI: https://doi.org/10.1007/978-981-13-3402-3_22
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-3401-6
Online ISBN: 978-981-13-3402-3
eBook Packages: Business and ManagementBusiness and Management (R0)